scan-codebase-health

name	scan-codebase-health
description	[Documentation] Use when you need to detect codebase health issues: unused exports, doc count-drift, orphan files, stale config references.

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex uses static project-reference loading instead of runtime-injected project docs. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Missing/stale context route: If docs/project-config.json, the docs index, lessons.md, CLAUDE.md, AGENTS.md, or any task-required reference doc is missing or stale, auto-run $project-init or the narrow setup route ($project-config, $docs-init, $scan-all, $scan --target=<key>, $claude-md-init) before ordinary project-specific work. If Codex mirrors or AGENTS.md are missing/stale, ask the user to run $sync-codex; do not auto-run it.

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec authoring, docs/specs/ pathing, or TC format: feature-spec-reference.md, spec-system-reference.md, spec-principles.md
• Behavior/public-contract changes or spec-test-code sync: workflow-spec-test-code-cycle-reference.md plus the spec docs above
• Derived spec indexes/ERDs/reimplementation guides: spec-system-reference.md and source Feature Specs under docs/specs/
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

Quick Summary

Goal: Detect structural rot in AI-assisted codebases — dead code, count-drift, orphan files, stale configs, dead feature flags, broken cross-references. Works on any project via docs/project-config.json.

Workflow:

1. Classify — Load config, detect available tooling (graph.db, CI, feature-flag patterns)
2. Run Detections — Execute 7 detection categories (graph-dependent checks skipped if no graph.db)
3. Fresh-Eyes Review — Verify findings before writing report
4. Generate Report — Write to plans/reports/codebase-health-scan-{YYMMDD}.md
5. Present Summary — Show actionable findings with severity levels

Key Rules:

• Generic — reads all paths from project-config.json, never hardcodes project names
• Graceful degradation — graph-dependent checks skipped if .code-graph/graph.db not found
• Report format — each finding has file:line, category, severity (HIGH/MEDIUM/LOW), suggested action MUST ATTENTION NEVER report a finding without file:line proof

---

Scan Codebase Health

Phase 0: Classify & Detect

Before any other step, in parallel:

1. Read docs/project-config.json for the codebaseHealth section:

{
    "codebaseHealth": {
        "sourcePaths": ["{discovered-source-root}/"],
        "docPaths": ["docs/"],
        "configPatterns": ["**/appsettings*.json", "**/environment*.ts"],
        "excludePaths": ["node_modules", "dist", "bin", "obj"]
    }
}

If codebaseHealth section is missing, discover source roots from project config, manifests, and populated code directories; use docPaths: ["docs/"] when docs exist.

2. Detect available tooling to determine which phases to run:

Signal	Phase Enabled
.code-graph/graph.db exists	Phase 3 (Unused Exports) + Phase 4 (Orphan Files)
CI config found (.github/workflows, azure-pipelines.yml)	Phase 6 (CI Health) — optional
Feature flag patterns found (FeatureFlags, IFeatureManager, LaunchDarkly)	Phase 6 (Dead Feature Flags)
Cross-reference patterns in docs (file:line, [link]())	Phase 7 (Broken Cross-References)

3. Create task tracking entries for each enabled phase before proceeding.

Evidence gate: If docs/project-config.json not found and no detectable source paths, report and ask user for guidance. DO NOT guess project structure.

Phase 1: Doc Count-Drift Detection (No Graph Required)

Think: Which numeric claims in docs can actually be verified? What's the drift threshold that signals a real maintenance problem vs normal growth?

Scan docs/ for numeric claims: "N files", "N tests", "N hooks", "N services", "N skills", "N components". For each claim:

1. Extract number and what it counts
2. Glob/grep to verify actual count
3. Flag if actual differs from claimed

Severity thresholds:

• Drift ≤10% → LOW (normal growth)
• Drift >10% and ≤30% → MEDIUM (needs update)
• Drift >30% → HIGH (significantly stale)
• Claim cannot be verified → MEDIUM (ambiguous claim)

Write findings incrementally to report after each doc scanned. NEVER batch at end.

