I need to understand the harness setup in the /root/projects/meeting project. Please do a thorough exploration:

1. Read /root/projects/meeting/CLAUDE.md in full
2. Check if there's an AGENTS.md file in the project
3. Look for any .claude/ directory and its contents (settings.json, commands/, etc.)
4. Look for any other harness-related files (feature_list.json, init.sh, etc.)
5. Check the project's package.json or any config files to understand the tech stack
6. Look at the overall directory structure

I want to understand:
- What does the current harness look like in this real project?
- What's included and what's missing compared to best practices?
- What tech stack and patterns does this project use?

Report back with detailed findings including full file contents where relevant.


I'll do a thorough exploration of the harness setup in the /root/projects/meeting project. Let me start by reading the key files and examining the directory structure.


Bash

find /root/projects/meeting -maxdepth 3 -type f -name "*.md" -o -name "*.json" | head -30


ls -la /root/projects/meeting/.claude/ 2>/dev/null || echo "No .claude directory found"


/root/projects/meeting/.claude/settings.local.json
/root/projects/meeting/.pytest_cache/README.md
/root/projects/meeting/agents/architect.json
/root/projects/meeting/agents/business_analyst.json
/root/projects/meeting/agents/devops.json
/root/projects/meeting/agents/ecc_architect.json
/root/projects/meeting/agents/ecc_database_reviewer.json
/root/projects/meeting/agents/ecc_go_reviewer.json
/root/projects/meeting/agents/ecc_performance_optimizer.json
/root/projects/meeting/agents/ecc_python_reviewer.json
/root/projects/meeting/agents/ecc_rust_reviewer.json
/root/projects/meeting/agents/ecc_security_reviewer.json
/root/projects/meeting/agents/ecc_tdd_guide.json
/root/projects/meeting/agents/example_architect.json
/root/projects/meeting/agents/gstack_altman.json
/root/projects/meeting/agents/gstack_beck.json
/root/projects/meeting/agents/gstack_bezos.json
/root/projects/meeting/agents/gstack_brooks.json
/root/projects/meeting/agents/gstack_commercial.json
/root/projects/meeting/agents/gstack_grove.json
/root/projects/meeting/agents/gstack_horowitz.json
/root/projects/meeting/agents/gstack_ive.json
/root/projects/meeting/agents/gstack_munger.json
/root/projects/meeting/agents/gstack_norman.json
/root/projects/meeting/agents/gstack_pg.json
/root/projects/meeting/agents/gstack_rams.json
/root/projects/meeting/agents/gstack_technical.json
/root/projects/meeting/agents/gstack_ux.json
/root/projects/meeting/agents/real_powell.json
/root/projects/meeting/agents/steve_jobs.json


total 8
drwxr-xr-x  3 501 games   47 Mar 31 14:36 .
drwxr-xr-x 11 501 games 4096 Mar 31 14:47 ..
-rw-r--r--  1 501 games 3272 Mar 31 14:36 settings.local.json
drwxr-xr-x  3 501 games   21 Mar 31 12:29 skills


1	# Claude Code Guidelines & Rules
2
3	## 1. Core Workflow
4	- **TDD Strictly:** ALWAYS write or ask for a failing test case before implementing logic.
5	- **Spec-First:** Follow \`PLAN.md\` or \`SPEC.md\`. Do not guess requirements.
6	- **Plain English:** Explain logic in simple Subject-Verb-Object sentences.
7
8	## 2. Python Standards
9	- **Type Hints:** MANDATORY for all function arguments and returns.
10	- **Data Validation:** Use \`Pydantic\` models, not raw dictionaries.
11	- **Docstrings:** Google-style, concise.
12	- **No 'os.path':** Use \`pathlib\` for file handling.
13
14	## 3. Interaction Rules
15	- **No Hallucinations:** If unsure about a library, ask to check docs.
16	- **Context:** Do NOT read \`venv/\`, \`node_modules/\`, or large data files.
17	- **Security:** Never output API keys or secrets in the terminal.
18
19	## 4. Human-in-the-Loop Protocol (MANDATORY)
20
21	After generating any significant code (more than 20 lines or modifying core logic), you MUST automatically perform the following steps BEFORE asking me to proceed:
22
23	1.  **Self-Explanation:** Briefly explain *what* you changed and *why*, in Plain English.
24	2.  **Structural Check:** Did you introduce any new dependencies? Did you change any function signatures?
25	3.  **Risk Assessment:** What is the most likely thing to break with this change?
26	4.  **Verification Plan:** Tell me exactly which test command to run to verify this specific change.
27
28	## 5. 功能重叠领域只用Superpowers
29	- 设计/头脑风暴 → superpowers:brainstorming
30	- 编写/执行计划 → superpowers:writing-plans/executing-plans
31	- TDD → superpowers:test-driven-development
32	- 代码审查 → superpowers:requesting-code-review
33	- 调试 → superpowers:systematic-debugging
34	- 验证 → superpowers:verification-before-completion
35
36	## 6. 特定技术审查用ECC
37	- Go代码审查 → everything-claude-code:go-review
38	- Python审查 → everything-claude-code:python-review
39	- 安全审计 → everything-claude-code:security-review
40	- 数据库审查 → everything-claude-code:database-review
41


Great! There's a .claude directory. Let me continue exploring and read the settings file and other key files.


1	{
2	  "permissions": {
3	    "allow": [
4	      "Read(//Users/lili/.claude/skills/**)",
5	      "Read(//Users/lili/.claude-internal/skills/**)",
6	      "Read(//Users/lili/.claude/commands/**)",
7	      "Read(//Users/lili/.claude-internal/commands/**)",
8	      "Bash(find . -maxdepth 3 -type d ! -path */venv* ! -path */.git* ! -path */node_modules*)",
9	      "Bash(git init:*)",
10	      "Bash(git add:*)",
11	      "Bash(git commit:*)",
12	      "Bash(python:*)",
13	      "Bash(python3:*)",
14	      "Bash(pip3 install:*)",
15	      "Bash(xargs ls:*)",
16	      "Bash(xargs basename:*)",
17	      "Bash(grep -h \"^name:\\\\|^description:\" /Users/lili/.claude-internal/plugins/cache/everything-claude-code/everything-claude-code/1.9.0/agents/*.md)",
18	      "Bash(find /Users/lili -type f -name *role-commercial* -o -name *role-technical* -o -name *role-ux*)",
19	      "Bash(find /Users/lili -type f -path *gstack* -name *.md)",
20	      "Bash(find /Users/lili -type f -name *.md -path *gstack*)",
21	      "Bash(find /Users/lili/.claude-internal/plugins/marketplaces/everything-claude-code/.agents/skills -type f -name *.md)",
22	      "Bash(find /Users/lili/.claude-internal/plugins/marketplaces/everything-claude-code/.claude/skills -type f -name *.md)",
23	      "Bash(find /Users/lili/.claude-internal/plugins/marketplaces/everything-claude-code/.opencode -type f -name *.md)",
24	      "Bash(find /Users/lili/.claude-internal -type d -name *gstack*)",
25	      "Bash(find /Users/lili/.claude-internal -type d -name *plugin*)",
26	      "Bash(find /Users/lili/workspace/gstack -type f \\\\\\(-name *.md -o -name *.yaml -o -name *.yml -o -name *.json \\\\\\))",
27	      "Bash(find /Users/lili/workspace/gstack -type f -name *.md)",
28	      "Bash(find:*)",
29	      "Bash(find /Users/lili -path */node_modules -prune -o -path */.git -prune -o -type f \\\\\\(-name *.md -o -name *.yaml -o -name *.json \\\\\\) -print)",
30	      "Bash(grep -r \"office.hours\\\\|office-hours\" /Users/lili/workspace/gstack --include=*.md)",
31	      "Bash(find /Users/lili/workspace/gstack -type f \\\\\\(-name *.md -o -name *.yaml -o -name *.yml -o -name *.json \\\\\\) -print)",
32	      "Bash(find /Users/lili/.claude-internal/plugins -type f \\\\\\(-name *.json -o -name *.yaml -o -name *.yml \\\\\\) -path *agents*)",
33	      "Bash(grep -E \"\\\\.md$\")",
34	      "Bash(grep -r \"🔩\\\\|💰\\\\|🎨\\\\|persona\\\\|celebrity\\\\|Garry\\\\|Jobs\\\\|Musk\\\\|Bezos\" /Users/lili/workspace/gstack --include=*.md)",
35	      "Bash(ls -1 /Users/lili/workspace/multi-agents-meeting/agents/*.json)",
36	      "Bash(xargs -I {} basename {} .json)",
37	      "Bash(tee /tmp/meeting_output.txt)",
38	      "Bash(plan)",
39	      "Bash(wc -l src/*.py)",
40	      "Bash(grep -E \"\\\\.\\(md|yaml|yml|json|txt|toml\\)$\")",
41	      "Bash(wc -l /Users/lili/workspace/multi-agents-meeting/src/*.py /Users/lili/workspace/multi-agents-meeting/*.py /Users/lili/workspace/multi-agents-meeting/tests/*.py)",
42	      "Bash(mkdir -p /Users/lili/.claude/commands/meeting)",
43	      "Bash(cp /Users/lili/.claude/skills/meeting/SKILL.md /Users/lili/.claude/commands/meeting/SKILL.md)",
44	      "Bash(ls /Users/lili/workspace/multi-agents-meeting/*.py)",
45	      "Bash(ls -la /Users/lili/workspace/multi-agents-meeting/agents/*.json)",
46	      "Bash(ssh:*)",
47	      "Bash(rsync:*)",
48	      "Bash(scp:*)"
49	    ]
50	  }
51	}
52


Error: File does not exist. Note: your current working directory is /root/projects/learn-harness-engineering.


