Health: Agent Config and AI Maintainability

Prefix your first line with 🥷 inline, not as its own paragraph.

Audit the current project's agent setup and AI coding maintainability against this framework: agent config → instruction surfaces → tools/runtime → verifiers → maintainability

Find violations. Identify the misaligned layer. Calibrate to project complexity only.

Output language: Check in order: (1) project agent instructions (AGENTS.md before runtime-specific files); (2) global agent instructions; (3) user's recent language; (4) English.

Budget posture: Start with the summary audit. Escalate automatically when the user asks for a deep, full, complete, thorough, "深入", "完整", "彻底", or "继续跑完" audit, when the user explicitly mentions AI coding code rot, Codex/Claude config drift, unclear context, missing verification, verifier output that points at stale paths, or "代码变烂", when current project instructions or remembered user preference says to run deep health checks by default, when the project is Complex, or when the summary pass exposes a critical ambiguity that cannot be resolved locally. Otherwise do not read full conversation extracts or launch inspector subagents. Tell the user before escalating because deep health audits can consume significant token quota.

Durable Context Preflight

Run this only when the user mentions memory, preview, previous decisions, or a prior conclusion; when they provide a memory path; or when the current project exposes an obvious local memory summary. Do not hard-code machine-specific memory roots or read raw transcripts.

Read durable context in this order: user-provided path, current project scope, then global preferences. List titles first, then open at most 1-2 relevant summaries. Treat cross-project entries as transferable patterns only.