Phase 2: Stale Config Reference Detection (No Graph Required)

Think: Which config values reference code artifacts (class names, module names, connection strings)? Could those artifacts have been renamed or deleted?

For each file matching configPatterns:

1. Extract class names, module names, or connection strings referenced
2. Grep codebase to verify each reference still exists
3. Flag missing references as HIGH severity

Evidence gate: NEVER flag a reference as stale without attempting grep. Confidence <80% → flag as MEDIUM "unverified" only.

Phase 3: Unused Exports Detection (Graph Required)

Skip if .code-graph/graph.db does not exist — log "Phase 3 skipped: no graph.db".

Think: Which public API surface has zero consumers? Could be dead code, or could be an intentional entry point — distinguish by file type.

For key exported symbols in source files:

1. Run python .claude/scripts/code_graph query importers_of <symbol> --json
2. Flag symbols with zero importers as MEDIUM severity
3. Exclude known entry points (main files, test files, config files, startup files)

Phase 4: Orphan File Detection (Graph Required)

Skip if .code-graph/graph.db does not exist — log "Phase 4 skipped: no graph.db".

Find source files (.ts,.cs,.py, etc.) with zero inbound edges:

1. Run python .claude/scripts/code_graph query importers_of <file> --json
2. Flag files with zero importers as LOW severity (may be entry points)
3. Exclude known entry points

Phase 5: Pattern Drift Detection (No Graph Required)

Think: Where does the same pattern appear across services/modules? Does it look different in different places? Is that divergence intentional or accidental?

Compare the same pattern across services/modules:

1. Pick a pattern (e.g., repository registration, service configuration, error handling)
2. Grep across all services/modules
3. Flag inconsistencies as MEDIUM severity

Phase 6: Dead Feature Flag Detection (If Feature Flags Detected)

Skip if no feature flag patterns found in Phase 0.

Think: Which flags exist in config but have no code references? Which code references flags that no longer exist in config?

1. Grep for feature flag names in config files
2. Grep for feature flag usage in code
3. Flag config-only flags (no code usage) as LOW
4. Flag code-only flags (no config entry) as HIGH (runtime error risk)

Phase 7: Broken Cross-Reference Detection (No Graph Required)

Think: Which doc links point to files that no longer exist? Which file:line references in docs are stale?

For docs containing markdown links [text](path) or file:line references:

1. Extract all file path references
2. Glob to verify each path exists
3. Flag missing paths as MEDIUM severity

Phase 8: Fresh-Eyes Review

Before writing final report, spawn a fresh sub-agent (zero memory) to:

• Sample 5-10 findings from the report
• Verify each has a real file:line evidence source
• Check: is the severity classification justified by the description?
• Flag false positives (things flagged but actually acceptable)

Max 2 rounds → escalate to user if review finds >30% false positive rate.

Phase 9: Generate Report

Write to plans/reports/codebase-health-scan-{YYMMDD}.md:

# Codebase Health Scan Report

**Date:** {YYYY-MM-DD}
**Phases Completed:** {N}/{total} ({reason for skipped phases})
**Findings:** {total} ({HIGH} high, {MEDIUM} medium, {LOW} low)

## Summary

| Phase                   | Status                              | Findings   |
| ----------------------- | ----------------------------------- | ---------- |
| Doc Count-Drift         | Scanned                             | N findings |
| Stale Config Refs       | Scanned                             | N findings |
| Unused Exports          | Scanned/Skipped (no graph.db)       | N findings |
| Orphan Files            | Scanned/Skipped (no graph.db)       | N findings |
| Pattern Drift           | Scanned                             | N findings |
| Dead Feature Flags      | Scanned/Skipped (no flags detected) | N findings |
| Broken Cross-References | Scanned                             | N findings |

## Findings

### HIGH Severity

- `{file}:{line}`: {description} — Action: {action}

### MEDIUM Severity

- `{file}:{line}`: {description} — Action: {action}

### LOW Severity

- `{file}:{line}`: {description} — Action: {action}

## False Positives (Fresh-Eyes Review)

{Findings dismissed by Round 2 review with reasoning}

---

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

Output Quality — Token efficiency without sacrificing quality.

