Agent skill

Brainstorm

by yonatangross in yonatangross/orchestkit

Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring…

MITAuto-check: notesAgent Workflows

Install Brainstorm

skills CLI
$ npx skills add yonatangross/orchestkit --skill brainstorm -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install yonatangross/orchestkit brainstorm --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/yonatangross/orchestkit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/skills/brainstorm .claude/skills/brainstorm && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
brainstorm
GitHub stars
289
Token cost
~7.6k tokens
SKILL.md length
2,444 words
Files
31 (incl. scripts, references, assets)
Skills in repo
108
Repo updated
First seen
Licence
MIT

At a glance

Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring…

  • Brainstorming ideas
  • SKILL.md covers Argument Resolution, STEP -1: MCP Probe + Resume…, STEP 0: Project Context… and STEP 0a: Verify User Intent…, plus 13 more sections
  • Calls python3
  • Exploring solutions

What it does

Brainstorm is an agent skill from yonatangross/orchestkit. Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring (0-10 across 7 dimensions), synthesis of top approaches, and trade-off comparison. Supports open exploration, constrained design, comparison, quick ideation, and iterative optimization modes. Use when brainstorming ideas, exploring solutions, or comparing alternatives.

Its SKILL.md is about 7.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 34 other files, including scripts, reference files and assets (for example `assets/idea-evaluation-template.md`, `checklists/brainstorm-completion.md` and `checklists/brainstorm-session-checklist.md`). Compatibility notes: Claude Code 2.1.277+. Requires memory MCP server.

It sits in Agent Workflows, covering Brainstorming. It works with Model Context Protocol. The repository describes itself as: The Complete AI Development Toolkit for Claude Code. 106 skills, 36 agents, 171 hooks. Install ork for stable (v9.x), or ork-alpha for the v10 line, which ships daily. The licence is MIT.

When your agent uses it

  • Brainstorming ideas
  • Exploring solutions
  • Comparing alternatives

Example prompts

  • “/brainstorm”

Requirements

  • Python 3
  • Docker
  • Compatibility (from SKILL.md): Claude Code 2.1.277+. Requires memory MCP server.
  • Pre-approved tools (allowed-tools): AskUserQuestion, Agent, Workflow, Read, Grep, Glob, Bash, TaskCreate, TaskUpdate, TaskList, TaskStop, ToolSearch, ExitWorktree, PushNotification, mcp__memory__search_nodes

What it can do on your machine

Read from SKILL.md and the folder at commit 0ef71d2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • AskUserQuestion
    • Agent
    • Workflow
    • Read
    • Grep
    • Glob
    • Bash
    • TaskCreate
    • TaskUpdate
    • TaskList

    …and 5 more on the same allowed-tools line.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code 2.1.277+. Requires memory MCP server.

    From compatibility in the SKILL.md frontmatter.

Context cost

Brainstorm loads about 7.6k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 119 tokens; SKILL.md has 2,444 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~119
When it runs · the whole SKILL.md, loaded when a task matches
~7.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~18k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: AskUserQuestion, Agent, Workflow, Read, Grep, Glob, Bash, TaskCreate, TaskUpdate, TaskList, TaskStop

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from yonatangross/orchestkit at commit 0ef71d2, republished under its MIT licence (© yonatangross). 2,444 words, ~7,585 tokens.

Download SKILL.mdSave it as .claude/skills/brainstorm/SKILL.md (or your agent's skills folder). This skill also uses 30 other files; get the full folder from GitHub.
name
brainstorm
description
Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring (0-10 across 7 dimensions), synthesis of top approaches, and trade-off comparison. Supports open exploration, constrained design, comparison, quick ideation, and iterative optimization modes. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
allowed-tools
AskUserQuestion, Agent, Workflow, Read, Grep, Glob, Bash, TaskCreate, TaskUpdate, TaskList, TaskStop, ToolSearch, ExitWorktree, PushNotification, mcp__memory__search_nodes
compatibility
Claude Code 2.1.277+. Requires memory MCP server.
license
MIT
argument-hint
[topic-or-idea]
context
fork
background
false
disable-model-invocation
false
user-invocable
true
skills
architecture-decision-record, api-design, memory, remember, scope-appropriate-architecture, testing-unit, testing-integration, chain-patterns, design-to-code…
model
sonnet
metadata.category
workflow-automation
metadata.mcp-server
memory

Brainstorming Ideas Into Designs

Host-neutral workflow. Invoke by skill name (brainstorm). Claude Code slash routing, YAML hook loaders, and .claude/chain live in references/claude-code.md.

