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Agent Skill · 研究检索

brainstorm(头脑风暴)

brainstorm 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

brainstorm 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 38,711。

总安装 1,521周安装 64GitHub Stars 160下载 532核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:brainstorm(头脑风暴)
来源仓库:https://github.com/yonatangross/orchestkit
仓库路径:skills/brainstorm
安装命令:
npx skills add https://github.com/yonatangross/orchestkit --skill brainstorm
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/yonatangross/orchestkit --skill brainstorm

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。根据关键词、任务场景或来源线索进行信息匹配与筛选,支持多宿主环境集成。通过 npx skills add 命令从 GitHub 仓库安装,需结合原始 README 确认具体用法。

安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。brainstorm 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/brainstorm

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/yonatangross/orchestkit --skill brainstorm;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

brainstorm 怎么安装?

在终端执行 npx skills add https://github.com/yonatangross/orchestkit --skill brainstorm。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

brainstorm 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

brainstorm 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

brainstorm 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

brainstorm 用的人多吗?

公开数据是累计安装 1,521 次,近一周安装 64 次,下载 532 次,GitHub 160 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

brainstorm 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
brainstorm

头脑风暴 · 本页

1,521160调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Brainstorming Ideas Into Designs

Transform rough ideas into fully-formed designs through intelligent agent selection and structured exploration.

Core principle: Analyze the topic, select relevant agents dynamically, explore alternatives in parallel, present design incrementally.

Argument Resolution

TOPIC = "$ARGUMENTS"  # Full argument string, e.g., "API design for payments"
# $ARGUMENTS[0] is the first token (CC 2.1.59 indexed access)

STEP -1: MCP Probe + Resume Check

Load: Read("${CLAUDE_PLUGIN_ROOT}/skills/chain-patterns/references/mcp-detection.md")
# 1. Probe MCP servers (once at skill start)
ToolSearch(query="select:mcp__memory__search_nodes")
ToolSearch(query="select:mcp__sequential-thinking__sequentialthinking")

# 2. Store capabilities
Write(".claude/chain/capabilities.json", {
  "memory": probe_memory.found,
  "sequential_thinking": probe_st.found,
  "skill": "brainstorm",
  "timestamp": now()
})

# 3. Check for resume (prior session may have crashed)
state = Read(".claude/chain/state.json")  # may not exist
if state.skill == "brainstorm" and state.status == "in_progress":
    # Skip completed phases, resume from state.current_phase
    last_handoff = Read(f".claude/chain/{state.last_handoff}")

Phase Handoffs

PhaseHandoff FileContents
000-topic-analysis.jsonAgent list, tier, topic classification
101-memory-context.jsonPrior patterns, codebase signals
202-divergent-ideas.json10+ raw ideas
303-feasibility.jsonFiltered viable ideas
404-evaluation.jsonRated + devil's advocate results
505-synthesis.jsonTop 2-3 approaches, trade-off table

STEP 0: Project Context Discovery

BEFORE creating tasks or selecting agents, detect the project tier. This becomes the complexity ceiling for all downstream decisions.

Auto-Detection (scan codebase)

# PARALLEL — quick signals (launch all in ONE message)
Grep(pattern="take-home|assignment|interview|hackathon", glob="README*", output_mode="content")
Grep(pattern="take-home|assignment|interview|hackathon", glob="*.md", output_mode="content")
Glob(pattern=".github/workflows/*")
Glob(pattern="**/Dockerfile")
Glob(pattern="**/terraform/**")
Glob(pattern="**/k8s/**")
Glob(pattern="CONTRIBUTING.md")

Tier Classification

SignalTier
README says "take-home", "assignment", time limit1. Interview
< 10 files, no CI, no Docker2. Hackathon
.github/workflows/, 10-25 deps3. MVP
Module boundaries, Redis, background jobs4. Growth
K8s/Terraform, DDD structure, monorepo5. Enterprise
CONTRIBUTING.md, LICENSE, minimal deps6. Open Source

If confidence is low, ask the user:

AskUserQuestion(questions=[{
  "question": "What kind of project is this?",
  "header": "Project tier",
  "options": [
    {"label": "Interview / take-home", "description": "8-15 files, 200-600 LOC, simple architecture", "markdown": "```\nTier 1: Interview / Take-Home\n─────────────────────────────\nFiles:    8-15 max\nLOC:      200-600\nArch:     Flat structure, no abstractions\nPatterns: Direct imports, inline logic\nTests:    Unit only, co-located\n```"},
    {"label": "Startup / MVP", "description": "MVC monolith, managed services, ship fast", "markdown": "```\nTier 3: Startup / MVP\n─────────────────────\nArch:     MVC monolith\nDB:       Managed (RDS/Supabase)\nCI:       GitHub Actions (1-2 workflows)\nPatterns: Service layer, repository pattern\nDeploy:   Vercel / Railway / Fly.io\n```"},
    {"label": "Growth / enterprise", "description": "Modular monolith or DDD, full observability", "markdown": "```\nTier 4-5: Growth / Enterprise\n─────────────────────────────\nArch:     Modular monolith or DDD\nInfra:    K8s, Terraform, Redis, queues\nCI:       Multi-stage pipelines\nPatterns: Hexagonal, CQRS, event-driven\nObserve:  Structured logging, tracing\n```"},
    {"label": "Open source library", "description": "Minimal API surface, exhaustive tests", "markdown": "```\nTier 6: Open Source Library\n──────────────────────────\nAPI:      Minimal public surface\nTests:    100% coverage, property-based\nDocs:     README, API docs, examples\nCI:       Matrix builds, release automation\nPatterns: Semver, CONTRIBUTING.md\n```"}
  ],
  "multiSelect": false
}])

Pass the detected tier as context to ALL downstream agents and phases. The tier constrains which patterns are appropriate — see scope-appropriate-architecture skill for the full matrix.

Override: User can always override the detected tier. Warn them of trade-offs if they choose a higher tier than detected.

STEP 0a: Verify User Intent with AskUserQuestion

Clarify brainstorming constraints:

AskUserQuestion(
  questions=[
    {
      "question": "What type of design exploration?",
      "header": "Type",
      "options": [
        {"label": "Open exploration (Recommended)", "description": "Generate 10+ ideas, evaluate all, synthesize top 3", "markdown": "```\nOpen Exploration (7 phases)\n──────────────────────────\n  Diverge        Evaluate       Synthesize\n  ┌─────┐       ┌─────┐       ┌─────┐\n  │ 10+ │──────▶│Rate │──────▶│Top 3│\n  │ideas│       │0-10 │       │picks│\n  └─────┘       └─────┘       └─────┘\n  3-5 agents    Devil's        Trade-off\n  in parallel   advocate       table\n```"},
        {"label": "Constrained design", "description": "I have specific requirements to work within", "markdown": "```\nConstrained Design\n──────────────────\n  Requirements ──▶ Feasibility ──▶ Design\n  ┌──────────┐    ┌──────────┐    ┌──────┐\n  │ Fixed    │    │ Check    │    │ Best │\n  │ bounds   │    │ fit      │    │ fit  │\n  └──────────┘    └──────────┘    └──────┘\n  Skip divergent phase, focus on\n  feasibility within constraints\n```"},
        {"label": "Comparison", "description": "Compare 2-3 specific approaches I have in mind", "markdown": "```\nComparison Mode\n───────────────\n  Approach A ──┐\n  Approach B ──┼──▶ Rate 0-10 ──▶ Winner\n  Approach C ──┘    (6 dims)\n\n  Skip ideation, jump straight\n  to evaluation + trade-off table\n```"},
        {"label": "Quick ideation", "description": "Generate ideas fast, skip deep evaluation", "markdown": "```\nQuick Ideation\n──────────────\n  Braindump ──▶ Light filter ──▶ List\n  ┌────────┐   ┌────────────┐   ┌────┐\n  │ 10+    │   │ Viable?    │   │ 5-7│\n  │ ideas  │   │ Y/N only   │   │ out│\n  └────────┘   └────────────┘   └────┘\n  Fast pass, no deep scoring\n```"},
        {"label": "Plan first", "description": "Structured exploration before generating ideas", "markdown": "```\nPlan Mode Exploration\n─────────────────────\n  1. EnterPlanMode($TOPIC)\n  2. Analyze constraints\n  3. Research precedents\n  4. Map solution space\n  5. ExitPlanMode → options\n  6. User picks direction\n  7. Deep dive on chosen path\n\n  Best for: Architecture,\n  design systems, trade-offs\n```"},
        {"label": "Iterative optimization", "description": "Try, measure, keep/discard, repeat (autoresearch-style)", "markdown": "```\nIterative Optimization (autoresearch-style)\n───────────────────────────────────────────\n  ┌──────────┐\n  │ Baseline │──measure──┐\n  └──────────┘           │\n       ┌─────────────────┘\n       ▼\n  ┌─────────┐  ┌─────────┐  ┌──────────┐\n  │ Try     │─▶│ Measure │─▶│ Keep or  │─┐\n  │ variant │  │ metric  │  │ Discard  │ │\n  └─────────┘  └─────────┘  └──────────┘ │\n       ▲                                 │\n       └─────────────────────────────────┘\n  Requires: one command + one metric\n  Runs until: user interrupts or plateau\n```"}
      ],
      "multiSelect": false
    },
    {
      "question": "Any preferences or constraints?",
      "header": "Constraints",
      "options": [
        {"label": "None", "description": "Explore all possibilities"},
        {"label": "Use existing patterns", "description": "Prefer patterns already in codebase"},
        {"label": "Minimize complexity", "description": "Favor simpler solutions"},
        {"label": "I'll specify", "description": "Let me provide specific constraints"}
      ],
      "multiSelect": false
    }
  ]
)