1. No inventories/counts — AI can grep | wc -l. Counts go stale instantly
2. No directory trees — AI can glob/ls. Use 1-line path conventions
3. No TOCs — AI reads linearly. TOC wastes tokens
4. No examples that repeat what rules say — one example only if non-obvious
5. Lead with answer, not reasoning. Skip filler words and preamble
6. Sacrifice grammar for concision in reports
7. Unresolved questions at end, if any

AI Mistake Prevention — Failure modes to avoid on every task:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

IMPORTANT MUST ATTENTION output quality: no counts/trees/TOCs, 1 example per pattern, lead with answer.

MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

Closing Reminders

IMPORTANT MUST ATTENTION break work into small task tracking tasks BEFORE starting — one per phase

MUST ATTENTION — Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• Critical Thinking: apply critical+sequential thinking; traced file:line proof, >80% to act.
• Output Quality: no counts/trees/TOCs; 1 example per pattern; lead with answer.
• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.

IMPORTANT MUST ATTENTION detect available tooling in Phase 0 — never assume graph.db exists IMPORTANT MUST ATTENTION NEVER report a finding without file:line evidence IMPORTANT MUST ATTENTION write findings incrementally after each phase — NEVER batch at end IMPORTANT MUST ATTENTION severity thresholds are concrete: HIGH = runtime failure risk; MEDIUM = drift/dead code; LOW = cleanup candidate IMPORTANT MUST ATTENTION Phase 8 fresh-eyes review is mandatory — prevents false positives from rationalization

Anti-Rationalization:

Evasion	Rebuttal
"Graph not needed, skip Phases 3-4"	Phases 3-4 are explicitly gated — state skip reason in report, don't silently omit
"Count drift is small, LOW severity is fine"	Apply the threshold table: >10% = MEDIUM, >30% = HIGH. No discretionary override.
"Finding looks valid, skip Round 2 review"	Main agent rationalizes own findings. Fresh-eyes is non-negotiable.
"No feature flags found, skip Phase 6"	Log "Phase 6 skipped: no feature flag patterns detected" in report
"Config reference might still exist"	Grep to verify. Confidence <80% → flag as MEDIUM "unverified" not LOW "probably fine"

[TASK-PLANNING] Before acting, analyze task scope and break into small todo tasks and sub-tasks using task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/.ck.json + .claude/skills/shared/sync-inline-versions.md (:full blocks) + .claude/scripts/lib/hookless-prompt-protocol.cjs

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. For spec, test-case, behavior-change, public-contract, or docs/specs/ work, route through the local spec docs named by the docs index: feature-spec-reference.md, spec-system-reference.md, spec-principles.md, and workflow-spec-test-code-cycle-reference.md when specs/tests/code must stay synchronized. If either file or a required reference doc is missing or stale, auto-run $project-init (or the narrow lower-level route such as $project-config, $docs-init, $scan-all, or $scan --target=<key>) before ordinary project-specific work. Any supported AI tool may execute when this shared context and local docs are available.

1. DETECT: If the prompt starts with an explicit slash skill/workflow command, execute it directly. Otherwise match the prompt against the workflow catalog and skill list.
2. ANALYZE: Choose the best option: execute directly, invoke a skill, activate a standard workflow, or compose a custom step combination.
3. AUTO-SELECT: Pick the best option yourself. Do not ask the user to choose between direct execution, skill, standard workflow, or custom workflow.
4. ACTIVATE: For a selected workflow, call $start-workflow <workflowId>; for a selected skill, invoke that skill; for a custom workflow, sequence custom steps directly; for direct execution, proceed with the task.
5. CREATE TASKS: task tracking for ALL workflow/skill/custom steps before execution when the selected path has multiple steps.
6. EXECUTE: Advance per the Workflow Step Advancement & Parallel Phases rule in your context instructions — model-driven; a sub-agent completion advances a step identically to an inline call; a parallel-phase group is an all-return barrier (advance only after ALL members return, never serialize it)

Shared AI-SDD Protocol Markers

Source: .claude/skills/shared/sync-inline-versions.md

SYNC:ai-sdd-artifact-contract

AI-SDD Artifact Contract — Shared spec-driven development rules stay portable and source-owned.

