Agent skill

brainstorm

Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.

Stars 143
Forks 15

Install this agent skill to your Project

npx add-skill https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/brainstorm

Metadata

Additional technical details for this skill

category
workflow-automation
mcp server
memory

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

python
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")

python
# 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

Phase Handoff File Contents
0 00-topic-analysis.json Agent list, tier, topic classification
1 01-memory-context.json Prior patterns, codebase signals
2 02-divergent-ideas.json 10+ raw ideas
3 03-feasibility.json Filtered viable ideas
4 04-evaluation.json Rated + devil's advocate results
5 05-synthesis.json Top 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)

python
# 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

Signal Tier
README says "take-home", "assignment", time limit 1. Interview
< 10 files, no CI, no Docker 2. Hackathon
.github/workflows/, 10-25 deps 3. MVP
Module boundaries, Redis, background jobs 4. Growth
K8s/Terraform, DDD structure, monorepo 5. Enterprise
CONTRIBUTING.md, LICENSE, minimal deps 6. Open Source

If confidence is low, ask the user:

python
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:

python
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:

python
# 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:

python
# 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)
Aspect Task Tool Agent Teams
Idea generation Each agent generates independently Agents riff on each other's ideas
Devil's advocate Lead challenges after all complete Agents challenge each other in real-time
Cost ~150K tokens ~400K tokens
Best for Quick ideation, constrained design Open 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)

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 Level Phases Run Token Budget Agents
low Phase 0 → Phase 2 (quick ideation) → Phase 5 (light synthesis) ~50K 2 max
medium Phase 0 → Phase 2 → Phase 3 → Phase 5 → Phase 6 ~150K 3 max
high (default) All 7 phases ~400K 3-5
python
# 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)

python
# 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

Phase Activities Output
0. Topic Analysis Classify keywords, select 3-5 agents Agent list
1. Memory + Context Search graph, check codebase, read experiment journal Prior patterns
2. Divergent Exploration Generate 10+ ideas WITHOUT filtering Idea pool
3. Keep/Discard Gate Binary viability: keep, discard, or crash (10s per idea) Survivors only
4. Evaluation & Rating Rate 0-10 (7 dimensions incl. simplicity), devil's advocate Ranked ideas
5. Synthesis Filter to top 2-3, trade-off table, test strategy per approach Options
6. Design Presentation Present in 200-300 word sections, log to experiment journal Validated design

Progressive Output (CC 2.1.76)

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

After Phase Show User
0. Topic Analysis Selected agents, tier classification
1. Memory + Context Prior decisions, relevant patterns, experiment journal summary
2. Divergent Exploration Each agent's ideas as they return (don't wait for all)
3. Keep/Discard Gate Survivors and discard reasons (keep/discard/crash per idea)
4. Evaluation Top-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 Example Agents 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):

python
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:

python
# 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).

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

Principle Application
Dynamic agent selection Select agents based on topic keywords
Parallel research Launch 3-5 agents in ONE message
Memory-first Check graph for past decisions before research
Divergent-first Generate 10+ ideas BEFORE filtering
Task tracking Use TaskCreate/TaskUpdate for progress visibility
YAGNI ruthlessly Remove 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>"):

File Content
phase-workflow.md Detailed 7-phase instructions
divergent-techniques.md SCAMPER, Mind Mapping, etc.
evaluation-rubric.md 0-10 scoring criteria
devils-advocate-prompts.md Challenge templates
socratic-questions.md Requirements discovery
common-pitfalls.md Mistakes to avoid
example-session-dashboard.md Complete example

Version: 4.8.0 (April 2026) — Fork-eligible agents for 30-50% cost reduction (#1227)

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