Use when GSD commands are failing or .planning/ artifacts look inconsistent. Run before any GSD execute-phase or verify-work attempt to catch cross-artifact mismatches early.
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See how to installReview the instructions the agent will receive before you install.
Pre-flight checks on .planning/ artifacts to catch issues before workflows fail.
PROJECT.md — Sections: What This Is, Requirements, Constraints, Key Decisions ROADMAP.md — Sequential phase numbers, each with Goal/Deliverables/Success Criteria STATE.md — Current Position matches ROADMAP status, reasonable progress % config.json — Valid JSON, valid enum values (mode, model_profile, depth)
| Level | Definition | Action |
|---|---|---|
| BLOCKER | Will cause workflow failure | Must fix first |
| WARNING | May cause confusion | Should fix |
| INFO | Informational | No action needed |
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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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