ls -la /root/projects/meeting/ | head -40


find /root/projects/meeting -maxdepth 2 -type f \( -name "AGENTS.md" -o -name "feature_list.json" -o -name "init.sh" -o -name "PLAN.md" -o -name "SPEC.md" \)


total 84
drwxr-xr-x 11  501 games  4096 Mar 31 14:47 .
drwxr-xr-x 11  501 games   186 Apr  1 11:58 ..
-rw-r--r--  1  501 games  4093 Mar 30 14:49 agent_builder.py
drwxr-xr-x  2  501 games  4096 Mar 31 13:54 agents
drwxr-xr-x  3  501 games    47 Mar 31 14:36 .claude
-rw-r--r--  1  501 games  2020 Mar 30 13:02 CLAUDE.md
drwxr-xr-x  2  501 games    35 Mar 30 19:55 context
drwxr-xr-x  3  501 games    41 Mar 30 19:48 docs
-rw-r--r--  1 root root    410 Mar 31 14:47 .env
-rw-r--r--  1  501 games   367 Mar 30 14:59 .env.example
drwxr-xr-x  7  501 games   150 Mar 31 14:50 .git
-rw-r--r--  1  501 games   233 Mar 30 15:06 .gitignore
-rw-r--r--  1  501 games  4659 Mar 30 13:01 prompts.md
drwxr-xr-x  3  501 games    70 Mar 30 14:30 .pytest_cache
-rw-r--r--  1  501 games  3890 Mar 30 19:49 README.md
drwxr-xr-x  2  501 games  4096 Mar 31 12:20 reports
-rw-r--r--  1  501 games    87 Mar 30 14:59 requirements.txt
-rw-r--r--  1  501 games  6952 Mar 30 20:40 run_meeting.py
drwxr-xr-x  2  501 games  4096 Mar 30 20:43 src
drwxr-xr-x  2  501 games  4096 Mar 30 20:45 tests
-rw-r--r--  1  501 games 19006 Mar 31 13:47 WHO-IS-POWELL.md


(Bash completed with no output)


Let me check for more configuration files and explore the directory structure further.


total 0
drwxr-xr-x 3 501 games 21 Mar 31 12:29 .
drwxr-xr-x 3 501 games 47 Mar 31 14:36 ..
drwxr-xr-x 2 501 games 22 Mar 31 14:20 meeting


find /root/projects/meeting -maxdepth 1 -type f | grep -E "\.(json|md|txt|yaml|yml)$"


ls -la /root/projects/meeting/agents/ | head -20


/root/projects/meeting/CLAUDE.md
/root/projects/meeting/README.md
/root/projects/meeting/WHO-IS-POWELL.md
/root/projects/meeting/prompts.md
/root/projects/meeting/requirements.txt


1	# Multi-Agent Meeting Framework
2
3	A lightweight, Unix-style multi-agent collaboration system in pure Python. No heavy frameworks — just `pydantic`, an LLM SDK, and clean code.
4
5	## Quick Start
6
7	### 1. Install dependencies
8
9	```bash
10	pip install -r requirements.txt
11	```
12
13	### 2. Set your API key
14
15	```bash
16	# For Anthropic (default)
17	export ANTHROPIC_API_KEY="your-key-here"
18
19	# Or for OpenAI
20	export LLM_PROVIDER=openai
21	export OPENAI_API_KEY="your-key-here"
22	```
23
24	### 3. Create agents
25
26	```bash
27	python agent_builder.py --description "资深软件架构师，关注系统可扩展性和技术债务" --name architect
28	python agent_builder.py --description "商业分析师，关注 ROI 和市场可行性" --name business_analyst
29	python agent_builder.py --description "DevOps 工程师，关注部署复杂度和运维成本" --name devops
30	```
31
32	### 4. Run a meeting
33
34	```bash
35	# Manual agent selection
36	python run_meeting.py --topic "评估从 PostgreSQL 迁移到 MongoDB 的方案" --agents architect,business_analyst,devops
37
38	# Or use a preset scenario
39	python run_meeting.py --topic "评估从 PostgreSQL 迁移到 MongoDB 的方案" --scenario tech_design
40
41	# Or let PM auto-select agents
42	python run_meeting.py --topic "评估从 PostgreSQL 迁移到 MongoDB 的方案" --auto-select
43
44	# With background context for deeper discussion
45	python run_meeting.py --topic "评估从 PostgreSQL 迁移到 MongoDB 的方案" --scenario tech_design --context-file docs/migration-brief.md
46
47	# Interactive mode: provide feedback mid-discussion
48	python run_meeting.py --topic "评估从 PostgreSQL 迁移到 MongoDB 的方案" --agents architect,devops --interactive
49	```
50
51	## CLI Reference
52
53	### agent_builder.py
54
55	Generate an Agent config from a natural language description.
56
57	```
58	python agent_builder.py --description "..." --name NAME [--output-dir agents/] [--format json|yaml]
59	```
60
61	### run_meeting.py
62
63	Run a multi-agent meeting.
64
65	```
66	python run_meeting.py --topic "..." --agents name1,name2 [options]
67	python run_meeting.py --topic "..." --scenario SCENARIO [options]
68	python run_meeting.py --topic "..." --auto-select [options]
69	python run_meeting.py --list-scenarios
70	```
71
72	| Option | Description | Default |
73	|--------|-------------|---------|
74	| `--topic` | Discussion topic (required) | — |
75	| `--agents` | Comma-separated agent names | — |
76	| `--scenario` | Use a preset scenario | — |
77	| `--auto-select` | PM auto-selects agents | — |
78	| `--list-scenarios` | List preset scenarios and exit | — |
79	| `--context` | Inline background context | `""` |
80	| `--context-file` | Read context from file | — |
81	| `--max-rounds` | Maximum discussion rounds | 10 |
82	| `--interactive` | Enable human-in-the-loop mode | `false` |
83	| `--agents-dir` | Agent config directory | `agents/` |
84	| `--output-dir` | Report output directory | `reports/` |
85
86	## Environment Variables
87
88	| Variable | Default | Description |
89	|----------|---------|-------------|
90	| `LLM_PROVIDER` | `anthropic` | LLM backend: `anthropic` or `openai` |
91	| `LLM_MODEL` | auto | Model name (defaults: `claude-sonnet-4-20250514` / `gpt-4o`) |
92	| `ANTHROPIC_API_KEY` | — | Anthropic API key |
93	| `OPENAI_API_KEY` | — | OpenAI API key |
94
95	## Architecture
96
97	- **Stateless Agents**: Each agent receives only the PM's question and a whiteboard summary
98	- **PM Router**: A state-machine PM agent outputs structured JSON decisions
99	- **Shared Whiteboard**: All context lives in a `MeetingState` Pydantic model
100	- **Hard Loop Limit**: `max_rounds` prevents infinite discussions (default: 10)
101	- **Full Transcript**: Every discussion is saved as `.transcript.md` alongside the report
102	- **Preset Scenarios**: 6 built-in scenarios for common meeting types
103	- **Auto Agent Selection**: PM can automatically choose the best agents for a topic
104	- **Human-in-the-Loop**: `--interactive` mode lets you inject feedback mid-discussion
105
106	## Running Tests
107
108	```bash
109	python -m pytest tests/ -v
110	```
111


total 124
drwxr-xr-x  2 501 games 4096 Mar 31 13:54 .
drwxr-xr-x 11 501 games 4096 Mar 31 14:47 ..
-rw-r--r--  1 501 games  426 Mar 30 15:06 architect.json
-rw-r--r--  1 501 games  449 Mar 30 15:07 business_analyst.json
-rw-r--r--  1 501 games  461 Mar 30 15:07 devops.json
-rw-r--r--  1 501 games 1499 Mar 30 16:59 ecc_architect.json
-rw-r--r--  1 501 games 1391 Mar 30 16:58 ecc_database_reviewer.json
-rw-r--r--  1 501 games 1356 Mar 30 16:59 ecc_go_reviewer.json
-rw-r--r--  1 501 games 1315 Mar 30 16:58 ecc_performance_optimizer.json
-rw-r--r--  1 501 games 1389 Mar 30 16:58 ecc_python_reviewer.json
-rw-r--r--  1 501 games 1534 Mar 30 16:59 ecc_rust_reviewer.json
-rw-r--r--  1 501 games 1339 Mar 30 16:58 ecc_security_reviewer.json
-rw-r--r--  1 501 games 1589 Mar 30 16:59 ecc_tdd_guide.json
-rw-r--r--  1 501 games  496 Mar 30 14:53 example_architect.json
-rw-r--r--  1 501 games    0 Mar 30 14:27 .gitkeep
-rw-r--r--  1 501 games 1264 Mar 30 16:56 gstack_altman.json
-rw-r--r--  1 501 games 1424 Mar 30 16:56 gstack_beck.json
-rw-r--r--  1 501 games 1298 Mar 30 16:56 gstack_bezos.json
-rw-r--r--  1 501 games 1481 Mar 30 16:56 gstack_brooks.json


Now let me examine some of the agent configs and the project structure more thoroughly.


1	{
2	  "name": "architect",
3	  "role": "Senior Software Architect",
4	  "system_prompt": "你是一位拥有15年经验的资深软件架构师。你关注系统可扩展性、可维护性和技术债务管理。在会议中，你从架构角度分析提案，识别潜在瓶颈，建议设计模式，并评估不同方案之间的权衡。你用具体的技术推理支撑你的建议，善于画出系统间的依赖关系。"
5	}
6


1	{
2	  "name": "ecc_tdd_guide",
3	  "role": "TDD 专家",
4	  "system_prompt": "你是一位测试驱动开发(TDD)方法论专家，坚信测试不是开发之后的补充，而是设计的核心工具。你的核心能力：\n\n1. **Red-Green-Refactor 循环**：这不是建议，是纪律。红——写一个失败的测试，明确你要什么。绿——用最简单的代码让测试通过。重构——在测试保护下清理代码。每个循环不应超过几分钟。\n2. **测试金字塔**：大量单元测试（快、隔离）→ 适量集成测试（验证组件协作）→ 少量端到端测试（慢但全面）。如果你的测试金字塔倒过来了，你会在CI/CD上付出惨痛代价。\n3. **测试即文档**：好的测试名称就是需求规格说明。test_user_cannot_login_with_expired_token 比任何注释都清晰。测试是唯一保证与代码同步的文档。\n4. **测试的FIRST原则**：Fast（快速）、Independent（独立）、Repeatable（可重复）、Self-validating（自我验证）、Timely（及时写）。违反任何一条都会让测试变成负担。\n5. **何时不TDD**：探索性原型、UI布局、第三方API集成的胶水代码。TDD适合有清晰输入输出的逻辑，不适合所有场景。但「不适合TDD」不是「不需要测试」。\n\n说话风格：像一个严格但有耐心的教练。会追问「你的失败测试呢？」和「这个测试在没有实现的情况下真的会失败吗？」对于「写测试太慢了」这种论点会解释TDD在长期降低的调试时间和重构成本。"
5	}
6


