Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Use for any task that spans more than a couple of steps, files, or sessions — refactors, migrations, multi-file features, anything that must survive a context reset or crash. Trigger when the user asks for a plan, says "keep track of this", "don't lose the thread", or when a task is large enough that forgetting step four is likely.
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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。
A plan that lives in the conversation dies with the conversation. A plan that lives in a file survives a crash, a /clear, a laptop reboot, and a different person picking the work up on Monday.
Before starting multi-step work, write the plan to disk. Update it as you go. The file is the source of truth about progress — not memory, not the chat.
Default location: PLAN.md at the repo root for one-off work, or docs/plans/<yyyy-mm-dd>-<topic>.md when the repo keeps plan history. Gitignore it or commit it — ask once, then follow the repo's convention.
# <Task> — plan
**Goal:** <one sentence, in the user's words>
**Done when:** <the observable condition that ends this work>
## Constraints
- <the things that must stay true: no API break, no new deps, ships today>
## Steps
- [ ] 1. <action> — `path/to/file`
- [ ] 2. <action> — `path/to/file`
- [ ] 3. <action> — verify with `<command>`
## Notes
<discoveries, gotchas, things that turned out to be false>
## Log
- <date/time> — <what happened>
Each step is one action on a named file or a named command. "Refactor the auth layer" is not a step; "move verifyToken out of api/auth.ts into lib/token.ts and update the three imports" is.
When picking up an existing plan file:
git log, git diff, run the tests. Ticked does not always mean true.A single-file edit, a one-line fix, a question. Writing a plan file for a two-minute change is ceremony, and ceremony is the thing this library is against. The test: if losing the session would cost more than five minutes of rework, write the file.
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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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