Parallel agent fleet dispatch with progress tracking and result aggregation. Launch N agents, monitor completion, merge results. Proven at 7-agent scale.
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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。
Activates when a task requires launching multiple agents in parallel to cover different aspects of the same work.
1. DECOMPOSE work into N independent units
2. LAUNCH agents in a single message (parallel tool calls)
3. Use run_in_background: true for all agents
4. MONITOR — agents report back via task notifications
5. AGGREGATE results as agents complete
6. COMMIT incrementally — don't wait for all to finish
| Work Type | Recommended Fleet | Agent Type |
|---|---|---|
| Research (broad topic) | 3-4 agents | general-purpose or gsd-phase-researcher |
| Document expansion | 2-4 agents (split by doc range) | gsd-executor or document-builder |
| Fact-checking | 2 agents (split by doc range) | fact-checker |
| Data gathering | 3 agents (by data domain) | market-researcher |
Agents in worktrees write to isolated copies. After completion:
cp worktree-path/output/file.md main-repo/output/file.md
Then commit from the main repo branch.
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Provides web accessibility best practices for semantic HTML, ARIA, keyboard navigation, color contrast, and screen reader patterns. Use when building UI components, reviewing accessibility, or when user mentions 'a11y', 'accessibility', 'ARIA', 'screen reader', 'keyboard navigation', 'WCAG'.
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Active listening techniques for effective communication. Covers attending behaviors, paraphrasing, reflective listening, clarifying questions, empathic response, barriers to listening, listening in conflict, and cross-cultural listening. Use when building listening skills, improving understanding in conversation, mediating disputes, or analyzing communication breakdowns.
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Adversarial spec-compliance PR review — cross-references diffs against approved specs, verifies runtime claims against source, detects competing PRs, audits scope/convention compliance. Use before merging.
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Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.
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Symbolic manipulation, equation solving, and algebraic structures for mathematical reasoning. Covers distributive law, factoring, completing the square, linear through polynomial equation solving, systems of equations (substitution, elimination, Gaussian elimination, matrix methods), algebraic structures (groups, rings, fields), modular arithmetic, polynomial theory, and inequalities. Use when solving equations, simplifying expressions, working with algebraic structures, or performing symbolic manipulation.
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Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.
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