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 skills add ronronner02/codepilot-agent --skill spec-brainstormgit clone --depth 1 https://github.com/ronronner02/codepilot-agentWrote 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/ronronner02/codepilot-agent/spec-brainstorm)<a href="https://agentmods.dev/skills/ronronner02/codepilot-agent/spec-brainstorm"><img src="https://agentmods.dev/badge/skills/ronronner02/codepilot-agent/spec-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/ronronner02/codepilot-agent/spec-brainstorm"><img src="https://agentmods.dev/badge/skills/ronronner02/codepilot-agent/spec-brainstorm.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00149 | $0.09251 |
| Opus 5.5 | $0.00060 | $0.03700 |
| Sonnet 5.5 | $0.00030 | $0.01850 |
| Haiku 4.5 | $0.00015 | $0.00925 |
Grade A, and why
spec-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 1mo 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 — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorm a Feature or Improvement
Note: Use the current date from the active host context. Use this when weighting external sources and dating artifacts.
Brainstorming helps answer WHAT to build through collaborative dialogue. It precedes spec-plan, which enriches the same unified plan artifact with HOW to build it.
The durable output of this workflow is a requirements-only unified plan. In other workflows this might be called a lightweight PRD or feature brief. In spec-first, keep the workflow name brainstorm, but write the first version of the plan artifact under docs/plans/ with artifact_readiness: requirements-only so planning does not need to invent product behavior, scope boundaries, or success criteria.
This skill does not implement code. It explores, clarifies, and documents decisions for later planning or execution.
Workflow Contract Summary
- 输入: 尚未收敛的产品想法、问题、目标用户、约束,以及可回源的仓库或外部证据。
- 输出:
artifact_readiness: requirements-only的统一计划,明确 Product Contract、范围边界、成功标准、未决问题与证据限制。 - 硬出口: 关键产品决定未确认、目标仓库或文档 owner 不明确、证据不足以支撑声明,或请求已属于规划、实现、调试、审查时停止并交还 owning route。
- 权威: 用户确认产品语义;LLM 负责探索与语义综合;脚本和 provider 只准备 advisory facts。该 workflow 不授权实现、commit 或 landing。
- 消费者:
spec-plan、spec-doc-review、产品 owner 与后续需求/规划审查者。
Core Principles
- Assess scope first - Match the amount of ceremony to the size and ambiguity of the work.
- Be a thinking partner - Suggest alternatives, challenge assumptions, and explore what-ifs instead of only extracting requirements.
- Resolve product decisions here - User-facing behavior, scope boundaries, and success criteria belong in this workflow. Detailed implementation belongs in planning.
- Keep implementation out of the Product Contract by default - Do not include libraries, schemas, endpoints, file layouts, or code-level design unless the brainstorm itself is inherently about a technical or architectural change.
- Right-size the artifact - Simple work gets a compact requirements-only unified plan or brief alignment. Larger work gets a fuller Product Contract. Do not add ceremony that does not help planning.
- Apply YAGNI to carrying cost, not coding effort - Prefer the simplest approach that delivers meaningful value. Avoid speculative complexity and hypothetical future-proofing, but low-cost polish or delight is worth including when its ongoing cost is small and easy to maintain.
- Keep product confirmation singular - The current conversation user is the only human product confirmer. Ask one highest-impact independent product question at a time; specialist material is evidence, not a second confirmation route.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/agents/slack-researcher.md 10 KB
- references/blindspot-pass.md 7.4 KB
- references/brainstorm-sections.md 19 KB
- references/handoff.md 12 KB
- references/html-rendering.md 34 KB
- references/markdown-rendering.md 10 KB
- references/model-tiers.md 1.5 KB
- references/product-pressure-test.md 4.3 KB
- references/settled-decisions.md 4.8 KB
- references/synthesis-summary.md 25 KB
- references/universal-brainstorming.md 7.1 KB
- references/verdict-routing.md 4.3 KB
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.
- 1mo ago First seen · 308 lines · 149 tokens per session scan A 7d38006a3eb1
spec-brainstorm is a skill published in the GitHub repository ronronner02/codepilot-agent (0 stars, last pushed 1mo ago), licensed MIT. It adds 149 tokens to every session and 9,251 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
generate-ai-rules
Generate AI assistant configuration files for a repository — CLAUDE.md, AGENTS.md, and Cursor rules (.cursor/rules/.mdc) — from codebase analysis. Use whenever the user wants to create or update CLAUDE.md, AGENTS.md, agent rules, Cursor rules, AI coding assistant configuration, or "onboard AI tools" to a project, even…
generate-readme
Generate or refresh a comprehensive, professional README.md for a repository, with architecture overview, mermaid and optional C4 diagrams, repository structure, dependencies, and API documentation. Use whenever the user asks to create, write, update, improve, or regenerate a README, project documentation, or a…
analyze-codebase
Run a multi-agent deep analysis of a codebase, producing AI-readable analysis documents in .ai/docs/ covering structure, dependencies, data flow, request flow, and APIs. Use whenever the user asks to analyze a repository, generate codebase analysis, understand an unfamiliar codebase in depth, or before generating…
secondary-architecture-reviewer
A read-only review skill for OpenSpec changes, Git diffs, frontend, BFF, backend, LangGraph, tools, MCP, and security boundaries. BFF means a backend service tailored for a frontend.
agent-builder
Build production-ready LLM agents with LangGraph, tool use, memory, streaming, and error handling. Use when designing or implementing an AI agent, multi-agent system, or agentic workflow.
mcp_demo
You have a shell. You can write Python files and run them. Use that to answer the user's question by searching a pre-built corpus.