Project state awareness and session recovery for GSD-managed projects. Use this skill whenever: starting a new work session, resuming after a break, the user asks "where did we leave off" or "what's the current state", before making substantial code changes (to check for existing plans), the user seems disoriented about project status, or when something seems off and STATE.md might have relevant context. Also activates when the user references .planning/ files, milestones, phases, or project progress.
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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。
| File | Purpose | When to Read |
|---|---|---|
.planning/PROJECT.md | Vision, constraints, decisions | Understanding project context |
.planning/REQUIREMENTS.md | What we're building (REQ-IDs) | Scoping work |
.planning/ROADMAP.md | Phase structure and status | Finding current position |
.planning/STATE.md | Session memory, blockers, decisions | Always on resume |
.planning/config.json | Workflow preferences | Checking mode (yolo/interactive) |
.planning/phases/XX-name/XX-YY-PLAN.md | Detailed task plans | Executing work |
.planning/phases/XX-name/XX-YY-SUMMARY.md | What was built | Reviewing completed work |
| File | Purpose | When to Read |
|---|---|---|
.planning/patterns/sessions.jsonl | Session observations | Pattern detection input |
.claude/commands/*.md | Project-level skills | Before GSD phase execution |
.claude/agents/*.md | Composed agents | Agent selection |
~/.claude/commands/*.md | User-level skills | Fallback when no project skill matches |
Let me check the current project state...
[Read ROADMAP.md, STATE.md]
This falls under Phase X. There's already a plan at `.planning/phases/...`.
Should I execute that plan, or are you looking to do something different?
/gsd:progress (or) /gsd:resume-work
This project doesn't have GSD initialized yet. Want me to run
`/gsd:new-project` to set up the planning structure?
Heads up — this would modify files that Phase N is planning to create.
Options:
1. Execute Phase N first (recommended)
2. Update the Phase N plan to account for this
3. Proceed anyway and reconcile later
Always check STATE.md when resuming or when something seems off — it is the project's memory.
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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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