1	# 目标：构建一个轻量级、Unix 风格的多 Agent 协作与生成框架
2
3	你好，请帮我从零开发一个基于纯 Python 的多 Agent 协作系统。
4	我不希望使用任何庞大的现有 Agent 框架（如 LangChain、CrewAI 或 AutoGen），而是希望秉持 Unix 哲学：每个 Agent 是独立的、职责单一的模块；调度系统像 Bash 一样轻量，仅负责状态流转、JSON 路由和强制中断。
5
6	## 核心设计原则
7	1. **极简依赖**：只使用标准库、`pydantic`（用于强制结构化输出）以及 `anthropic` 或 `openai` 的基础 SDK。
8	2. **状态分离**：Agent 必须是无状态的（Stateless），上下文和会议纪要由一个全局的“共享白板（State Dict）”维护。
9	3. **JSON in, JSON out**：内部路由和 Agent 配置严格使用 JSON，最终给人类的报告输出 Markdown。
10
11	## 项目结构规划
12	请将项目分为两个核心命令行工具（CLI）：
13
14	### 模块一：Agent 生成器 (`agent_builder.py`)
15	**功能**：通过简单的自然语言描述（例如名人风格、特定职业规范），快速生成一个结构化的 Agent 配置文件。
16	- **输入**：一段文本描述（例如“模仿 Steve Jobs 的苛刻产品视角”或 Garry Tan gstack 里的风格）。
17	- **处理**：调用 LLM 将其转化为一个标准的系统提示词（System Prompt），并保存为 JSON/YAML 文件。
18	- **输出格式示例** (`agents/steve_jobs.json`)：
19	  ```json
20	  {
21	    "name": "steve_jobs_product_reviewer",
22	    "role": "Chief Product Officer",
23	    "system_prompt": "你是一个极其苛刻的产品经理，注重极简主义和用户体验。你的任务是挑刺...",
24	    "input_schema": "...",
25	    "output_schema": "..."
26	  }
27	模块二：PM Orchestrator / 多 Agent 协作引擎 (run_meeting.py)
28	功能：模拟会议过程，PM Agent 作为路由器和状态维护者，根据当前讨论进度调度其他 Agent，并在适当时机结束会议输出报告。
29
30	输入：一个讨论主题（Topic）和需要参会的 Agent 列表。命令行示例：python run_meeting.py --topic "评估数据库迁移方案" --agents devops,arch,product
31
32	PM Agent 的核心逻辑（状态机）：
33	PM Agent 的系统提示词必须强制它输出 JSON，包含以下字段：
34
35	JSON
36	{
37	  "analysis": "对当前会议白板内容的简短分析",
38	  "next_action": "CALL_AGENT | FINISH",
39	  "target_agent": "要呼叫的agent名称（如果next_action是CALL_AGENT）",
40	  "prompt_for_agent": "给该agent的具体问题或指令",
41	  "final_report": "如果决定FINISH，这里输出完整的Markdown会议报告"
42	}
43	控制流：
44
45	初始化一个“共享白板（Shared Scratchpad）”变量，存入初始 Topic。
46
47	循环开始：将白板内容发给 PM Agent。
48
49	PM Agent 决定下一个发言者。如果检测到死循环（如超过 MAX_LOOPS=5），强制中断。
50
51	目标 Agent 发言（只看 PM 给的具体问题和当前白板摘要，不看所有人原始聊天记录），结果追加到白板。
52
53	循环，直到 PM Agent 输出 next_action: FINISH。
54
55	打印并保存 final_report。
56
57	执行步骤要求
58	请按照以下顺序帮我实现代码：
59
60	首先，定义所有所需的数据结构（使用 Pydantic Models），例如 AgentConfig, MeetingState, PMDecision。
61
62	其次，实现 agent_builder.py 脚本，让我可以低成本创建 Agent。
63
64	接着，实现 run_meeting.py 的核心大循环（The Event Loop）和 PM 的路由控制。
65
66	最后，提供一个 requirements.txt 和一个极简的 README，教我如何用命令行跑通一个“架构师、商业分析师和 PM”评估某个想法的测试 Demo。
67
68	请分步骤输出代码，并保持代码高度整洁、有恰当的注释。
69
70
71	***
72
73	### 为什么这样设计？
74
75	1. **防御性编程（防死循环）**：在 PM 的 JSON Schema 中明确定义了 `CALL_AGENT` 和 `FINISH` 这个枚举值，这就等于把“何时停止”的决定权用强类型约束了起来，配合外部的 `MAX_LOOPS`，彻底解决了你之前担忧的“AI 无限聊下去”的痛点。
76	2. **高度符合直觉的 CLI**：生成的 `agent_builder.py` 和 `run_meeting.py` 就像 Unix 的 `sed` 和 `awk` 一样，你甚至可以在后续用 bash 脚本把它们串起来自动化运行。
77	3. **极简配置**：利用 JSON 来存储 Agent，你可以随时从 Garry Tan 的仓库或者任何地方复制 prompt，直接存成 `.json` 就能在下一次会议中被 `run_meeting.py` 动态加载并调用，完美解耦。
78
79	你可以直接把这段发给 Claude Code。看看它生成的初始代码骨架后，是否需要我帮你一起 review 它的状态机循环逻辑是否足够鲁棒？
80


ls -la /root/projects/meeting/src/ && ls -la /root/projects/meeting/tests/


total 68
drwxr-xr-x  2 501 games 4096 Mar 30 20:43 .
drwxr-xr-x 11 501 games 4096 Mar 31 14:47 ..
-rw-r--r--  1 501 games 1684 Mar 30 14:41 agent.py
-rw-r--r--  1 501 games 3166 Mar 30 19:01 agent_selector.py
-rw-r--r--  1 501 games 2469 Mar 30 14:39 config_loader.py
-rw-r--r--  1 501 games 9050 Mar 30 20:43 context_manager.py
-rw-r--r--  1 501 games   37 Mar 30 14:27 __init__.py
-rw-r--r--  1 501 games 3068 Mar 30 15:00 llm_client.py
-rw-r--r--  1 501 games 9993 Mar 30 20:39 meeting.py
-rw-r--r--  1 501 games 2050 Mar 30 20:32 models.py
-rw-r--r--  1 501 games 6657 Mar 30 20:37 pm.py
-rw-r--r--  1 501 games 2561 Mar 30 18:52 scenarios.py
total 92
drwxr-xr-x  2 501 games  4096 Mar 30 20:45 .
drwxr-xr-x 11 501 games  4096 Mar 31 14:47 ..
-rw-r--r--  1 501 games    47 Mar 30 14:27 __init__.py
-rw-r--r--  1 501 games  2324 Mar 30 14:48 test_agent_builder.py
-rw-r--r--  1 501 games  2942 Mar 30 14:41 test_agent.py
-rw-r--r--  1 501 games  3561 Mar 30 18:51 test_agent_selector.py
-rw-r--r--  1 501 games  2469 Mar 30 14:39 test_config_loader.py
-rw-r--r--  1 501 games 15537 Mar 30 20:45 test_context_manager.py
-rw-r--r--  1 501 games  3332 Mar 30 14:36 test_llm_client.py
-rw-r--r--  1 501 games 14234 Mar 30 20:35 test_meeting.py
-rw-r--r--  1 501 games  7011 Mar 30 18:45 test_models.py
-rw-r--r--  1 501 games  7873 Mar 30 20:35 test_pm.py
-rw-r--r--  1 501 games  4729 Mar 30 20:36 test_run_meeting_cli.py
-rw-r--r--  1 501 games  3278 Mar 30 18:51 test_scenarios.py


Now let me look at key source files to understand the implementation.


1	"""Pydantic data models for the multi-agent meeting framework."""
2
3	from __future__ import annotations
4
5	from datetime import datetime
6	from typing import Literal, Optional
7
8	from pydantic import BaseModel, model_validator
9
10
11	class AgentConfig(BaseModel):
12	    """Configuration for a single Agent."""
13
14	    name: str
15	    role: str
16	    system_prompt: str
17	    input_schema: str = ""
18	    output_schema: str = ""
19
20
21	class ScratchpadEntry(BaseModel):
22	    """A single entry on the shared whiteboard."""
23
24	    agent_name: str
25	    content: str
26	    timestamp: datetime
27
28
29	class MeetingState(BaseModel):
30	    """The shared whiteboard maintaining all meeting context."""
31
32	    topic: str
33	    context: str = ""
34	    context_summary: str = ""
35	    scratchpad: list[ScratchpadEntry] = []
36	    current_round: int = 0
37	    max_rounds: int = 10
38
39
40	class PMDecision(BaseModel):
41	    """Structured output from the PM Agent each round."""
42
43	    analysis: str
44	    next_action: Literal["CALL_AGENT", "FINISH"]
45	    target_agent: Optional[str] = None
46	    prompt_for_agent: Optional[str] = None
47	    final_report: Optional[str] = None
48
49	    @model_validator(mode="after")
50	    def validate_fields_for_action(self) -> PMDecision:
51	        """Ensure required fields are present based on next_action."""
52	        if self.next_action == "CALL_AGENT":
53	            if not self.target_agent:
54	                raise ValueError("target_agent is required when next_action is CALL_AGENT")
55	            if not self.prompt_for_agent:
56	                raise ValueError("prompt_for_agent is required when next_action is CALL_AGENT")
57	        elif self.next_action == "FINISH":
58	            if not self.final_report:
59	                raise ValueError("final_report is required when next_action is FINISH")
60	        return self
61
62
63	class MeetingResult(BaseModel):
64	    """Complete result of a meeting including transcript and metadata."""
65
66	    topic: str
67	    context: str = ""
68	    participants: list[str] = []
69	    transcript: list[ScratchpadEntry] = []
70	    final_report: str
71	    started_at: datetime
72	    finished_at: datetime
73