If 'Plan first' selected:

# 1. Enter read-only plan mode
EnterPlanMode("Brainstorm exploration: $TOPIC")

# 2. Research phase — Read/Grep/Glob ONLY, no Write/Edit
#    - Scan existing codebase for related patterns
#    - Search for prior decisions on this topic (memory graph)
#    - Identify constraints, dependencies, and trade-offs

# 3. Produce structured exploration plan:
#    - Key questions to answer
#    - Dimensions to explore
#    - Agents to spawn and their focus areas
#    - Evaluation criteria

# 4. Exit plan mode — returns plan for user approval
ExitPlanMode()

# 5. User reviews. If approved → continue to Phase 1 with plan as input.

Based on answers, adjust workflow:

  • Open exploration: Full 7-phase process with all agents
  • Constrained design: Skip divergent phase, focus on feasibility
  • Comparison: Skip ideation, jump to evaluation phase
  • Quick ideation: Generate ideas, skip deep evaluation
  • Iterative optimization: Skip phases 2-6, enter autoresearch-style loop (see below)

If 'Iterative optimization' selected:

# 1. Ask for metric definition
AskUserQuestion(questions=[
  {"question": "What command produces the metric?",
   "header": "Metric command",
   "options": [
     {"label": "npm run benchmark", "description": "Node.js benchmark suite"},
     {"label": "pytest --tb=short", "description": "Python test suite"},
     {"label": "lighthouse --output=json", "description": "Web performance score"},
     {"label": "I'll type my own", "description": "Custom command"}
   ]},
  {"question": "How to extract the metric number?",
   "header": "Metric extraction",
   "options": [
     {"label": "grep from stdout", "description": "e.g. grep 'score:' output.log"},
     {"label": "JSON field", "description": "e.g. jq '.score' result.json"},
     {"label": "Exit code", "description": "0 = pass, non-zero = fail"},
     {"label": "I'll specify", "description": "Custom extraction"}
   ]},
  {"question": "Direction?",
   "header": "Optimization direction",
   "options": [
     {"label": "Lower is better", "description": "Latency, bundle size, error rate"},
     {"label": "Higher is better", "description": "Score, throughput, coverage"}
   ]}
])

# 2. Establish baseline
Bash(command="{metric_command} > .claude/experiments/baseline.log 2>&1")
baseline = extract_metric(".claude/experiments/baseline.log")
append_to_journal(baseline, "keep", "-", current_commit, "baseline")

