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.
npx agentmods add skills/shousper/claude-kit/brainstormingnpx skills add shousper/claude-kit --skill brainstorminggit clone --depth 1 https://github.com/shousper/claude-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shousper/claude-kit/brainstorming)<a href="https://agentmods.dev/skills/shousper/claude-kit/brainstorming"><img src="https://agentmods.dev/badge/skills/shousper/claude-kit/brainstorming.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00070 | $0.01965 |
| Opus 5 | $0.00035 | $0.00983 |
| Sonnet 5 | $0.00014 | $0.00393 |
| Haiku 4.5 | $0.00007 | $0.00197 |
Grade A, and why
brainstorming 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorming Ideas Into Designs
Overview
Help turn ideas into fully formed designs through collaborative dialogue, enhanced by parallel research scouts.
Deploy scouts to explore project context in parallel, synthesize their findings, then ask questions one at a time to refine the idea. Once you understand what you're building, present the design and get user approval.
Anti-Pattern: "This Is Too Simple To Need A Design"
Every project goes through this process. "Simple" projects are where unexamined assumptions cause the most wasted work. The design can be short, but you MUST present it and get approval.
Checklist
You MUST create a task for each of these items and complete them in order:
- Deploy research scouts — spawn ephemeral Explore-type subagents (Agent tool, no team) to investigate project context in parallel
- Synthesize findings — collect scout reports, build understanding
- Ask clarifying questions — one at a time, understand purpose/constraints/success criteria
- Propose 2-3 approaches — with trade-offs and your recommendation
- Present design — in sections scaled to their complexity, ask after each section whether it looks right so far
- Get explicit design approval — STOP and wait for your human partner to confirm the design. Do NOT proceed until they explicitly approve. If they have concerns, revise and re-present.
- Create worktree — on design approval, invoke kit:git-worktrees to create isolated workspace and cd into it
- Write design doc — save to
docs/plans/YYYY-MM-DD-<topic>-design.mdin the worktree (do NOT commit) - STOP — confirm transition — Tell your human partner the design is documented and ask if they're ready to move to implementation planning. Do NOT invoke writing-plans until they confirm.
- Invoke writing-plans — on confirmation, invoke kit:writing-plans to create the implementation plan
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.
- 4d ago First seen · 166 lines · 70 tokens per session scan A b323346c2316
brainstorming is a skill published in the GitHub repository shousper/claude-kit (4 stars, last pushed 7d ago), licensed MIT. It adds 70 tokens to every session and 1,965 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…