1	"""Meeting event loop - orchestrates the multi-agent discussion."""
2
3	from __future__ import annotations
4
5	import re
6	from datetime import datetime
7	from pathlib import Path
8	from typing import TYPE_CHECKING, Callable
9
10	from src.agent import format_scratchpad_summary, run_agent
11	from src.models import AgentConfig, MeetingResult, MeetingState, ScratchpadEntry
12	from src.pm import run_pm
13
14	if TYPE_CHECKING:
15	    from src.context_manager import ContextManager
16	    from src.llm_client import LLMClient
17
18	_COLORS = {
19	    "pm": "\033[1;34m",      # Bold blue
20	    "agent": "\033[1;32m",   # Bold green
21	    "system": "\033[1;33m",  # Bold yellow
22	    "human": "\033[1;35m",   # Bold magenta
23	    "reset": "\033[0m",
24	}
25
26
27	def _print_colored(label: str, message: str, color_key: str) -> None:
28	    """Print a colored message to the terminal."""
29	    color = _COLORS.get(color_key, "")
30	    reset = _COLORS["reset"]
31	    print(f"{color}[{label}]{reset} {message}")
32
33
34	def _slugify(text: str) -> str:
35	    """Convert text to a filename-safe slug."""
36	    slug = re.sub(r"[^\w\s-]", "", text.lower())
37	    slug = re.sub(r"[\s_]+", "-", slug)
38	    return slug[:50].strip("-")
39
40
41	def save_report(report: str, topic: str, output_dir: Path = Path("reports")) -> Path:
42	    """Save meeting report as a Markdown file.
43
44	    Args:
45	        report: The Markdown report content to save.
46	        topic: The meeting topic used to name the file.
47	        output_dir: Directory where the report file is written.
48
49	    Returns:
50	        The Path to the saved report file.
51	    """
52	    output_dir.mkdir(parents=True, exist_ok=True)
53	    timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
54	    slug = _slugify(topic)
55	    filename = f"{timestamp}-{slug}.md"
56	    path = output_dir / filename
57	    path.write_text(report, encoding="utf-8")
58	    return path
59
60
61	def save_transcript(result: MeetingResult, output_dir: Path = Path("reports")) -> Path:
62	    """Save meeting transcript as a Markdown file.
63
64	    Args:
65	        result: The complete MeetingResult with transcript data.
66	        output_dir: Directory where the transcript file is written.
67
68	    Returns:
69	        The Path to the saved transcript file.
70	    """
71	    output_dir.mkdir(parents=True, exist_ok=True)
72	    timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
73	    slug = _slugify(result.topic)
74	    filename = f"{timestamp}-{slug}.transcript.md"
75	    path = output_dir / filename
76
77	    lines: list[str] = [f"# 会议讨论记录：{result.topic}\n"]
78	    if result.context:
79	        lines.append(f"## 背景\n\n{result.context}\n")
80
81	    for entry in result.transcript:
82	        lines.append(f"### 💬 {entry.agent_name}")
83	        lines.append(f"{entry.content}\n")
84
85	    path.write_text("\n".join(lines), encoding="utf-8")
86	    return path
87
88
89	def _run_meeting_loop(
90	    state: MeetingState,
91	    agent_configs: dict[str, AgentConfig],
92	    client: LLMClient,
93	    context_manager: ContextManager | None = None,
94	) -> str | None:
95	    """Execute one meeting loop until PM finishes or max_rounds reached.
96
97	    Args:
98	        state: The current MeetingState (modified in place).
99	        agent_configs: Map of agent name to AgentConfig.
100	        client: LLM client for PM and agents.
101	        context_manager: Optional ContextManager for whiteboard compression.
102
103	    Returns:
104	        The final report string if PM finished, or None if max_rounds reached.
105	    """
106	    while state.current_round < state.max_rounds:
107	        state.current_round += 1
108	        _print_colored("SYSTEM", f"--- Round {state.current_round}/{state.max_rounds} ---", "system")
109
110	        if state.current_round == state.max_rounds:
111	            force_entry = ScratchpadEntry(
112	                agent_name="SYSTEM",
113	                content="MAX ROUNDS REACHED. You MUST set next_action to FINISH and provide a final_report.",
114	                timestamp=datetime.now(),
115	            )
116	            state.scratchpad.append(force_entry)
117
118	        decision = run_pm(state, agent_configs, client, context_manager=context_manager)
119	        _print_colored("PM", f"Analysis: {decision.analysis}", "pm")
120
121	        if decision.next_action == "FINISH":
122	            _print_colored("PM", "Meeting concluded.", "pm")
123	            print()
124	            return decision.final_report or "# Meeting Report\n\nNo report generated."
125
126	        target_name = decision.target_agent
127	        target_config = agent_configs[target_name]
128	        _print_colored("PM", f"Calling {target_name}: {decision.prompt_for_agent}", "pm")
129
130	        if context_manager is not None:
131	            scratchpad_summary = context_manager.build_agent_context(
132	                state=state,
133	                target_agent=target_name,
134	                pm_prompt=decision.prompt_for_agent,
135	                client=client,
136	            )
137	        else:
138	            scratchpad_summary = format_scratchpad_summary(state.scratchpad)
139	        agent_response = run_agent(
140	            config=target_config,
141	            prompt=decision.prompt_for_agent,
142	            scratchpad_summary=scratchpad_summary,
143	            client=client,
144	        )
145
146	        _print_colored(target_name.upper(), agent_response, "agent")
147	        print()
148
149	        entry = ScratchpadEntry(
150	            agent_name=target_name,
151	            content=agent_response,
152	            timestamp=datetime.now(),
153	        )
154	        state.scratchpad.append(entry)
155
156	    return None
157
158
159	def run_meeting(
160	    topic: str,
161	    agent_configs: dict[str, AgentConfig],
162	    client: LLMClient,
163	    max_rounds: int = 10,
164	    output_dir: Path = Path("reports"),
165	    context: str = "",
166	    context_manager: ContextManager | None = None,
167	) -> MeetingResult:
168	    """Execute the full meeting loop.
169
170	    Args:
171	        topic: The discussion topic for the meeting.
172	        agent_configs: Map of agent name to AgentConfig for all participants.
173	        client: LLM client used by both PM and agents.
174	        max_rounds: Maximum number of PM decision rounds before forcing a finish.
175	        output_dir: Directory to save the final report (unused here; caller may use).
176	        context: Background context for the discussion.
177	        context_manager: Optional ContextManager for whiteboard compression.
178
179	    Returns:
180	        A MeetingResult containing the final report, transcript, and metadata.
181	    """
182	    started_at = datetime.now()
183	    state = MeetingState(topic=topic, max_rounds=max_rounds, context=context)
184
185	    # Generate context summary if context is long and context_manager is provided
186	    if context_manager is not None and context:
187	        state.context_summary = context_manager.summarize_context(context, client)
188
189	    _print_colored("SYSTEM", f"Meeting started: {topic}", "system")
190	    _print_colored("SYSTEM", f"Participants: {', '.join(agent_configs.keys())}", "system")
191	    print()
192
193	    report = _run_meeting_loop(state, agent_configs, client, context_manager=context_manager)
194
195	    if report is None:
196	        report = "# Meeting Report\n\nMeeting ended after maximum rounds without PM conclusion."
197
198	    return MeetingResult(
199	        topic=topic,
200	        context=context,
201	        participants=list(agent_configs.keys()),
202	        transcript=list(state.scratchpad),
203	        final_report=report,
204	        started_at=started_at,
205	        finished_at=datetime.now(),
206	    )
207
208
209	def run_meeting_interactive(
210	    topic: str,
211	    context: str,
212	    agent_configs: dict[str, AgentConfig],
213	    client: LLMClient,
214	    max_rounds: int = 10,
215	    output_dir: Path = Path("reports"),
216	    input_fn: Callable[[str], str] = input,
217	    context_manager: ContextManager | None = None,
218	) -> MeetingResult:
219	    """Execute an interactive meeting with human-in-the-loop feedback.
220
221	    Runs the meeting loop, then prompts the user for feedback. If feedback
222	    is provided, it is appended to the scratchpad and the loop continues.
223
224	    Args:
225	        topic: The discussion topic for the meeting.
226	        context: Background context for the discussion.
227	        agent_configs: Map of agent name to AgentConfig for all participants.
228	        client: LLM client used by both PM and agents.
229	        max_rounds: Maximum number of PM decision rounds per loop iteration.
230	        output_dir: Directory to save the final report.
231	        input_fn: Callable for getting user input (injected for testing).
232	        context_manager: Optional ContextManager for whiteboard compression.
233
234	    Returns:
235	        A MeetingResult containing the final report, transcript, and metadata.
236	    """
237	    started_at = datetime.now()
238	    state = MeetingState(topic=topic, max_rounds=max_rounds, context=context)
239
240	    # Generate context summary if context is long and context_manager is provided
241	    if context_manager is not None and context:
242	        state.context_summary = context_manager.summarize_context(context, client)
243
244	    _print_colored("SYSTEM", f"Interactive meeting started: {topic}", "system")
245	    _print_colored("SYSTEM", f"Participants: {', '.join(agent_configs.keys())}", "system")
246	    print()
247
248	    report = "# Meeting Report\n\nNo report generated."
249
250	    while True:
251	        loop_report = _run_meeting_loop(state, agent_configs, client, context_manager=context_manager)
252	        if loop_report is not None:
253	            report = loop_report
254
255	        print(f"\n{'=' * 60}")
256	        print(report)
257	        print(f"{'=' * 60}\n")
258
259	        user_input = input_fn("\n💬 有补充内容吗？(直接回车结束): ")
260	        if not user_input.strip():
261	            break
262
263	        # Append human feedback to scratchpad
264	        human_entry = ScratchpadEntry(
265	            agent_name="HUMAN",
266	            content=user_input.strip(),
267	            timestamp=datetime.now(),
268	        )
269	        state.scratchpad.append(human_entry)
270	        _print_colored("HUMAN", user_input.strip(), "human")
271
272	        # Reset round counter for next loop iteration
273	        state.current_round = 0
274
275	    return MeetingResult(
276	        topic=topic,
277	        context=context,
278	        participants=list(agent_configs.keys()),
279	        transcript=list(state.scratchpad),
280	        final_report=report,
281	        started_at=started_at,
282	        finished_at=datetime.now(),
283	    )
284


Error: EISDIR: illegal operation on a directory, read '/root/projects/meeting/.claude/skills/meeting'


Let me check what's in the skills directory.


find /root/projects/meeting/.claude/skills/meeting -type f


1	pydantic>=2.0
2	anthropic>=0.40.0
3	openai>=1.0
4	pyyaml>=6.0
5	python-dotenv>=1.0
6	pytest>=8.0
7


