Borrowing it
Nothing to install: this file belongs to HatmanStack/RAGStack-Lambda. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/HatmanStack/RAGStack-Lambda/main/.claude/skills/brainstorm/SKILL.mdgit clone --depth 1 https://github.com/HatmanStack/RAGStack-LambdaWrote 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/hatmanstack/ragstack-lambda/brainstorm)<a href="https://agentmods.dev/skills/hatmanstack/ragstack-lambda/brainstorm"><img src="https://agentmods.dev/badge/skills/hatmanstack/ragstack-lambda/brainstorm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hatmanstack/ragstack-lambda/brainstorm"><img src="https://agentmods.dev/badge/skills/hatmanstack/ragstack-lambda/brainstorm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00031 | $0.01254 |
| Opus 5.5 | $0.00012 | $0.00502 |
| Sonnet 5.5 | $0.00006 | $0.00251 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
brainstorm 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 2mo 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Brainstorm
You are helping the user refine a feature idea into a complete design spec through structured exploration and questioning.
Input
The user will provide a feature idea as $ARGUMENTS. This may be a description, a pointer to a document, or a rough concept.
Process
Step 1: Understand the Feature Idea
Read the user's feature description carefully. If they point to a document, read it.
Step 2: Explore Relevant Codebase
Focus your exploration on areas relevant to the feature idea. Do not survey the entire codebase.
- Use Glob to find files in areas the feature will touch
- Use Grep to find existing patterns, utilities, or conventions
- Use Read to understand key files, config, and project structure
- Check
package.json,requirements.txt, or equivalent for dependencies and scripts - Look at recent git history for active areas:
git log --oneline -20
Build a mental model of: tech stack, project structure, existing patterns the feature should follow, and integration points.
Step 3: Ask Clarifying Questions
Ask questions one at a time. Aim for 5-15 questions total, prioritizing high-impact scope decisions.
Prefer multiple choice, but open-ended is fine when the option space is too large:
The codebase uses DynamoDB for storage. For this feature's data, should we:
A) Add tables to the existing DynamoDB setup
B) Use a different storage approach (e.g., S3 for documents)
C) Both — DynamoDB for metadata, S3 for content
Question priority order:
- Scope — What's in, what's out? MVP vs full vision?
- Architecture — How does this integrate with existing code?
- Data model — What entities, relationships, storage?
- User-facing behavior — Inputs, outputs, error cases?
- Non-functional — Performance, security, deployment constraints?
Rules:
- One question per message
- Wait for the user's answer before asking the next question
- Reference specific files/patterns you found during exploration to ground questions in reality
- If a question has an obvious answer based on existing codebase patterns, state your assumption and ask for confirmation instead
- Track which questions you've asked and what's been decided
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.
- 2mo ago First seen · 151 lines · 31 tokens per session scan A d4b821352394
brainstorm is a skill published in the GitHub repository HatmanStack/RAGStack-Lambda (25 stars, last pushed yesterday), licensed Apache-2.0. It adds 31 tokens to every session and 1,254 once invoked, about $0.0001 per session on Opus 5.5. 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.
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