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

Probe MCP servers once at skill start, store capabilities, and resume from any prior crashed session. Each phase emits a JSON handoff file consumed by the next.

Full procedure + handoff-file table: Read("references/mcp-probe-resume.md")


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
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:

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"},
    {"label": "Startup / MVP", "description": "MVC monolith, managed services, ship fast"},
    {"label": "Growth / enterprise", "description": "Modular monolith or DDD, full observability"},
    {"label": "Open source library", "description": "Minimal API surface, exhaustive tests"}
  ],
  "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
# NOTE: AskUserQuestion caps each question at 4 options (CC schema: minItems 2,
# maxItems 4). Use plain `label` + `description` only — the schema permits a
# `preview` field, but on current CC it forces a side-by-side picker layout with
# dead up/down keyboard nav (confirmed 2026-05-28), so skills no longer use it
# (`markdown` was never valid). The 6 legacy
# modes are split across 3 valid questions: Q1 = exploration flow, Q2 folds the
# old "Constrained design" mode into constraints, Q3 carries the orthogonal
# "Plan first" preamble (it composes with any Q1 mode — it was never mutually
# exclusive). "Quick ideation" + STEP 0c /effort=low overlap; both downscale.
AskUserQuestion(
  questions=[
    {
      "question": "What type of design exploration?",
      "header": "Mode",
      "options": [
        {"label": "Open exploration (Recommended)", "description": "Generate 10+ ideas, evaluate all, synthesize top 3"},
        {"label": "Comparison", "description": "Compare 2-3 specific approaches I have in mind"},
        {"label": "Quick ideation", "description": "Generate ideas fast, skip deep evaluation"},
        {"label": "Iterative optimization", "description": "Try, measure, keep/discard, repeat (autoresearch-style)"}
      ],
      "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": "Fixed requirements (constrained)", "description": "Hard requirements to work within — skip divergent phase, focus on feasibility (old 'Constrained design' mode)"}
      ],
      "multiSelect": false
    },
    {
      "question": "Research before ideating?",
      "header": "Plan-first",
      "options": [
        {"label": "No — dive straight in (Recommended)", "description": "Go directly to ideation"},
        {"label": "Yes — plan first", "description": "Read-only research (EnterPlanMode): scan codebase, map the solution space, then ExitPlanMode for approval before Phase 1 (old 'Plan first' mode)"}
      ],
      "multiSelect": false
    }
  ]
)

If Q3 = 'Yes — plan first' (composes with any Q1 mode):

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 (Q1): Full 7-phase process with all agents
  • Comparison (Q1): Skip ideation, jump to evaluation phase
  • Quick ideation (Q1): Generate ideas, skip deep evaluation
  • Iterative optimization (Q1): Skip phases 2-6, enter autoresearch-style loop (see below)
  • Constrained design (Q2 = "Fixed requirements"): Skip divergent phase, focus on feasibility within the stated requirements — composes with any Q1 mode

If 'Iterative optimization' selected: skip Phases 2-6 and enter the autoresearch-style metric-driven loop.

Full sub-flow (metric question, baseline, loop body): Read("references/iterative-optimization-mode.md")


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

Choose Agent Teams (mesh, agents debate and challenge ideas) or Agent 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. Agent tool mode → for quick ideation
  3. ORCHESTKIT_FORCE_TASK_TOOL=1 → Agent tool (override)
AspectAgent 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 Agent tool for remaining phases.


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

Read the /effort setting and scale brainstorm depth — low runs phases 0/2/5 only, high (default) runs all 7, xhigh adds extra devil's-advocate and synthesis rounds. Explicit user choice in STEP 0a always overrides downscaling.

Full level table + detection rules: Read("references/effort-scaling.md")

Phase 2 always runs at effort low, whatever the level above: in-the-loop ideation is where low effort pays, scoring is not. The skill has no effort: frontmatter on purpose (it would lower Phase 4 too); workflows/brainstorm-diverge.js passes effort: "low" to every generator instead. Details in the same reference.