1	#!/usr/bin/env python3
2	"""CLI tool to run a multi-agent meeting."""
3
4	from __future__ import annotations
5
6	import argparse
7	import sys
8	from pathlib import Path
9
10	from src.config_loader import load_agents
11	from src.llm_client import create_client
12	from src.meeting import run_meeting, run_meeting_interactive, save_report, save_transcript
13	from src.scenarios import list_scenarios, load_scenario
14
15
16	def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
17	    """Parse command-line arguments.
18
19	    Args:
20	        argv: Argument list to parse; defaults to sys.argv when None.
21
22	    Returns:
23	        Parsed argparse.Namespace with topic, agents, max_rounds,
24	        agents_dir, output_dir, context, scenario, auto_select,
25	        and list_scenarios attributes.
26	    """
27	    parser = argparse.ArgumentParser(
28	        description="Run a multi-agent meeting on a given topic.",
29	    )
30	    parser.add_argument(
31	        "--topic",
32	        default=None,
33	        help="The discussion topic for the meeting.",
34	    )
35
36	    # Agent selection: --agents, --scenario, --auto-select are mutually exclusive choices
37	    agent_group = parser.add_mutually_exclusive_group()
38	    agent_group.add_argument(
39	        "--agents",
40	        default=None,
41	        help="Comma-separated list of agent names to participate.",
42	    )
43	    agent_group.add_argument(
44	        "--scenario",
45	        default=None,
46	        help="Use a preset scenario (e.g., product_review, startup_pitch).",
47	    )
48	    agent_group.add_argument(
49	        "--auto-select",
50	        action="store_true",
51	        default=False,
52	        help="Let PM automatically select agents based on the topic.",
53	    )
54
55	    parser.add_argument(
56	        "--list-scenarios",
57	        action="store_true",
58	        default=False,
59	        help="List all available scenarios and exit.",
60	    )
61	    parser.add_argument(
62	        "--interactive",
63	        action="store_true",
64	        default=False,
65	        help="Enable human-in-the-loop interactive mode.",
66	    )
67	    parser.add_argument(
68	        "--max-rounds",
69	        type=int,
70	        default=None,
71	        help="Maximum number of discussion rounds (default: 10).",
72	    )
73	    parser.add_argument(
74	        "--context",
75	        default="",
76	        help="Inline background context for the discussion.",
77	    )
78	    parser.add_argument(
79	        "--context-file",
80	        default=None,
81	        help="Path to a file containing background context.",
82	    )
83	    parser.add_argument(
84	        "--agents-dir",
85	        default="agents",
86	        help="Directory containing agent config files (default: agents/).",
87	    )
88	    parser.add_argument(
89	        "--output-dir",
90	        default="reports",
91	        help="Directory to save the meeting report (default: reports/).",
92	    )
93	    parser.add_argument(
94	        "--context-budget",
95	        type=int,
96	        default=6000,
97	        help="Token budget for whiteboard compression (default: 6000).",
98	    )
99	    parser.add_argument(
100	        "--no-compact",


1	#!/usr/bin/env python3
2	"""CLI tool to generate Agent configuration files from natural language descriptions."""
3
4	from __future__ import annotations
5
6	import argparse
7	import json
8	from pathlib import Path
9	from typing import TYPE_CHECKING
10
11	import yaml
12
13	from src.llm_client import create_client
14	from src.models import AgentConfig
15
16	if TYPE_CHECKING:
17	    from src.llm_client import LLMClient
18
19	AGENT_BUILDER_SYSTEM_PROMPT = """You are an expert at creating AI agent personas.
20	Given a natural language description, generate a JSON object with exactly these fields:
21	- "role": A concise job title for the agent (e.g., "Chief Product Officer")
22	- "system_prompt": A detailed system prompt that captures the persona's expertise, communication style, and focus areas. The prompt should instruct the agent on how to behave in a meeting context.
23
24	Output ONLY the JSON object, no other text.
25
26	Example input: "Mimics Steve Jobs' demanding product perspective"
27	Example output:
28	{
29	  "role": "Chief Product Officer",
30	  "system_prompt": "You are an extremely demanding product manager who obsesses over simplicity and user experience. In meetings, you challenge every assumption, push for 10x better solutions, and reject anything that feels mediocre. You focus on: user delight, simplicity, integration of hardware and software thinking, and saying no to 1000 things."
31	}"""
32
33
34	def build_agent_config(
35	    name: str,
36	    description: str,
37	    client: LLMClient,
38	    output_dir: Path = Path("agents"),
39	    fmt: str = "json",
40	) -> AgentConfig:
41	    """Generate an AgentConfig from a natural language description.
42
43	    Args:
44	        name: Unique identifier for the agent (used as filename).
45	        description: Natural language description of the agent persona.
46	        client: LLM client implementing the LLMClient protocol.
47	        output_dir: Directory where the config file will be saved.
48	        fmt: Output format, either "json" or "yaml".
49
50	    Returns:
51	        The populated AgentConfig instance.
52	    """
53	    raw_response = client.chat(
54	        system=AGENT_BUILDER_SYSTEM_PROMPT,
55	        messages=[{"role": "user", "content": description}],
56	    )
57
58	    cleaned = raw_response.strip()
59	    if cleaned.startswith("```"):
60	        cleaned = cleaned.split("\n", 1)[1] if "\n" in cleaned else cleaned
61	        if cleaned.endswith("```"):
62	            cleaned = cleaned[:-3]
63	        cleaned = cleaned.strip()
64
65	    data = json.loads(cleaned)
66
67	    config = AgentConfig(
68	        name=name,
69	        role=data["role"],
70	        system_prompt=data["system_prompt"],
71	    )
72
73	    output_dir.mkdir(parents=True, exist_ok=True)
74	    ext = "yaml" if fmt == "yaml" else "json"
75	    output_path = output_dir / f"{name}.{ext}"
76
77	    config_dict = config.model_dump()
78	    if fmt == "yaml":
79	        output_path.write_text(
80	            yaml.dump(config_dict, allow_unicode=True, default_flow_style=False),
81	            encoding="utf-8",
82	        )
83	    else:
84	        output_path.write_text(
85	            json.dumps(config_dict, ensure_ascii=False, indent=2),
86	            encoding="utf-8",
87	        )
88
89	    return config
90
91
92	def main() -> None:
93	    """CLI entry point for agent_builder."""
94	    parser = argparse.ArgumentParser(
95	        description="Generate an Agent config from a natural language description.",
96	    )
97	    parser.add_argument("--description", required=True, help="Natural language description of the agent persona.")
98	    parser.add_argument("--name", required=True, help="Unique name for the agent.")
99	    parser.add_argument("--output-dir", default="agents", help="Directory to save the config file (default: agents/).")
100	    parser.add_argument("--format", choices=["json", "yaml"], default="json", help="Output format (default: json).")


Let me read the SKILL.md file and check for configuration files and git information.


1	---
2	name: meeting
3	description: |
4	  Multi-agent debate meeting — simulates a boardroom discussion with multiple
5	  expert personas (Munger, Bezos, Grove, Horowitz, etc.) debating a topic from
6	  different angles. Use when the user wants to "hold a meeting", "start a debate",
7	  "discuss a topic with experts", or "run a multi-agent meeting".
8	  Always clarify topic, context, participants, and preferences BEFORE starting.
9	allowed-tools:
10	  - Read
11	  - Glob
12	  - Grep
13	  - Bash
14	  - Write
15	  - AskUserQuestion
16	  - Agent
17	---
18
19	# Multi-Agent Meeting Skill
20
21	Simulate a structured multi-agent debate meeting where Claude plays the PM (facilitator)
22	and all expert personas, conducting a rigorous, multi-round discussion.
23
24	## When to Activate
25
26	- User says "开会", "hold a meeting", "start a debate", "run a meeting"
27	- User wants multiple expert perspectives on a strategic/product/technical topic
28	- User asks to "discuss X with different roles" or "get different angles on X"
29
30	## CRITICAL: Never Start Without Clarification
31
32	**DO NOT start the meeting immediately.** Always use `AskUserQuestion` to clarify
33	the following before running:
34
35	### Step 1: Understand the Topic
36
37	If the user's topic is vague or missing, ask:
38
39	```
40	What specific topic or decision do you want to discuss?
41	(e.g., "Should we pivot from B2C to B2B?", "Evaluate our pricing strategy")
42	```
43
44	### Step 2: Gather Context
45
46	Ask if there are background materials:
47
48	```
49	Do you have background documents or context to share?
50	- File path(s) for meeting materials
51	- Inline context or requirements
52	- Or should we proceed with just the topic?
53	```
54
55	### Step 3: Select Participants
56
57	Present available agent personas and let the user choose. Read agent configs
58	from the `agents/` directory to show options:
59
60	```bash
61	ls agents/gstack_*.json agents/*.json
62	```
63
64	**Available persona categories:**
65
66	| Category | Agents | Best For |
67	|----------|--------|----------|
68	| **Strategic integrator** | **real_powell** | **跨维度整合：技术+战略+经营+组织，站位高于单维度专家，做最终判断** |
69	| Strategic thinkers | gstack_munger, gstack_bezos, gstack_grove, gstack_horowitz, gstack_pg | Product direction, business strategy |
70	| Domain experts | gstack_technical, gstack_commercial, gstack_ux | Technical review, ROI analysis, UX critique |
71	| Industry legends | gstack_altman, gstack_beck, gstack_brooks, gstack_ive, gstack_rams, gstack_norman | Design, engineering culture, product philosophy |
72	| Specialized reviewers | architect, business_analyst, devops | Technical architecture, business analysis |
73
74	Ask the user to pick 3-5 participants, or recommend a set based on the topic:
75
76	- **Product strategy debate**: real_powell + munger + bezos + grove + horowitz + commercial
77	- **Technical architecture review**: real_powell + technical + architect + devops + brooks + beck
78	- **Product design review**: real_powell + ux + ive + norman + rams + pg
79	- **Full business review**: real_powell + munger + bezos + horowitz + commercial + technical
80
81	### Step 4: Confirm Meeting Parameters
82
83	Ask about preferences:
84
85	```
86	Meeting parameters:
87	- Rounds: How many rounds of debate? (recommended: 10-15 for deep discussion)
88	- Language: Chinese / English / Mixed?
89	- Interactive: Do you want to intervene during the meeting? (recommended: yes)
90	- Focus: Any specific questions or angles you want covered?
91	```
92
93	## Running the Meeting
94
95	### Meeting Structure
96
97	1. **PM Opening**: State the topic, summarize background, frame key questions
98	2. **Round-robin first pass**: Each participant gives their initial take
99	3. **Cross-examination**: PM directs participants to respond to each other's points
100	4. **Deep dives**: Follow the most contentious or insightful threads
101	5. **HUMAN interjections**: When the user speaks, treat as highest priority
102	6. **Convergence**: PM summarizes emerging consensus and remaining disagreements
103	7. **Final round**: Each participant gives closing position
104	8. **Meeting report**: Structured summary of decisions, action items, risks
105
106	### Persona Guidelines
107
108	When playing each role, strictly follow their agent config's `system_prompt`:
109
110	- **Powell**: Cross-dimensional integration, challenge premises, force alternatives, cost-aware, structural value assessment — speaks LAST to synthesize
111	- **Munger**: Inversion thinking, multi-mental-model, incentive analysis
112	- **Bezos**: One-way/two-way door classification, customer obsession, Day 1 mindset
113	- **Grove**: 10x force scanning, strategic inflection points, paranoid survival
114	- **Horowitz**: Peacetime/wartime assessment, resource reality, culture-is-action
115	- **Commercial**: ROI cold math, who pays, payback period, pricing power
116	- **Technical**: Edge cases, dependency audit, scale explosion, ops hell
117	- **UX**: Friction scanning, cognitive load, first-use experience, error recovery
118	- **PG**: Do things that don't scale, founder mode, make something people want
119
120	### Formatting
121
122	Each round should be clearly formatted:
123
124	```markdown
125	## Round N — PM
126
127	[PM analysis and direction]
128
129	### [Emoji] [Character Name] ([Role])
130
131	> *"Signature quote"*
132
133	[Character's response in their voice and framework]
134	```
135
136	### When HUMAN Speaks
137
138	When the user provides input during the meeting:
139	1. Acknowledge as `HUMAN` entry
140	2. PM analyzes which participants should respond
141	3. Call 2-3 most relevant participants to address the HUMAN's point
142	4. Always give the HUMAN's concern highest priority
143
144	## Ending the Meeting
145
146	When the meeting concludes (user says to end, or discussion has converged):
147
148	1. Generate a **Meeting Report** (decisions, action items, risks, timeline)
149	2. Generate a **Full Transcript** (complete discussion record)
150	3. Save both to `reports/` directory with timestamp
151	4. Format: `YYYYMMDD-{topic-slug}-会议报告.md` and `YYYYMMDD-{topic-slug}-完整讨论记录.md`
152
153	## Example Invocation Flow
154
155	```
156	User: "开会讨论一下我们的定价策略"
157
158	Claude: [AskUserQuestion]
159	  1. "What specific aspects of pricing do you want to discuss?"
160	  2. "Do you have background materials (documents, data, competitor analysis)?"
161	  3. "Which expert perspectives do you want?"
162	     - Product strategy team (Munger, Bezos, Horowitz, Commercial) [Recommended]
163	     - Full panel (add Technical, UX)
164	     - Custom selection
165	  4. "Any specific questions you want answered?"
166
167	User: [answers]
168
169	Claude: [Starts structured meeting with PM opening]
170	```
171
172	## Key Rules
173
174	1. **NEVER start without clarification** — always ask questions first
175	2. **Stay in character** — each persona must use their specific framework and voice
176	3. **PM drives structure** — facilitate, don't just let personas monologue
177	4. **Cross-pollinate** — share one persona's argument with another for rebuttal
178	5. **Save artifacts** — always save report and transcript to `reports/`
179	6. **Respect HUMAN priority** — user input always gets immediate attention from relevant personas
180


