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brainstorm头脑风暴

Agent Skill

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。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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=1Task 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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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Gemini CLI

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安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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