brainstorming
MUST USE when the user wants new features, behavior changes, refactoring with new capabilities, or architecture decisions and no…
Creates minimal, high-signal CLAUDE.md and AGENTS.md context files based on empirical best practices. Invoke on /init command, "create CLAUDE.md", "update CLAUDE.md", "write AGENTS.md", or "set up Claude Code for this project". Also invoked by brainstorming when repo lacks a
$ npx -y skills add REPOZY/superpowers-optimized --skill claude-md-creator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/claude-md-creatorContext preview
The summary Claude sees to decide when to auto-load this skill.
Creates minimal, high-signal CLAUDE.md and AGENTS.md context files based on empirical best practices. Invoke on /init command, "create CLAUDE.md", "update CLAUDE.md", "write AGENTS.md", or "set up Claude Code for this project". Also invoked by brainstorming when repo lacks a
name: claude-md-creator description: > Creates minimal, high-signal CLAUDE.md and AGENTS.md context files based on empirical best practices. Invoke on /init command, "create CLAUDE.md", "update CLAUDE.md", "write AGENTS.md", or "set up Claude Code for this project". Also invoked by brainstorming when repo lacks a context file. tools: Read, Glob, Grep, Bash, Edit
Creates repository-level context files (`CLAUDE.md`, `AGENTS.md`) that give coding agents the minimum guidance needed to work correctly in a repo.
**Core principle: Only include what the agent cannot easily discover itself.**
Empirical research (Gloaguen et al., "Evaluating AGENTS.md", v3 2026) found that context files did not significantly change task success — LLM-generated ones −0.5% to −2%, developer-written ones +2.4% — while raising cost 20–23%, because agents follow what the file says. Developer-written files did significantly beat LLM-generated ones, which mostly restated existing docs. Every line is an instruction the agent will act on: it costs steps whether or not it helps.
Invoke this skill when any of the following occur:
Agents use a tool almost only when the file names it (`uv`: 1.6 uses per task when mentioned, under 0.01 otherwise), so naming the project's *non-default* tooling reliably changes what they run. That is compliance, not proof of benefit: in the same study, testing instructions raised cost without improving success. Include a command only when the agent would otherwise run the wrong one. Spell those out exactly:
npm run test -- --watch uv run pytest tests/ -x make lint && make typecheck
Env vars, required services, secrets handling, database setup — things the agent would get wrong without being told.
Focus narrowly on constraints where violating them breaks something:
Only patterns unique to this project that differ from standard practice. If it's what any experienced developer would do by default, leave it out.
These categories were measured to give no benefit (or only cost) in the study, or follow directly from its findings:
Nearly every LLM-generated context file included one (100% for Sonnet 4.5, 95–99% for two other models), yet overviews did not meaningfully reduce the steps agents took before reaching the relevant files. The agent explores the repo anyway — an overview just adds tokens without saving any work.
Same finding: detailed directory structures don't help agents locate relevant files. They navigate codebases by searching, not by reading maps.
Broad architecture descriptions don't help agents solve tasks. If there's an architectural constraint that would cause incorrect behavior (e.g., "this is a monorepo — changes to `packages/core` require rebuilding all dependents"), include the constraint. Skip the explanation of how the architecture works.
Don't restate what's already in README, docs/, wiki, or inline comments. Duplication is why LLM-generated files didn't help: when researchers removed the repos' documentation, the same files improved performance by 2.7%. With the docs present, they only added cost.
"Write tests", "follow SOLID principles", "use meaningful variable names" — agents already know these. Only include project-specific deviations from standard practice.
Unnecessary requirements make tasks harder. Every rule you add has a cost — the agent spends reasoning tokens processing it and may over-apply it. Include a constraint only if violating it would cause a real problem in this specific repo.
1. **Scan the repo** — read key config files (`package.json`, `tsconfig.json`, `Makefile`, CI configs, etc.) and source structure. 2. **Identify gaps** — what would an agent get wrong without explicit guidance? Focus on commands, env setup, and constraints that cause breakage. 3. **Ask minimal questions** — only ask about things that can't be inferred from the repo. 4. **Draft a short, high-signal context file** — aim for under ~50 lines. Every line should pass the test: *"would the agent produce incorrect output without this?"* 5. **Self-assess before presenting** — before showing the draft, apply the filter to every line yourself: *"Is this discoverable by reading the code, types, or inline comments?"* If yes, cut it. If unsure, cut it. Only surface lines that would cause incorrect agent behavior if absent. Do not ask the user to identify redundant items — that is your job. 6. **Present the draft for human review** — LLM-generated context files without human review consistently underperform. Walk the user through what you kept and briefly state *why each section survives the filter* (one phrase per section is enough). Ask only questions the codebase cannot answer — e.g., undocumented team conventions, production-only gotchas, or decisions made outside the repo.
The production-grade fork of obra/superpowers — same trusted workflow, dramatically leaner, safer, and more intelligent.
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