Finish line. Done means: the top approaches are scored on all seven dimensions and the trade-offs are presented for the user to choose. Follow Read("../../shared/rules/long-run-protocol.md"): keep going when a step needs no input from the user, stop and ask only when you can't continue without them or before anything destructive, check each subagent's evidence before accepting it, and mark anything you couldn't confirm with where you looked.

🚨 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. 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
6.4. Living Plan Playground (optional, on request or multi-wave plan)Emit an updatable LPP playground via visualize-plan's living-plan path<slug>.html (living)
6.5. Audio Podcast (signal-fired, optional)Auto-emit brainstorm-podcast.m4a when composite≥8.0 + approaches≥4.m4a file
Phase 6.4 — Living Plan Playground (optional)

When the user asked for a playground output, OR the synthesis produced a multi-wave plan (2+ execution waves with verifiable items), emit the design as a living plan playground instead of a one-shot render: waves from Phase 5 synthesis, 7-dimension scores from Phase 4, discarded ideas with reasons from Phase 3, one item per work unit with a "done when" evidence check. Route through visualize-plan's living-plan path (exemplar living-plan.template.html, update-mode contract in its references/format-dispatch.md §Living-plan update mode). The artifact then tracks its own execution: later sessions flip item statuses in the embedded lpp-state JSON and append to its changelog — one plan = one file, never fork.

Phase 6.5 — Post-synthesis audio podcast (signal-fired, optional)

After Phase 6 lands, optionally invoke scripts/post_synth_podcast.py <session-dir> to auto-emit an audio podcast summarizing the top approaches + trade-offs. Self-skips on every non-happy-path so it never breaks the brainstorm:

bash
python3 ${CLAUDE_SKILL_DIR}/scripts/post_synth_podcast.py "$CLAUDE_JOB_DIR"

Auto-skip conditions (all exit 0, all WARN-logged):

Skip reasonTrigger
signal absentcomposite[recommended] < 8.0 OR len(approaches) < 4
design-doc.md not foundPhase 6 didn't write a design doc to the session dir
yg-mcp-core not importableyg-mcp-core>=0.3.0 not installed (orchestkit is public; yg-mcp-core lives on private pypi.yonyon.ai — HQ-only)
hq-content MCP unreachableMCP server down OR .mcp.json doesn't define hq-content

Session dir must contain brainstorm-output.json (with recommended, composite, approaches) + design-doc.md. Handoff JSON at <session-dir>/brainstorm-podcast.json records status (fired / skipped) and m4a_path on success.

Mirrors Yonatan-HQ/hq-ext-plugin#194 (audio_podcast handler for /hq-ext:decide).

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("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.


Show full SKILL.md (1,251 more words)Show less

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
# CC 2.1.178+: one implicit team per session — no TeamCreate.
# Spawn teammates directly via Agent(name=...). Requires
# CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 (set in ork.settings.json).

# Always include the system design lead
Agent(subagent_type="ork: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="ork: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="ork: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="ork: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; explicit since CC 2.1.232): 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. Since CC 2.1.232 forking is on by default and subagent_type: "fork" selects it explicitly, so the eligibility conditions matter only for ordinary Agent() calls. See chain-patterns/references/fork-pattern.md.

Nested delegation (CC 2.1.172+): Teammates MAY be instructed to delegate a bounded sub-question to their declared sub-agents (e.g. backend-thinker → database-engineer to sanity-check a schema idea) instead of guessing inline. Keep chains ≤ 3 levels deep; divergent ideation itself stays flat — the parallel teammates above ARE the fan-out for independent ideas. See chain-patterns Pattern 9 (CC 2.1.172+).

Team teardown after synthesis:

python
# After Phase 5 synthesis and design presentation
# CC 2.1.178+: no TeamDelete — teammates wind down at turn end
# (press Ctrl+F twice to stop lingering background teammates).

# 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 implement

Fallback: If team formation fails, load Read("references/phase-workflow.md") and use standard Phase 2 Task spawns.

Partial results (CC 2.1.76, extended 2.1.246): Background agents that are killed (timeout, context limit) return responses tagged with [PARTIAL RESULT]. Since CC 2.1.246 an agent that stops at its maxTurns limit is ALSO reported as partial: its summary reads "stopped at its N-turn limit (partial result; continue it with SendMessage to the task-id)" and its output is marked partial instead of appearing finished. When collecting Phase 2 divergent ideas, check each agent's output for either shape. If present, include the partial ideas but note them as incomplete in Phase 3 feasibility. For the turn-limit shape, SendMessage to that agent's id with "continue from where you stopped" reuses its context; a re-spawn starts from zero and pays the prompt again. Never treat a turn-limited agent as finished.