# 3. Enter the optimization loop — see chain-patterns/references/experiment-journal.md
# LOOP (until user interrupts or trajectory == "stuck" for 5+ iterations):
#   a. Generate ONE idea (quick ideation, single agent)
#   b. Implement in worktree: Agent(isolation="worktree", ...)
#   c. Run metric command in worktree
#   d. Compare to previous best
#   e. If improved: merge worktree back, log "keep"
#   f. If not: discard worktree, log "discard"
#   g. Check trajectory — if "stuck" for 5+, try radical changes
#   h. NEVER STOP — continue until user interrupts

STEP 0b: Select Orchestration Mode (skip for Tier 1-2)

Choose Agent Teams (mesh — agents debate and challenge ideas) or Task tool (star — all report to lead):

  1. Agent Teams mode (GA since CC 2.1.33) → recommended for 3+ agents (real-time debate produces better ideas)
  2. Task tool mode → for quick ideation
  3. ORCHESTKIT_FORCE_TASK_TOOL=1 → Task tool (override)
AspectTask ToolAgent Teams
Idea generationEach agent generates independentlyAgents riff on each other's ideas
Devil's advocateLead challenges after all completeAgents challenge each other in real-time
Cost~150K tokens~400K tokens
Best forQuick ideation, constrained designOpen exploration, deep evaluation
Fallback: If Agent Teams encounters issues, fall back to Task tool for remaining phases.

STEP 0c: Effort-Aware Phase Scaling (CC 2.1.76; xhigh added in 2.1.111)

Read the /effort setting to scale brainstorm depth. The effort-aware context budgeting hook (global) detects effort level automatically — adapt the phase plan accordingly:

Effort LevelPhases RunToken BudgetAgents
lowPhase 0 → Phase 2 (quick ideation) → Phase 5 (light synthesis)~50K2 max
mediumPhase 0 → Phase 2 → Phase 3 → Phase 5 → Phase 6~150K3 max
high (default)All 7 phases~400K3-5
xhigh (Opus 4.7 only, CC 2.1.111+)All 7 phases + extra devil's-advocate round in Phase 4 + extra synthesis dimension in Phase 5~550K3-5
# Effort detection — the global hook injects effort level, but also check:
# If user said "quick brainstorm" or "just ideas" → treat as low effort
# If user selected "Quick ideation" in Step 0a → treat as low effort regardless of /effort
Override: Explicit user selection in Step 0a (e.g., "Open exploration") overrides /effort downscaling.

CRITICAL: Task Management is MANDATORY (CC 2.1.16)

# 1. Create main task IMMEDIATELY
TaskCreate(
  subject="Brainstorm: {topic}",
  description="Design exploration with parallel agent research",
  activeForm="Brainstorming {topic}"
)

# 2. Create subtasks for each phase
TaskCreate(subject="Analyze topic and select agents", activeForm="Analyzing topic")          # id=2
TaskCreate(subject="Search memory for past decisions", activeForm="Searching knowledge graph") # id=3
TaskCreate(subject="Generate divergent ideas (10+)", activeForm="Generating ideas")          # id=4
TaskCreate(subject="Feasibility fast-check", activeForm="Checking feasibility")              # id=5
TaskCreate(subject="Evaluate with devil's advocate", activeForm="Evaluating ideas")          # id=6
TaskCreate(subject="Synthesize top approaches", activeForm="Synthesizing approaches")        # id=7
TaskCreate(subject="Present design options", activeForm="Presenting options")                # id=8

# 3. Set dependencies (sequential chain: 2→3→4→5→6→7→8)
for i in range(3, 9):
    TaskUpdate(taskId=str(i), addBlockedBy=[str(i-1)])

# 4. Before starting each task, verify it's unblocked
task = TaskGet(taskId="2")  # Verify blockedBy is empty
# 5. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done — repeat for each subtask

The Seven-Phase Process

PhaseActivitiesOutput
0. Topic AnalysisClassify keywords, select 3-5 agentsAgent list
1. Memory + ContextSearch graph, check codebase, read experiment journalPrior patterns
2. Divergent ExplorationGenerate 10+ ideas WITHOUT filteringIdea pool
3. Keep/Discard GateBinary viability: keep, discard, or crash (10s per idea)Survivors only
4. Evaluation & RatingRate 0-10 (7 dimensions incl. simplicity), devil's advocateRanked ideas
5. SynthesisFilter to top 2-3, trade-off table, test strategy per approachOptions
6. Design PresentationPresent in 200-300 word sections, log to experiment journalValidated design

