Human-Machine Brainstorm (人机风暴)

This skill should be used when the user asks to "人机风暴", "Human-Machine Brainstorm", "human storm", "ccb brainstorm", "需求对齐调度", "spec convergence", or wants a CCB-based multi-model requirement alignment loop with Codex as the dispatcher.

167 stars

Best use case

Human-Machine Brainstorm (人机风暴) is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

This skill should be used when the user asks to "人机风暴", "Human-Machine Brainstorm", "human storm", "ccb brainstorm", "需求对齐调度", "spec convergence", or wants a CCB-based multi-model requirement alignment loop with Codex as the dispatcher.

Teams using Human-Machine Brainstorm (人机风暴) should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/human-machine-brainstorm/SKILL.md --create-dirs "https://raw.githubusercontent.com/cnfjlhj/ai-collab-playbook/main/skills/full/human-machine-brainstorm/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/human-machine-brainstorm/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Human-Machine Brainstorm (人机风暴) Compares

Feature / AgentHuman-Machine Brainstorm (人机风暴)Standard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

This skill should be used when the user asks to "人机风暴", "Human-Machine Brainstorm", "human storm", "ccb brainstorm", "需求对齐调度", "spec convergence", or wants a CCB-based multi-model requirement alignment loop with Codex as the dispatcher.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Human-Machine Brainstorm (HMB) — CCB Dispatcher Loop

## Purpose

Run a repeatable multi-model requirement alignment loop in **CCB** where:
- **Codex** acts as the *dispatcher* (facilitator + router).
- **Claude Code** acts as the *scribe* (single source of truth spec author).
- **OpenCode (Gemini)** acts as the *divergent thinker* (alternatives + ASCII prototypes).

Keep every round auditable by exporting per-provider context into `./.ccb/history/` and keeping the canonical spec in `./.ccb/spec/`.

## Hard Rules

- Treat `./.ccb/spec/overview.md` as the **single source of truth**. Only Claude Code edits it.
- Never assume panes “share context”. Always broadcast updates explicitly.
- Enforce question IDs in this format:
  - Claude: `C-Q01`, `C-Q02`, ...
  - OpenCode: `O-Q01`, `O-Q02`, ...
- Accept user answers only in ID-addressed form (so routing is deterministic).

## Quick Start (zero-friction, recommended)

This workflow is optimized for a **2×Codex** setup:
- **Cmd pane** runs a dedicated **Codex Chair** (dispatcher).
- The normal **Codex provider pane** participates as a reviewer/solver (not just idle).

1) Create (or choose) a topic directory (recommended location: `$HOME/ccb-startups/...`).
   - Optional helper: `bash $HOME/.codex/skills/human-machine-brainstorm/scripts/hmb-init.sh "<topic-slug>"`

2) Start CCB.
   - If CCB global config enables the chair cmd pane, just run: `ccb`
   - Otherwise: `ccb claude codex opencode cmd` (fallback)

3) Talk only to the **Codex Chair** (cmd pane).
   - Paste the raw requirement.
   - The chair broadcasts to `claude`, `opencode`, and participant `codex` via `ask`.
   - Use the round prompt template in `references/round_prompt_template.md` if needed.

## Round Loop (R1/R2/R3...)

### Step A — Broadcast (dispatcher = Codex Chair)

Send the same “Round prompt” to:
- `ask claude "<ROUND PROMPT>"`
- `ask opencode "<ROUND PROMPT>"`
- `ask codex "<ROUND PROMPT>"` (participant Codex pane)

Require them to respond with:
- 10–20 numbered questions using `C-Q##` / `O-Q##` / `P-Q##`
- 1 ASCII diagram (flow/state/component)
- 1 short “current assumptions” list

### Step B — Collect Answers (human)

Ask the human to answer in this format:

- `C-Q01: ...`
- `C-Q02: ...`
- `O-Q01: ...`
- `P-Q01: ...`

Optionally allow a shared block:
- `SHARED: ...` (facts that apply to both)

### Step C — Route Answers (dispatcher = Codex)

Send Claude only `C-*` + `SHARED`.
Send OpenCode only `O-*` + `SHARED`.
Send participant Codex only `P-*` + `SHARED`.