Cross-session replies land in the parent (CC 2.1.248): when a subagent sends SendMessage to another session, the reply is delivered to the parent session's conversation, never to the subagent; a subagent sends and moves on, the parent reads the answer. Cross-session SendMessage / ListAgents also work on Bedrock, Vertex and Foundry and with telemetry disabled (CC 2.1.248).

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). Reactive compaction (CC 2.1.142+) now sizes the first summarize to the actual overflow, so mid-turn stalls are rare — no need to expect a second pass.

When context fills (CC 2.1.141+): Instead of ending the session, use the rewind menu's "Summarize up to here" to compress earlier turns while keeping recent context intact — a deliberate compaction point that pairs with the improved reactive compaction above.

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").

Lighter alternative (CC 2.1.141+): Hooks can emit desktop notifications, window titles, and bells natively via the terminalSequence field in hook JSON output — no Remote Control required, no Anthropic round-trip. Ork's notification hooks (notification/desktop, notification/sound) already emit via terminalSequence (#1847, shipped).

Manual cleanup: To stop lingering background teammates, press Ctrl+F twice (foreground/Stop). Note: /clear (CC 2.1.72+) preserves background agents — only foreground tasks are cleared.

Teammate background tasks survive turn-end (CC 2.1.183): A background task started by a teammate is no longer killed when that teammate finishes its turn. Phase-2 teammates may safely kick off a long run_in_background task (e.g. a feasibility build) and let it outlive their own turn — the result is still collectable at synthesis. Pre-2.1.183 this required the lead to own the background task.

That claim does not cover auto-backgrounded work (#3448): a foreground Bash call the harness backgrounds on timeout is a different path. Its completion notice goes to the agent that spawned it, and if that agent already ended its turn on "I'll wait", the notice lands nowhere and the output is lost with zero logged errors. Two rules follow. The lead owns any research call whose documented runtime can approach the Bash timeout (tavily-research advertises 30-120s and real queries pass 180s). And no teammate ends a turn on "I'll wait for X" without either collecting X or handing ownership of X to the lead.


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

Running unattended with /goal

Set a completion condition with /goal (CC 2.1.139+, 2.1.143+ recommended for this skill) and this skill will keep working across turns until the condition is met. Works in interactive, -p, and Remote Control. The overlay panel shows live elapsed / turns / tokens.

Why 2.1.143+: Pre-2.1.143 /goal evaluator could fire while background shells or delegated subagents were still running — racing the parallel agents this skill spawns in Phase 2. Fixed in CC 2.1.143 (changelog: "/goal evaluator firing while background shells or delegated subagents are still running").

Example completion condition for this skill:

/goal until design.options_count >= 2 AND user_chose_option, or stop after 8 turns

Stops when: 2+ ranked design options presented and the user selects one (or after Phase 6 if running unattended via -p). Compatible with claude.ai Remote Control runs.


Quality Bar

Done means all of these hold:

  • Phase 2 produced >=10 distinct ideas BEFORE any filtering, each surfaced as its agent returned
  • Every idea past the keep/discard gate carries a one-line keep/discard/crash verdict
  • Each surviving idea scores 0-10 on all 7 dimensions (simplicity included) plus one devil's-advocate challenge
  • Synthesis narrows to 2-3 approaches with a trade-off table AND a per-approach test strategy
  • Output ends with ranked options the user chooses from (or Phase 6 completes when running unattended via -p)
  • 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

Picker fallback (#1795)

The picker stall reported in orchestkit#1795 was a schema break, not a CC input bug: questions with >4 options or a markdown field (the markdown key is not valid — the schema field is preview) fail AskUserQuestion validation, so the picker never renders. All skill questions conform to the schema (2–4 options, no preview on multiSelect), enforced by tests/skills/structure/test-askuserquestion-schema.sh. Separately, although the schema permits a preview field, current CC renders any question that uses one in a side-by-side picker layout where ↑/↓ keyboard navigation is dead (confirmed 2026-05-28). So skills now standardize on plain label + description only and no longer set preview. If you still hit a stall on a future CC build, ORK_ASK_FALLBACK=text remains as a defensive opt-in: the lifecycle/ask-fallback-injector hook then tells the assistant to pose options inline as a numbered list. Hook propagates globally — no per-skill edit needed.