1. Keep reusable AI-SDD principles in .claude; put repository-specific paths, commands, owners, products, and formats in project config/reference docs.
2. Preserve cycle: spec -> plan -> tasks -> implement -> verify -> update spec/docs.
3. Trace every requirement or invariant through decision, task, TC/test, source evidence, and docs/spec update.
4. Treat code-to-spec extraction as reference-only until accepted by the canonical spec owner.
5. Any supported AI tool may plan, implement, review, or verify with synced context; using multiple tools is optional.
6. Update .claude source first, then sync generated mirrors; do not manually edit .agents, .codex, or AGENTS.md. — why: mirrors are generated artifacts; hand-edits are overwritten on the next sync
7. If docs/project-config.json, root instruction files, or a required project-reference doc is missing or stale, auto-run $project-init or the narrow lower-level route before ordinary project-specific work.

Active reference: shared/sdd-artifact-contract.md in the active skills root.

---

SYNC:ai-sdd-artifact-contract:reminder

• MANDATORY Apply shared/sdd-artifact-contract.md; keep reusable AI-SDD in .claude and local rules in project docs.
• MANDATORY Code-to-spec extraction is reference-only until canonical acceptance; any supported AI tool may execute with synced context.
• MANDATORY Update .claude source before syncing generated mirrors; do not manually edit .agents, .codex, or AGENTS.md.
• MANDATORY Missing or stale project config, root instruction files, or required reference docs route project-specific work through $project-init or the narrow setup route automatically. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security-review/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

Common AI Mistake Prevention (System Lessons)

• Re-read files after context compaction. Edit requires prior Read in same context; compaction wipes read state. Re-read before editing.
• Grep for old terms after bulk replacements. AI over-trusts find/replace completeness. Grep full repo after bulk edits for missed refs in docs/configs/catalogs.
• Check downstream references before deleting. Deletions cascade doc/code staleness. Map referencing files before removal.
• After memory loss, check existing state before creating new. Compaction wipes prior-work memory. Query current state to resume — never blindly duplicate.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, method signatures. Grep to confirm existence before documenting/referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream consumers. Trace the full chain.
• When renaming, grep ALL consumer file types. Some file types silently ignore missing refs (no compile error). Search code, templates, configs, generated files.
• Trace ALL code paths when verifying correctness. Code existing ≠ code executing. Trace early exits, error branches, conditional skips — not just happy path.
• Update docs that embed canonical data when source changes. Docs inlining derived data (workflows, schemas, configs) go stale silently. Update all embedding docs alongside source.
• Verify sub-agent results after context recovery. Background agents may finish while parent compacted — grep-verify output, don't trust assumed completion.
• Cross-check full target list against sub-agent assignments. Parallel sub-agents by category miss boundary items. Reconcile union of assignments against target list before proceeding.
• Sub-agents inherit knowledge only from their agent .md definition — use custom agent types, not built-in Explore. Tool adoption = permission + knowledge + enforcement (numbered workflow step).
• Persist sub-agent findings incrementally, not as a final batch. Long sub-agents hit cutoffs before final write — findings lost. Instruct append-per-section to report file.
• When debugging, ask "whose responsibility?" before fixing. Trace caller (wrong data) vs callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Grep ALL removed names after extraction/refactoring. Primary file "done" ≠ secondary files clean. Grep entire scope for every removed symbol before declaring complete.
• Assume existing values are intentional — ask WHY before changing. Pattern-matching as "wrong" skips context. Before changing any constant/limit/flag: read comments, git blame, surrounding code.