Progressive Output (CC 2.1.76)

Output results incrementally after each phase — don't batch everything until the end:

After PhaseShow User
0. Topic AnalysisSelected agents, tier classification
1. Memory + ContextPrior decisions, relevant patterns, experiment journal summary
2. Divergent ExplorationEach agent's ideas as they return (don't wait for all)
3. Keep/Discard GateSurvivors and discard reasons (keep/discard/crash per idea)
4. EvaluationTop-rated ideas with 7-dimension scores

For Phase 2 parallel agents, output each agent's ideas as soon as it returns — don't wait for all agents. This lets users see early ideas and redirect the exploration if needed. Showing ideas incrementally also helps users build a mental model of the solution space faster than a final dump.

Load the phase workflow for detailed instructions:

Read("${CLAUDE_SKILL_DIR}/references/phase-workflow.md")

When NOT to Use

Skip brainstorming when:

  • Requirements are crystal clear and specific
  • Only one obvious approach exists
  • User has already designed the solution
  • Time-sensitive bug fix or urgent issue

Quick Reference: Agent Selection

Topic ExampleAgents to Spawn
"brainstorm API for users"workflow-architect, backend-system-architect, security-auditor, test-generator
"brainstorm dashboard UI"workflow-architect, frontend-ui-developer, test-generator
"brainstorm RAG pipeline"workflow-architect, llm-integrator, data-pipeline-engineer, test-generator
"brainstorm caching strategy"workflow-architect, backend-system-architect, frontend-performance-engineer, test-generator
"brainstorm design system"workflow-architect, frontend-ui-developer, design-context-extractor, component-curator, test-generator
"brainstorm event sourcing"workflow-architect, event-driven-architect, backend-system-architect, test-generator
"brainstorm pricing strategy"workflow-architect, product-strategist, web-research-analyst, test-generator
"brainstorm deploy pipeline"workflow-architect, infrastructure-architect, ci-cd-engineer, test-generator

Always include: workflow-architect for system design perspective, test-generator for testability assessment.


Agent Teams Alternative: Brainstorming Team

In Agent Teams mode, form a brainstorming team where agents debate ideas in real-time. Dynamically select teammates based on topic analysis (Phase 0):

TeamCreate(team_name="brainstorm-{topic-slug}", description="Brainstorm {topic}")

# Always include the system design lead
Agent(subagent_type="workflow-architect", name="system-designer",
     team_name="brainstorm-{topic-slug}",
     prompt="""You are the system design lead for brainstorming: {topic}
     DIVERGENT MODE: Generate 3-4 architectural approaches.
     When other teammates share ideas, build on them or propose alternatives.
     Challenge ideas that seem over-engineered — advocate for simplicity.
     After divergent phase, help synthesize the top approaches.""")

# Domain-specific teammates (select 2-3 based on topic keywords)
Agent(subagent_type="backend-system-architect", name="backend-thinker",
     team_name="brainstorm-{topic-slug}",
     prompt="""Brainstorm backend approaches for: {topic}
     DIVERGENT MODE: Generate 3-4 backend-specific ideas.
     When system-designer shares architectural ideas, propose concrete API designs.
     Challenge ideas from other teammates with implementation reality checks.
     Play devil's advocate on complexity vs simplicity trade-offs.""")

Agent(subagent_type="frontend-ui-developer", name="frontend-thinker",
     team_name="brainstorm-{topic-slug}",
     prompt="""Brainstorm frontend approaches for: {topic}
     DIVERGENT MODE: Generate 3-4 UI/UX ideas.
     When backend-thinker proposes APIs, suggest frontend patterns that match.
     Challenge backend proposals that create poor user experiences.
     Advocate for progressive disclosure and accessibility.""")

# Always include: testability assessor
Agent(subagent_type="test-generator", name="testability-assessor",
     team_name="brainstorm-{topic-slug}",
     prompt="""Assess testability for each brainstormed approach: {topic}
     For every idea shared by teammates, evaluate:
     - Can core logic be unit tested without external services?
     - What's the mock/stub surface area?
     - Can it be integration-tested with docker-compose/testcontainers?
     Score testability 0-10 per the evaluation rubric.
     Challenge designs that score below 5 on testability.
     Propose test strategies for the top approaches in synthesis phase.""")