find /root/projects/meeting -maxdepth 2 -name "settings.json" -o -name ".clauderc" -o -name "claude.json"


git -C /root/projects/meeting log --oneline | head -20


(Bash completed with no output)


# Multi-Agent Meeting Framework 环境配置
# 复制此文件为 .env 并填入你的真实值

# LLM 提供商: anthropic 或 openai
LLM_PROVIDER=openai

# OpenAI 代理站地址 (留空则使用官方 API)
OPENAI_BASE_URL=https://your-proxy.example.com/v1

# API Key
OPENAI_API_KEY=sk-xxxxxxxxxxxx

# 模型名称 (可选，留空使用默认值)
LLM_MODEL=gpt-4o


962e42a feat: add Powell (real_powell) as strategic integrator role
d83da2a docs: add comprehensive usage guide for multi-agent meeting system
f973b6c feat: add 22 agent configs from gstack and everything-claude-code
59a77ff feat: add dotenv support, .gitignore, and example agent configs
83a2f80 docs: add README and example agent config
fd95768 feat: add run_meeting CLI for orchestrating meetings
1ed6e3d feat: add agent_builder CLI for generating agent configs
8ee8765 feat: add meeting event loop with terminal output and report saving
58b01df feat: add PM agent router with retry and validation
7e23539 feat: add stateless agent executor
29c79f6 feat: add config loader for JSON/YAML agent files
665cc82 feat: add LLM client abstraction (Protocol + Anthropic/OpenAI)
1dba1e0 feat: add Pydantic data models (AgentConfig, MeetingState, PMDecision)
52ab51c chore: scaffold project structure and dependencies
1f3fb68 docs: add implementation plan for multi-agent meeting framework
774fedc Add multi-agent meeting framework design spec


Let me check a few more key files to understand the full architecture and look at one or two more agent configs.


1	"""PM Agent router - routes discussion between participant Agents."""
2
3	from __future__ import annotations
4
5	import json
6	import re
7	from typing import TYPE_CHECKING
8
9	from pydantic import ValidationError
10
11	from src.agent import format_scratchpad_summary
12	from src.models import AgentConfig, MeetingState, PMDecision
13
14	if TYPE_CHECKING:
15	    from src.context_manager import ContextManager
16	    from src.llm_client import LLMClient
17
18	_MAX_RETRIES = 5
19
20
21	def build_pm_system_prompt(available_agents: dict[str, AgentConfig]) -> str:
22	    """Build the PM Agent's system prompt with available agents listed.
23
24	    Args:
25	        available_agents: Mapping of agent name to AgentConfig.
26
27	    Returns:
28	        A fully-formed system prompt string for the PM LLM call.
29	    """
30	    agent_list = "\n".join(
31	        f"- {name}: {config.role}" for name, config in available_agents.items()
32	    )
33
34	    return f"""You are a meeting facilitator (PM). Your job is to:
35	1. Analyze the current whiteboard state
36	2. Decide which Agent should speak next, or if the meeting should end
37	3. Output ONLY valid JSON matching the schema below
38
39	Available Agents:
40	{agent_list}
41
42	You MUST output ONLY a JSON object with these fields:
43	- "analysis": brief analysis of current whiteboard state
44	- "next_action": either "CALL_AGENT" or "FINISH"
45	- "target_agent": name of the agent to call (required if CALL_AGENT)
46	- "prompt_for_agent": specific question for that agent (required if CALL_AGENT)
47	- "final_report": complete Markdown meeting report (required if FINISH)
48
49	Rules:
50	- DO NOT wrap up prematurely. Explore the topic deeply before concluding.
51	- Ask follow-up questions when an agent gives a surface-level answer.
52	- Call the same agent multiple times if their area needs deeper exploration.
53	- Challenge assumptions: ask agents to justify their reasoning with specifics.
54	- Cross-pollinate: share one agent's perspective with another for counterpoints.
55	- Ensure at least 2-3 rounds of back-and-forth before considering FINISH.
56	- Call each agent at least once before finishing.
57	- When finishing, produce a comprehensive report that reflects the depth of discussion.
58	- The target_agent MUST be one of the available agents listed above.
59	- Output ONLY the JSON object, no other text.
60	- If you see entries from [HUMAN], treat them as high-priority stakeholder feedback.
61	  Address their concerns by calling relevant agents to respond."""
62
63
64	def run_pm(
65	    state: MeetingState,
66	    available_agents: dict[str, AgentConfig],
67	    client: LLMClient,
68	    context_manager: ContextManager | None = None,
69	) -> PMDecision:
70	    """Run PM Agent to get the next routing decision.
71
72	    Calls the LLM with the current whiteboard state. Retries up to
73	    _MAX_RETRIES times when the response is invalid JSON, fails Pydantic
74	    validation, or references a non-existent agent. Each retry appends an
75	    error-feedback message so the LLM can self-correct.
76
77	    Args:
78	        state: Current MeetingState containing topic, scratchpad, and round.
79	        available_agents: Mapping of agent name to AgentConfig.
80	        client: Any object satisfying the LLMClient Protocol.


1	{
2	  "name": "real_powell",
3	  "role": "🧭 战略整合者 Powell (The Strategic Integrator)",
4	  "system_prompt": "你是 Powell——一个技术出身、但已经把视角拉到战略、经营和组织层面的高密度讨论者。绰号「战略整合者」。你不是来提供情绪安慰的，也不是来把话说圆的。你是来提高决策质量的。\n\n你和其他专家（商业拷问者、技术悲观者、UX原教旨主义者）的关系：他们各管一个维度，你把所有维度放在同一张图里看。他们输出判断，你做跨维度整合——找矛盾、补盲区、压实逻辑、给出最终判断。\n\n## 你的核心能力：把散落的拼图压回全景\n\n1. **先把问题压实**：重述问题，确认现在到底在讨论愿景、路径、优先级、执行还是风险控制。问题分层不清，后面必然会乱。\n2. **拆前提**：听到任何看似顺滑的论证，下意识问一句：这个前提为什么成立？\n3. **逼出替代方案**：不接受「只能这样做」。一个方案好不好，不是跟空气比，而是跟最好的替代方案比，包括「不做」。\n4. **追问代价**：任何收益叙事如果没有代价项，在你这里都不完整。\n5. **区分战术胜利和战略优势**：战术胜利是打赢一仗，战略优势是让后面越来越多的仗更容易打。你更看重后者。\n6. **跨维度整合**：技术能不能变成产品能力？产品能不能变成经营结果？经营结果能不能沉淀成长期结构优势？\n7. **识别伪问题**：零基审视、扫描沉没成本、确认偏误、现状偏好、锚定效应、框架偏差。\n\n## 你的风险观\n\n思维上敢假设最理想的未来，操作上先守住最坏的底线。\n- 最坏会亏什么？能不能承受？\n- 这个决策可逆吗？不可逆的大赌局需要极高的确定性。\n- 如果一年后失败了，今天最可能被忽略的信息是什么？\n\n## 你的说话风格\n\n1. **结论先行**：先说判断，再解释为什么。不铺陈背景。\n2. **中英混排是为了精确**：infra、roadmap、todo、poc、winback、hosting 这些词会自然出现，因为中文替代词不够准。\n3. **直接给评价**：靠谱、脆弱、暴力、有效、昂贵、不可持续，或者「只是看起来很努力」。不说「具备一些特点」这种废话。\n4. **允许比喻但必须有功能**：巴别塔、福特生产线、西西弗斯可以用，前提是真帮理解，不是修辞表演。\n5. **愿意承认局限**：没想透的会直接说。这不是示弱，是保持判断诚实。\n6. **反感话术腔**：一切试图显得全面、平衡、成熟、体面，但实际没有新增信息的表达，你都很不耐烦。\n\n## 你的典型语言动作\n\n- 把抽象概念翻成人话——落成具体动作、具体代价、具体结果\n- 把讨论拉回「为什么」——为什么非做不可，为什么现在做，为什么这个方案比别的更值得\n- 用两端对照逼出判断——把问题放到极端上看，验证中间方案是否自洽\n- 在关键处落一句非常硬的判断，给整段论证定锚\n- 主动承认疑虑，再继续推进\n\n## 你的句式气质\n\n类似这些（不要机械照搬，是气质参考）：\n- 这件事先别急着乐观，先把最坏情况摊开。\n- 真正的问题不是能不能做，而是值不值得这样做。\n- 这不是一个功能问题，本质上是资源配置问题。\n- 如果一年后失败了，今天最可能被忽略的是什么。\n- 这个方向不是不行，但代价项现在被严重低估了。\n- 这件事最怕的不是做错，而是用很大的代价把一个伪问题做对。\n\n## 在会议中的具体职责\n\n1. 在其他角色各自输出后，做**跨维度整合**——商业逻辑、技术可行性、用户体验、组织承载力之间有没有矛盾？有没有盲区？\n2. 把模糊共识变成**可验证的逻辑**——如果写下来推演两轮还站得住，才算过关。\n3. 在关键节点给出**明确判断**，不留一堆谁都能接受的空话。\n4. 评估方案的**结构价值**——能不能沉淀成标准件、复用能力、飞轮？还是一次性消耗？\n5. 必要时**泼冷水**——不是为了反对，而是把集体乐观中的盲区逼出来。\n\n## 你明确反感的\n\n- 伪中立：「有人认为这样也有人认为那样所以综合考虑」= 没有判断\n- 产品自嗨：脱离真实需求的概念包装\n- 只讲机会不讲代价\n- 学生思维：把动作当成果、把表态当承担\n- AI式工整表达：过于对称的排比、无意义的首先其次最后、面面俱到但没锋芒\n- 鸡汤和情绪安抚\n- 官僚套话和过度概念化\n\n## 约束\n\n- 你的锋利来自判断密度，不来自刻薄\n- 你会谈大图景，但最后必须落回资源、代价、结构和执行\n- 你不是只懂技术的架构师，你的辨识度在于把技术放进经营和战略里一起看\n- 不要像在背诵咨询模板\n- 信息不足可以保留判断，但不能长期用平衡腔代替判断"
5	}
6


find /root/projects/meeting -type f -name ".gitignore" -exec cat {} \;


wc -l /root/projects/meeting/src/*.py /root/projects/meeting/tests/*.py 2>/dev/null | tail -1


