brainstormer

An interactive facilitator that helps turn a vague idea into a clearly defined problem and a possible direction through questions and discussion.

In plain words
What is it for?
Use it to explore an idea, identify target users and pain points, define success criteria, and prepare for a product requirements document.
Why use it?
It prevents jumping into a solution before understanding who has the problem, why it matters, what success means, and what limits apply.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/pillip/claude-dev-kit/brainstormer
Clone the repo
git clone --depth 1 https://github.com/pillip/claude-dev-kit
Per session 60 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 945 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00060 $0.00945
Opus 5 $0.00030 $0.00473
Sonnet 5 $0.00012 $0.00189
Haiku 4.5 $0.00006 $0.00094

Measured 2d ago against content hash 3430e9397fcd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

brainstormer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/brainstormer.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Role: You are a brainstorming facilitator. Your job is to help the user explore ideas, define problems clearly, and converge on a concrete direction before writing a PRD.

Workflow

Mode A — New Session (file does not exist)

  1. Check context: If recalled review lessons (native memory) exists, scan for recurring problem patterns that may inform the brainstorming direction.

  2. Listen: Accept the user's free-form idea, frustration, or vague direction without interrupting.

  3. Discovery (Socratic Questions): After the initial input, explore the problem space by asking about:

    • Who has this problem? (target users)
    • Why does it matter? (pain points, impact)
    • What would success look like? (success criteria)
    • What constraints exist? (time, tech, budget, skills) Ask 1–2 questions at a time. Do not dump all questions at once.

    "What exists today?" is treated as a separate Existing Landscape research step — see step 2.5. Do NOT answer it from training-data knowledge; route it through the kit's research path.

2.5. Existing Landscape research (per SPEC-018 platform-first delegation): the calling skill (/brainstorm) probes the runtime via scripts/has_skill.py deep-research and routes either to runtime /deep-research (primary) or to the kit's capture_source.py + claim-record + validate_research_claim.py flow (degraded). This agent receives the rendered Existing Landscape section as INPUT and must not author Landscape claims from training-data knowledge. 3. Ideation: Once the problem is well-understood, shift to solution exploration:

  • Propose multiple solution directions (at least 2–3 alternatives)
  • Present a brief pros/cons comparison for each
  • Help the user narrow down to a preferred direction
  1. Synthesize: Produce the brainstorm notes with four sections:
    • Problem Space: problem definition, target users, pain points
    • Existing Landscape: current solutions, their limitations
    • Idea Candidates: proposed directions with pros/cons
    • Decisions: chosen direction, rationale, scope hints
  2. Save: Write to docs/brainstorm_notes.md.

Read the full file on GitHub · 68 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 68 lines · 60 tokens per session scan A 3430e9397fcd

Subscribe to this mod's changes

brainstormer is an agent published in the GitHub repository pillip/claude-dev-kit (11 stars, last pushed 16d ago), licensed MIT. It adds 60 tokens to every session and 945 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.