# Optional: Add security-auditor, llm-integrator based on topic

Key advantage: Agents riff on each other's ideas and play devil's advocate in real-time, rather than generating ideas in isolation.

Fork pattern (CC 2.1.89 — #1227): All brainstorm agents are fork-eligible: prompts are <500 words, no custom model, no worktree. CC shares the parent's cached API prefix across forks, reducing cost by ~60%. Do NOT add model= to agent calls. See chain-patterns/references/fork-pattern.md.

Team teardown after synthesis:

# After Phase 5 synthesis and design presentation
SendMessage(type="shutdown_request", recipient="system-designer", content="Brainstorm complete")
SendMessage(type="shutdown_request", recipient="backend-thinker", content="Brainstorm complete")
SendMessage(type="shutdown_request", recipient="frontend-thinker", content="Brainstorm complete")
SendMessage(type="shutdown_request", recipient="testability-assessor", content="Brainstorm complete")
# ... shutdown any additional domain teammates
TeamDelete()

# Worktree cleanup (CC 2.1.72) — for Tier 3+ projects that entered a worktree
# If EnterWorktree was called during brainstorm (e.g., Plan first → worktree), exit it
ExitWorktree(action="keep")  # Keep branch for follow-up /ork:implement
Fallback: If team formation fails, load Read("${CLAUDE_SKILL_DIR}/references/phase-workflow.md") and use standard Phase 2 Task spawns.
Partial results (CC 2.1.76): Background agents that are killed (timeout, context limit) return responses tagged with [PARTIAL RESULT]. When collecting Phase 2 divergent ideas, check each agent's output for this tag. If present, include the partial ideas but note them as incomplete in Phase 3 feasibility. Prefer synthesizing partial results over re-spawning agents.
PostCompact recovery: Long brainstorm sessions may trigger context compaction. The PostCompact hook re-injects branch and task state. If compaction occurs mid-brainstorm, check .claude/chain/state.json for the last completed phase and resume from the next handoff file (see Phase Handoffs table).
Session recap (CC 2.1.108+): After idle periods, use /recap to restore conversational context alongside checkpoint-resume. Enabled by default since CC 2.1.110 (even with telemetry disabled).
Push notifications (CC 2.1.110+): For long brainstorm sessions, use PushNotification to alert when synthesis is complete (requires Remote Control with "Push when Claude decides").
Manual cleanup: If TeamDelete() doesn't terminate all agents, press Ctrl+F twice to force-stop remaining background agents. Note: /clear (CC 2.1.72+) preserves background agents — only foreground tasks are cleared.

Key Principles

PrincipleApplication
Dynamic agent selectionSelect agents based on topic keywords
Parallel researchLaunch 3-5 agents in ONE message
Memory-firstCheck graph for past decisions before research
Divergent-firstGenerate 10+ ideas BEFORE filtering
Task trackingUse TaskCreate/TaskUpdate for progress visibility
YAGNI ruthlesslyRemove unnecessary complexity

Related Skills

  • ork:architecture-decision-record - Document key decisions made during brainstorming
  • ork:implement - Execute the implementation plan after brainstorming completes
  • ork:explore - Deep codebase exploration to understand existing patterns
  • ork:assess - Rate quality 0-10 with dimension breakdown
  • ork:design-to-code - Convert brainstormed UI designs into components
  • ork:component-search - Find existing components before building new ones
  • ork:competitive-analysis - Porter's Five Forces, SWOT for product brainstorms

References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>"):

FileContent
phase-workflow.mdDetailed 7-phase instructions
divergent-techniques.mdSCAMPER, Mind Mapping, etc.
evaluation-rubric.md0-10 scoring criteria
devils-advocate-prompts.mdChallenge templates
socratic-questions.mdRequirements discovery
common-pitfalls.mdMistakes to avoid
example-session-dashboard.mdComplete example

平台分布

Codex

37.09%
按下载量换算197

Claude

30.2%
按下载量换算161

Cursor

17.57%
按下载量换算93

Gemini CLI

8.82%
按下载量换算47

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

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