References

Load on demand with Read("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
mcp-probe-resume.mdSTEP -1 probe + resume + handoff-file table
iterative-optimization-mode.mdAutoresearch-style metric loop sub-flow
effort-scaling.md/effort levels and phase scaling rules

© yonatangross, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 30 other files (scripts, references, assets) in src/skills/brainstorm of yonatangross/orchestkit.

  • SKILL.md
  • assets/idea-evaluation-template.md
  • checklists/brainstorm-completion.md
  • checklists/brainstorm-session-checklist.md
  • examples/orchestkit-feature-brainstorm.md
  • references/claude-code.md
  • references/common-pitfalls.md
  • references/devils-advocate-prompts.md
  • references/divergent-techniques.md
  • references/effort-scaling.md
  • references/evaluation-rubric.md
  • references/example-session-auth.md
  • references/example-session-dashboard.md
  • references/iterative-optimization-mode.md
  • references/mcp-probe-resume.md
  • references/phase-workflow.md
  • references/socratic-questions.md
  • … and 14 more

Open the folder on GitHubat commit 0ef71d2

Compare with similar skills

Brainstorm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Brainstorm compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Brainstorm this skillyonatangross/orchestkit289—~7.6kAutomated safety check: NotesMIT
Chatgpt App Builderalpic-ai/skybridge2.1k—~1kAutomated safety check: PassMIT
MCP App Builderalpic-ai/skybridge2.1k—~906Automated safety check: PassMIT
Skybridgealpic-ai/skybridge2.1k—~923Automated safety check: PassMIT
BrainstormFrkAk/piyaz194—~4kAutomated safety check: PassAGPL-3.0
PadPerpetualSoftware/pad186—~10kAutomated safety check: NotesApache-2.0

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Categories

Questions about Brainstorm

What does Brainstorm do?

Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring…. Brainstorm is an agent skill from yonatangross/orchestkit. Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring (0-10 across 7 dimensions), synthesis of top approaches, and trade-off comparison.

When should I use Brainstorm?

Brainstorm fits situations like: brainstorming ideas; exploring solutions; comparing alternatives.

How do I install Brainstorm in Claude Code?

Run `npx skills add yonatangross/orchestkit --skill brainstorm -a claude-code`. Or copy the skill folder (src/skills/brainstorm in yonatangross/orchestkit) into .claude/skills/brainstorm in your project. Claude Code loads it when a task matches its description.

How do I install Brainstorm in Codex?

Run `npx skills add yonatangross/orchestkit --skill brainstorm -a codex`. Or copy the skill folder (src/skills/brainstorm in yonatangross/orchestkit) into .agents/skills/brainstorm in your project. Codex loads it when a task matches its description.

Can I use Brainstorm in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add yonatangross/orchestkit --skill brainstorm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/brainstorm, .gemini/skills/brainstorm, .github/skills/brainstorm and .opencode/skills/brainstorm in your project.

What does Brainstorm need to run?

Going by SKILL.md and its folder, Brainstorm needs the command-line tools its instructions call (python3). Our summary lists: Python 3; Docker. Its frontmatter pre-approves these tools: AskUserQuestion, Agent, Workflow, Read, Grep, Glob, Bash, TaskCreate, TaskUpdate, TaskList, TaskStop, ToolSearch, ExitWorktree, PushNotification, mcp__memory__search_nodes. Compatibility (from SKILL.md): Claude Code 2.1.277+. Requires memory MCP server..

Does Brainstorm access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Brainstorm safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Brainstorm use?

Brainstorm is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Brainstorm use?

About 7.6k tokens (SKILL.md is roughly 30k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Brainstorm?

Skills that share tags, products or a category with Brainstorm: Chatgpt App Builder (alpic-ai/skybridge, 2.1k stars), MCP App Builder (alpic-ai/skybridge, 2.1k stars), Skybridge (alpic-ai/skybridge, 2.1k stars) and Brainstorm (FrkAk/piyaz, 194 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Brainstorm?

yonatangross (a GitHub user) maintains it in yonatangross/orchestkit, which has 289 GitHub stars. The repository holds 108 skills in this directory. The repository was last updated on October 7, 2026.

Source: yonatangross/orchestkit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.