Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Writes and restructures technical documentation - READMEs, tutorials, how-to guides, user guides, architecture docs, onboarding guides, runbooks and SOPs, troubleshooting guides, release notes, and knowledge base articles - grounded in the actual code or product, with commands and examples verified where possible. Use when the user asks to document a project, feature, system, or process, write or fix a README, explain a codebase to new developers, turn notes into a guide, or make existing docs clearer. For endpoint-by-endpoint API reference or OpenAPI specs, use api-documentation-writer; for CLAUDE.md or AGENTS.md files, use claude-md-writer.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Write documentation that lets a specific reader finish a specific task without asking anyone.
Name the reader and the job. One sentence: "[Reader] needs to [do what] and already knows [what]." A new contributor setting up the repo, an admin configuring SSO, and an executive evaluating the architecture need different documents. If the request does not say, infer from context and state the assumption at the top of your draft.
Pick the document type. Each type answers one question; mixing them is the most common reason docs feel unclear.
| Reader wants to... | Type | Shape |
|---|---|---|
| learn by doing, first time | Tutorial | One guaranteed-to-work path, start to finish |
| complete a known task | How-to guide | Goal, prerequisites, numbered steps, verify |
| look something up | Reference | Tables and lists, complete and consistent |
| understand why | Explanation / architecture | Context, design, trade-offs |
| fix something broken | Troubleshooting | Symptom, cause, fix |
A README is a front door: what it is, quickstart, links to the rest. Templates for every type are in references/templates.md.
Gather facts from the source, not from memory. In a codebase, read the entry points, package.json / pyproject.toml / Makefile scripts, config files, env var usage (grep -r "process.env\|os.environ"), and existing docs. Every command, flag, file path, env var, and default value in the doc must come from something you read or ran. When a fact cannot be verified, mark it [VERIFY: ...] rather than guessing.
Draft with the template, then apply references/style-guide.md. Lead with the most common task. Put prerequisites before step 1, not in the middle.
Test the doc. If you can run commands, walk the steps in order in a clean state and fix anything that fails or needs an unstated step. Otherwise, reread as the reader: is every step possible with only what the doc and prerequisites provide?
Deliver as the actual file (for example README.md or docs/setup.md), not wrapped in commentary. After it, list any [VERIFY] markers, assumptions about the reader, and what should trigger an update (a config format change, a new required env var).
Request: "Write setup docs for this repo for new devs."
package.json scripts dev, test, db:migrate; .env.example lists DATABASE_URL and STRIPE_SECRET_KEY; docker-compose.yml runs Postgres 17; engines.node is >=22..env.example to .env.local and what each variable is for -> docker compose up -d -> npm run db:migrate -> npm run dev with the expected URL -> npm test with the expected output -> Troubleshooting for port 5432 already in use and a missing DATABASE_URL.npm run db:seed) that the app needs; it was added as step 6.还没有评价。欢迎分享你的使用感受。
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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