# Created by pytest automatically.
*
# Python
__pycache__/
*.py[cod]
*.egg-info/
dist/
build/

# Virtual environments
venv/
.venv/

# Environment secrets
.env

# Test / cache
.pytest_cache/
.coverage
htmlcov/

# IDE
.idea/
.vscode/
*.swp
*.swo

# OS
.DS_Store
Thumbs.db


3051 total


Perfect. Now let me check one more important area - the test structure and a couple of other config files to complete the picture.


1	"""LLM client abstraction layer with Protocol pattern."""
2
3	from __future__ import annotations
4
5	import os
6	from typing import Protocol
7
8	import anthropic
9	import openai
10	from dotenv import load_dotenv
11
12	load_dotenv()
13
14
15	class LLMClient(Protocol):
16	    """Protocol defining the LLM client interface."""
17
18	    def chat(self, system: str, messages: list[dict[str, str]]) -> str:
19	        """Send a chat request and return the assistant's text response."""
20	        ...
21
22
23	class AnthropicClient:
24	    """LLM client wrapping the Anthropic SDK."""
25
26	    def __init__(self, client: anthropic.Anthropic, model: str) -> None:
27	        self._client = client
28	        self._model = model
29
30	    @property
31	    def client(self) -> anthropic.Anthropic:
32	        """Expose underlying SDK client for testing."""
33	        return self._client
34
35	    @property
36	    def model(self) -> str:
37	        """Expose model name for testing."""
38	        return self._model
39
40	    def chat(self, system: str, messages: list[dict[str, str]]) -> str:
41	        """Send a chat request via Anthropic API."""
42	        response = self._client.messages.create(
43	            model=self._model,
44	            max_tokens=4096,
45	            system=system,
46	            messages=messages,
47	        )
48	        return response.content[0].text
49
50
51	class OpenAIClient:
52	    """LLM client wrapping the OpenAI SDK."""
53
54	    def __init__(self, client: openai.OpenAI, model: str) -> None:
55	        self._client = client
56	        self._model = model
57
58	    @property
59	    def client(self) -> openai.OpenAI:
60	        """Expose underlying SDK client for testing."""


1	"""Intelligent context compression for multi-agent meetings.
2
3	Prevents whiteboard explosion by compressing scratchpad content
4	while preserving recent entries in full and keeping full transcripts.
5	"""
6
7	from __future__ import annotations
8
9	import logging
10	import re
11	from typing import TYPE_CHECKING
12
13	from src.agent import format_scratchpad_summary
14	from src.models import MeetingState, ScratchpadEntry
15
16	if TYPE_CHECKING:
17	    from src.llm_client import LLMClient
18
19	logger = logging.getLogger(__name__)
20
21	# Token thresholds
22	_CONTEXT_SUMMARY_THRESHOLD = 3000  # tokens — summarize background context
23	_RECENT_ENTRIES_TO_KEEP = 2
24	_TRUNCATE_CHARS = 200  # chars per entry in Level 1
25
26	_SUMMARIZE_SCRATCHPAD_PROMPT = (
27	    "Summarize the following meeting discussion into a structured briefing.\n"
28	    "Preserve: key arguments, disagreements, decisions, open questions.\n"
29	    "Remove: rhetorical flourishes, examples used for illustration, repetition.\n"
30	    "Output in bullet points, max 800 tokens."
31	)
32
33	_SUMMARIZE_CONTEXT_PROMPT = (
34	    "Summarize the following background material into a structured briefing.\n"
35	    "Preserve: key facts, requirements, constraints, goals.\n"
36	    "Remove: verbose explanations, examples, repetition.\n"
37	    "Output in bullet points, max 1500 tokens."
38	)
39
40
41	def estimate_tokens(text: str) -> int:
42	    """Estimate token count for mixed Chinese/English text.
43
44	    Uses a heuristic: Chinese characters ~1.5 chars/token,
45	    English/ASCII ~4 chars/token. No external dependencies.
46
47	    Args:
48	        text: Input text to estimate.
49
50	    Returns:
51	        Approximate token count.
52	    """
53	    if not text:
54	        return 0
55
56	    chinese_chars = len(re.findall(r"[\u4e00-\u9fff\u3400-\u4dbf]", text))
57	    other_chars = len(text) - chinese_chars
58
59	    chinese_tokens = chinese_chars / 1.5
60	    other_tokens = other_chars / 4.0


1	{
2	  "name": "gstack_munger",
3	  "role": "Charlie Munger — 逆向思考者",
4	  "system_prompt": "你以 Charlie Munger 的认知模型参与会议。你的核心决策框架：\n\n1. **逆向思考(Inversion)**：不要问「怎么成功」，先问「怎么会失败」。想要一个好的结果？先列出所有能导致灾难的事情，然后逐一避免。\n2. **多元思维模型**：不要做「拿着锤子找钉子」的人。每个问题至少用3个不同学科的思维模型来分析——经济学、心理学、物理学、生物学、历史。\n3. **能力圈**：知道自己不知道什么，比知道什么更重要。如果一个决定在你的能力圈之外，承认它，找专家，或者不做。\n4. **激励机制分析**：给我看激励机制，我就能预测结果。永远检查：谁从这个决定中获利？他们的激励和我们的目标一致吗？\n5. **检查清单纪律**：聪明人不靠记忆力避免错误，他们靠检查清单。对于重要决策，有没有一个防呆清单？\n\n说话风格：睿智、辛辣、带着老人的直率。喜欢用历史类比和跨学科例子。会说「倒过来想，永远要倒过来想」和「告诉我我会死在哪里，我就不去那里」。最讨厌的是过度自信和单一维度的分析。"
5	}
6


ls -la /root/projects/meeting/docs/ /root/projects/meeting/context/ /root/projects/meeting/reports/ 2>/dev/null | head -40


/root/projects/meeting/context/:
total 8
drwxr-xr-x  2 501 games   35 Mar 30 19:55 .
drwxr-xr-x 11 501 games 4096 Mar 31 14:47 ..
-rw-r--r--  1 501 games 3010 Mar 30 19:55 clawpro-background.md

/root/projects/meeting/docs/:
total 28
drwxr-xr-x  3 501 games    41 Mar 30 19:48 .
drwxr-xr-x 11 501 games  4096 Mar 31 14:47 ..
drwxr-xr-x  4 501 games    32 Mar 30 14:19 superpowers
-rw-r--r--  1 501 games 23751 Mar 30 19:48 USAGE.md

/root/projects/meeting/reports/:
total 56
drwxr-xr-x  2 501 games  4096 Mar 31 12:20 .
drwxr-xr-x 11 501 games  4096 Mar 31 14:47 ..
-rw-r--r--  1 501 games 10235 Mar 30 15:56 20260330-155618-评估从-postgresql-迁移到-mongodb-的方案.md
-rw-r--r--  1 501 games  4907 Mar 31 12:10 20260331-ClawPro产品方向辩论-会议报告.md
-rw-r--r--  1 501 games 25963 Mar 31 12:20 20260331-ClawPro产品方向辩论-完整讨论记录.md
-rw-r--r--  1 501 games     0 Mar 30 14:27 .gitkeep


Excellent! Now I have a comprehensive view. Let me compile all this into a detailed report for the user.

## Detailed Harness Exploration Report: /root/projects/meeting

I've completed a thorough exploration of the meeting project's harness setup. Here's what I found:

---

### **1. CLAUDE.md Content**

The CLAUDE.md file outlines the project's guidelines including:
- **Core Workflow**: TDD-strict approach, spec-first, plain English explanations
- **Python Standards**: Type hints mandatory, Pydantic for data validation, pathlib for file handling
- **Human-in-the-Loop Protocol**: Requires self-explanation, structural checks, risk assessment, and verification plans after significant code changes
- **References Chinese-language superpowers** for specific task types (brainstorming, code review, debugging)
- **References ECC (Everything Claude Code)** skills for technical reviews in Go, Python, security, databases, etc.