### Step D — Export Evidence (end of round)

From the `cmd` pane (or any shell pane) run:
- `./.ccb/bin/round-save.sh 20`

This writes:
- `./.ccb/history/claude-<timestamp>.md`
- `./.ccb/history/codex-<timestamp>.md`
- `./.ccb/history/opencode-<timestamp>.md`

### Step E — Update Spec (scribe = Claude Code)

Ask Claude to update:
- `./.ccb/spec/overview.md` (bump version vN)
- `./.ccb/spec/open_questions.md` (close answered questions)
- `./.ccb/spec/decisions.md` (record non-reversible decisions)
- `./.ccb/spec/changelog.md` (vN → vN+1 diff)

Then re-run another round until all reviewers say “no blocking issues”.

## Final Handoff to GPT-5.2 (new session)

Provide a clean handoff pack:
- `./.ccb/spec/overview.md`
- `./.ccb/spec/decisions.md`
- `./.ccb/spec/open_questions.md` (should be empty or non-blocking)
- `./.ccb/spec/changelog.md`

Instruct GPT-5.2 to:
- Treat the spec as authoritative
- Output an executable plan first
- Use multi-agent decomposition for implementation/testing/review

Related Skills

writing-anti-ai

167
from cnfjlhj/ai-collab-playbook

This skill should be used when the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI writing" guide, detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases.

Content & Documentation

xhs-note-creator

167
from cnfjlhj/ai-collab-playbook

小红书笔记素材创作技能。当用户需要创建小红书笔记素材时使用这个技能。技能包含:根据用户的需求和提供的资料,撰写小红书笔记内容(标题+正文),生成图片卡片(封面+正文卡片),以及发布小红书笔记。

xhs-longform-private-publisher

167
from cnfjlhj/ai-collab-playbook

This skill should be used when the user wants to publish an existing Markdown article to Xiaohongshu as a private longform post, keep the original wording and structure, insert inline images in order, use one-click layout, and verify the result in note manager.

timestamped-video-summary

167
from cnfjlhj/ai-collab-playbook

Generate a detailed, professional video content summary from timestamped subtitles/transcripts (e.g., lines starting with 00:00 / 1:23:45). Enforce strict per-segment structure (timestamp range + bold segment title + 2-paragraph body: first-person creator summary + expert 【导师评注】 critique with uncertainty handling). Use when the user provides time-coded subtitles and asks for a规范化纪要/内容纪要/逐段总结, and optionally wants a clean PDF export (do NOT include the full raw transcript in the PDF unless explicitly requested).

skills-governance

167
from cnfjlhj/ai-collab-playbook

Use when auditing a large local skill collection, identifying duplicate or imported skills, comparing skill roots, or deciding what to keep, disable, or archive across Codex and adjacent agent skill directories.

skill-governance-loop

167
from cnfjlhj/ai-collab-playbook

Use when the user asks to review a skill, analyze skill quality, update a skill version, or run a repeatable keep/disable/archive decision loop from real failures instead of abstract best practices.

skill-creator

167
from cnfjlhj/ai-collab-playbook

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.

session-recovery-codex

167
from cnfjlhj/ai-collab-playbook

Use when recovering a Codex session, especially if the user provides a Codex session id or wants recent Codex sessions listed before resuming work.

research-lead-sidecar

167
from cnfjlhj/ai-collab-playbook

Use when the user wants multi-agent division of labor for research-led work and the lead should stay on the critical path while 1-2 bounded sidecars handle low-coupling tasks. Do not use this for tiny tasks, fully sequential debugging, or overlapping refactors.

question-refiner

167
from cnfjlhj/ai-collab-playbook

Use when a research question is still vague and must be clarified into a structured deep-research brief before actual literature research or execution. Skip this if the user already has a concrete paper draft or a ready-to-run research specification.

prompt-polisher

167
from cnfjlhj/ai-collab-playbook

Use when receiving messy, unstructured input like voice transcriptions, stream-of-consciousness notes, or rough document content that needs to be transformed into a polished, optimized prompt. Cleans up filler words, extracts intent, asks clarifying questions, applies Claude 4.x/Opus 4.5/Sonnet 4.5 best practices, and previews the polished prompt for approval before execution. Trigger phrases include "polish this", "clean this up", "turn this into a prompt", or when input is clearly rough/unstructured.

proactive-explorer

167
from cnfjlhj/ai-collab-playbook

落实 CLAUDE.md / AGENTS.md 中的“主动探索”原则,在向用户提问前自动使用 Grep、Read、Bash、WebSearch 等工具获取信息