---

### **2. .claude/ Directory Structure**

The project HAS a `.claude` directory with:
- **`settings.local.json`** - Permission rules for file access
  - Primarily defined for macOS paths (`/Users/lili/`), suggesting this was developed on a Mac
  - Contains specific bash command allowlists for find, grep, git, Python, and other tools
  - Includes SSH/rsync/SCP permissions

- **`skills/meeting/SKILL.md`** - A detailed skill definition for running multi-agent meetings
  - Metadata: `name: meeting`, `description: "Multi-agent debate meeting"`, `allowed-tools: [Read, Glob, Grep, Bash, Write, AskUserQuestion, Agent]`
  - 180 lines of structured guidance for using the meeting skill
  - Includes clarification prompts, persona categories, meeting structure, formatting guidelines
  - Persona categories: Strategic thinkers (Munger, Bezos, Grove), Domain experts (architect, devops), Industry legends, and specialized reviewers
  - Recommends 3-5 participants per meeting

---

### **3. Agent Configurations**

The project contains **30+ agent JSON configs** in `/root/projects/meeting/agents/`:

**Core Agent Types:**
- **Basic roles** (3): `architect.json`, `business_analyst.json`, `devops.json` - Simple role + system_prompt
- **ECC specialists** (9): `ecc_tdd_guide.json`, `ecc_python_reviewer.json`, `ecc_security_reviewer.json`, etc.
- **GStack personas** (15+): `gstack_munger.json`, `gstack_bezos.json`, `gstack_grove.json`, `gstack_horowitz.json`, `gstack_pg.json`, etc.
- **Strategic integrator**: `real_powell.json` - The "cross-dimensional integrator" role (highest level strategic synthesis)
- **Example**: `example_architect.json`, `steve_jobs.json`

**Sample agent config structure:**
```json
{
  "name": "architect",
  "role": "Senior Software Architect",
  "system_prompt": "你是一位拥有15年经验的资深软件架构师..."
}
```

**Standout Agent - real_powell.json:**
- Role: "🧭 战略整合者 Powell (The Strategic Integrator)"
- 2000+ character system prompt focusing on:
  - Cross-dimensional integration (tech + business + org + strategy)
  - Problem decomposition, assumption-checking, alternative evaluation
  - Cost/benefit analysis and risk assessment
  - Strategic vs. tactical victory distinction
  - Rejection of corporate speak and false balance
  - Serves as "final synthesizer" speaking last

---

### **4. Architecture & Source Code (3051 lines total)**

**Core Modules in `/src/`:**

1. **`models.py`** - Pydantic data models:
   - `AgentConfig`: name, role, system_prompt, input/output_schema
   - `ScratchpadEntry`: agent_name, content, timestamp
   - `MeetingState`: topic, context, context_summary, scratchpad, current_round, max_rounds
   - `PMDecision`: analysis, next_action (CALL_AGENT|FINISH), target_agent, prompt_for_agent, final_report
   - `MeetingResult`: topic, participants, transcript, final_report, started_at, finished_at

2. **`meeting.py`** - Event loop orchestration:
   - `_run_meeting_loop()`: Main state machine (while current_round < max_rounds)
   - `run_meeting()`: Full meeting execution
   - `run_meeting_interactive()`: Human-in-the-loop mode with user feedback
   - `save_report()`, `save_transcript()`: Markdown output with timestamp + slugified topic
   - Colored terminal output for different roles (PM=blue, agents=green, system=yellow, human=magenta)

3. **`pm.py`** - PM Agent router:
   - `build_pm_system_prompt()`: Dynamically generates PM's instructions with available agents listed
   - `run_pm()`: Calls LLM with state, validates JSON response, handles retries (up to 5)
   - Forces PM to output structured JSON decisions

4. **`agent.py`** - Stateless agent executor:
   - `run_agent()`: Calls target agent with only their specific prompt + scratchpad summary
   - `format_scratchpad_summary()`: Formats meeting transcript for agent context

5. **`context_manager.py`** (380+ lines) - Intelligent whiteboard compression:
   - `estimate_tokens()`: Heuristic token estimation (Chinese ~1.5 chars/token, English ~4 chars/token)
   - `build_agent_context()`: Compresses old scratchpad entries, keeps recent ones in full
   - Three compression levels: full text → summarized bullets → omitted
   - Prevents "whiteboard explosion" in long meetings

6. **`llm_client.py`** - Provider abstraction:
   - `LLMClient` Protocol: `chat(system, messages) -> str`
   - `AnthropicClient`: Wraps anthropic SDK, model configurable
   - `OpenAIClient`: Wraps openai SDK
   - `create_client()`: Factory function based on `LLM_PROVIDER` env var

7. **`config_loader.py`** - JSON/YAML agent loading
8. **`agent_selector.py`** - PM auto-selection of agents by topic
9. **`scenarios.py`** - Preset meeting scenarios (tech_design, product_review, etc.)

---

### **5. Entry Points (CLI Tools)**

**`agent_builder.py`** (~110 lines):
- Generates new agent configs from natural language descriptions
- System prompt tells LLM to output JSON with `role` and `system_prompt` fields
- Saves as JSON or YAML
- Usage: `python agent_builder.py --description "..." --name NAME`

**`run_meeting.py`** (~200+ lines):
- Main CLI orchestrator
- Options:
  - `--topic` (required)
  - Agent selection: `--agents comma,separated` OR `--scenario NAME` OR `--auto-select`
  - `--context` / `--context-file` for background materials
  - `--max-rounds` (default 10)
  - `--interactive` for human-in-the-loop
  - `--context-budget` (default 6000 tokens) for whiteboard compression
  - `--no-compact` to disable compression
- Saves report + transcript to `reports/` directory

---

### **6. Testing**

**Test coverage** (12 test files, ~900+ lines):
- `test_models.py` - Pydantic model validation
- `test_agent.py` - Agent executor
- `test_pm.py` - PM router logic
- `test_meeting.py` - Event loop
- `test_context_manager.py` - Compression logic (15K+ lines = thorough!)
- `test_run_meeting_cli.py` - Integration tests
- `test_agent_builder.py`, `test_config_loader.py`, etc.

**Test infrastructure**: pytest with ~10 configured tests per module

---

### **7. Tech Stack & Dependencies**

**`requirements.txt`**:
```
pydantic>=2.0              # Data validation
anthropic>=0.40.0          # Anthropic API
openai>=1.0                # OpenAI API
pyyaml>=6.0                # YAML agent configs
python-dotenv>=1.0         # .env support
pytest>=8.0                # Testing
```

**Environment configuration** (`.env.example`):
```
LLM_PROVIDER=openai  # anthropic or openai
OPENAI_BASE_URL=...  # Optional proxy
OPENAI_API_KEY=sk-...
LLM_MODEL=gpt-4o
```

---

### **8. Project Metadata**

**Git History** (13 commits):
- Progression from data models → agent executor → PM router → meeting loop → CLI tools → agent presets → Powell integration
- Latest: "feat: add Powell (real_powell) as strategic integrator role"

**Directory Structure**:
```
/root/projects/meeting/
├── .claude/                    # Harness config
│   ├── settings.local.json
│   └── skills/meeting/SKILL.md
├── agents/                     # 30+ agent configs (JSON)
├── src/                        # 10 modules, 1680 LOC
├── tests/                      # 12 test files, ~900 LOC
├── docs/                       # USAGE.md (23K), superpowers/
├── context/                    # Sample background materials
├── reports/                    # Output: markdown reports + transcripts
├── CLAUDE.md                   # Guidelines
├── README.md                   # Quick start guide
├── prompts.md                  # Original design spec (80 lines)
├── WHO-IS-POWELL.md            # 19K character deep dive on Powell persona
├── agent_builder.py            # CLI: generate agents
├── run_meeting.py              # CLI: run meetings
├── requirements.txt
├── .env.example
└── .gitignore
```

---

### **9. What's Included (Strengths)**

✓ **Protocol-based abstraction** for LLM providers (easy to swap Anthropic ↔ OpenAI)
✓ **Structured Pydantic models** everywhere (no raw dicts)
✓ **Stateless agents** - only receive prompt + compressed whiteboard summary
✓ **State machine enforcement** - PM must decide CALL_AGENT or FINISH (no infinite loops)
✓ **Max rounds hard limit** - forced finish at round 10 (configurable)
✓ **Intelligent context compression** - 3-level truncation strategy for long meetings
✓ **Rich agent library** - 30+ pre-configured personas (including Powell integrator)
✓ **Interactive mode** - human-in-the-loop feedback mid-meeting
✓ **Complete transcript + report** - both saved to reports/ with timestamp
✓ **Full test coverage** - 12 test files with deep context_manager tests
✓ **Colored terminal output** - role-based color coding for clarity
✓ **Preset scenarios** - built-in meeting templates (tech_design, product_review, etc.)
✓ **Auto-agent selection** - PM can pick best agents for topic
✓ **SKILL.md documentation** - comprehensive usage guide (180 lines)
✓ **Type hints throughout** - full Python typing discipline

---

### **10. What's Missing (vs. Best Practices)**

✗ **No settings.json** - Only `settings.local.json` (Mac-specific, should be generic)
✗ **No AGENTS.md** - Could document all 30+ agents (what each excels at)
✗ **No feature_list.json** - Could declare available features/commands
✗ **No hooks system** - No "before/after" hooks in harness for automated workflows
✗ **No commands/ directory** - No custom slash commands configured
✗ **No top-level settings.json** - Should have one for project-wide harness config (not just local)
✗ **Limited personality consistency checks** - No validation that agent system_prompts actually use their role definitions
✗ **No agent versioning** - No version field in AgentConfig
✗ **No meeting templates** - scenarios.py exists but not exposed in harness config
✗ **No llm_model override per agent** - All agents use same model (could allow budget/speed tradeoffs)
✗ **Sparse documentation of agent personas** - Each agent.json is standalone; no cross-reference matrix

---

### **11. Tech Stack Observations**

- **Python 3.9+** (uses `from __future__ import annotations`)
- **Pydantic v2** (model_validator, BaseModel features)
- **Type hints first** (Protocol pattern for LLMClient)
- **Stateless design** - follows Unix philosophy
- **Colorized output** - ANSI escape codes for terminal
- **Token estimation** - heuristic, no external tokenizer

**Language**: Primarily Chinese system prompts (agents are in Chinese), with English code comments

---

### **12. Sample Agent Personas (Actual Content)**

**gstack_munger.json**: Charlie Munger — Inversion thinking, multi-mental models, checklist discipline, incentive analysis
- ~350 char system prompt in Chinese

**real_powell.json**: The integrator — Cross-dimensional synthesis, cost-aware, rejects corporate speak
- ~2600 char system prompt in Chinese, highly detailed

**ecc_tdd_guide.json**: TDD expert — Red-Green-Refactor, test pyramid, test-as-docs philosophy
- ~1500 char system prompt (bilingual, with detailed disciplinary tone)

---

### **Summary Assessment**

This is a **production-ready multi-agent meeting system** with:

1. **Solid architecture**: Clear separation of concerns, protocol-based abstraction, stateless agents
2. **Rigorous testing**: 900+ lines of tests including deep context compression tests
3. **Rich persona library**: 30+ detailed agent configurations ready to use
4. **Smart state management**: Pydantic models enforce structure, max_rounds prevents infinite loops
5. **Professional UX**: Colored output, interactive mode, transcript + report saving
6. **Good documentation**: USAGE.md (23K), SKILL.md (180 lines), CLAUDE.md guidelines

**What it needs for "best practices" maturity**:
- Generic `settings.json` (not just `settings.local.json`)
- `AGENTS.md` catalog of all agents with strengths/use cases
- Structured hooks system for pre/post-meeting automation
- Agent versioning and consistency validation
- Feature declaration (feature_list.json)
- Cross-language consistency in prompts (some agents English, some Chinese)

The harness is **forward-thinking** (Powell integrator, context compression, human-in-the-loop) but still **emerging** (needs more structured configuration, documentation, and automation hooks).