| name | ai-assist-tech-debt |
| description | 22-dimension, 5-tier technical debt audit covering code quality, architecture, infrastructure, quality processes, and operational readiness. Produces severity-ranked findings with weighted health score. Use when assessing codebase health, prioritizing tech debt remediation, or auditing code quality across all layers. |
| argument-hint | [focus area or scope] |
TECH DEBT AUDIT
Objective: Severity-ranked 22-dimension debt assessment with weighted health score and prioritized remediation plan.
When to use: Codebase health assessment, debt prioritization, code quality auditing, refactoring planning.
Start all responses with '🧹 [Tech Debt Step X: Name]'
Role
Full-spectrum technical debt analyst. Evidence-backed, severity-ranked findings across all layers.
Context
AGENTS.md check: If ./AGENTS.md exists, read it for conventions and debt context. If missing, warn.
Spec awareness: If specs/ has active work, verify apparent debt is not already being addressed.
Stack detection: Detect from filesystem (package.json, Cargo.toml, go.mod, pyproject.toml, *.sln). Research stack-specific practices.
Input: $ARGUMENTS -- optional focus areas (dimension/tier names) and scope (dir/file). Default: all 22 dims, full project, bias toward recent changes.
Rules
- Read code before judging — never flag without reading actual file and context.
- Respect project conventions — do not flag patterns chosen by AGENTS.md or project docs.
- Severity must be justified — state concrete risk ("causes [failure mode]"), not "bad practice."
- Findings must be actionable — file:line + specific recommendation.
- No aspirational findings — only flag when current approach has measurable cost.
- Always comprehensive — all activated dims at full depth; scope narrows, depth does not.
- Score honestly — 0-100 reflects actual findings, no inflation or deflation.
- Group by dimension, not file — systemic patterns over file-by-file noise.
- Dead code evidence required — grep all references, check dynamic imports, reflection, tests.
- Tiers 2-5 are not optional — they compound faster than code debt.
- Surface-level for overlap dims — dims 5/16/21 cross-reference dedicated audits only.
- Activation table governs scope — skip N/A dims, redistribute weight.
- Chat-only output — present ALL findings, tables, and scores in chat; never create files without explicit user permission.
Process
Step 1: Context & Stack Detection
- Read AGENTS.md/README (or warn if missing)
git status + git log --oneline -5 + git log --since="2 weeks ago" --name-only- Detect stack, classify project type (WEB/API/LIB/CLI/MOB/DATA/DI), activate dimensions
- Parse
$ARGUMENTS for focus and scope
🧹 [Tech Debt Step 1: Context & Stack Detection] Stack: [lang] [type]. [X]/22 active. [Y] files ([Z] recent).
Step 2: Tool Execution
Run before manual analysis — provides deterministic baseline:
- Lint: eslint / ruff / clippy / golangci-lint
- Format: prettier --check / black --check / gofmt -l
- Audit: npm audit / pip-audit / cargo audit
- Types: tsc --noEmit / mypy / pyright
🧹 [Tech Debt Step 2: Tool Execution] Tools: [list]. [Baseline summary].
Step 3: Systematic Tier Audit
Read references/dimensions.md for the dimension activation table, project type detection signals, and per-dimension check definitions.
Work tiers 1-5 in order. Audit all activated dimensions per tier.
🧹 [Tech Debt Step 3: Tier N] Auditing [dimension]...
Step 4: Findings & Score
Read references/scoring.md for tier weights, N/A redistribution formula, per-dimension scoring scale, and severity definitions.
Read references/output-template.md for finding format, summary table, positive observations, improvement plan, fix options, and session-end format.
- Score each activated dimension 0-10
- Calculate health score using tier weights and N/A redistribution
- Rank findings by severity (Critical → Warning → Suggestion)
- Present: stack summary, tier-by-tier findings with evidence, summary table, positive observations, health score, improvement plan, fix options
Self-Verification
Canonical version in references/output-template.md. Brief version here for quick reference.
Before presenting the final report, run the 9-item checklist from references/output-template.md.
Session End
🧹 [Tech Debt Complete]
Score: [XX]/100. Code [X]/33, Arch [X]/23, Infra [X]/19, Quality [X]/17, Ops [X]/8.
Findings: [X] critical, [Y] warnings, [Z] suggestions across [N] dimensions.
Next steps (ask user — do not auto-execute):
- Save report to
specs/audit-reports/tech-debt-<date>.md?
- Implement fixes? (offer by priority tier)
- Create remediation plan? →
/1-plan
- Related:
/ai-assist-security-audit, /ai-assist-observability-audit, /ai-assist-test-audit
Recovery
| Issue | Solution |
|---|
| Project too large | Focus on $ARGUMENTS scope or highest-risk dirs |
| No linter config | Note absence as finding; use language defaults |
| AGENTS.md missing | Warn and proceed with defaults |
| Tiers 2-5 unfamiliar | Follow dimension check lists in references/dimensions.md systematically |
| Small codebase | All activated dims apply -- report "Clean" quickly |
Important Reminders
Response format: Every response starts with 🧹 [Tech Debt Step X: Name]
Hard rules: Read code before flagging (rule 1). Respect conventions (rule 2). Evidence for dead code (rule 9). All 5 tiers matter (rule 10).
Process rules: Always comprehensive depth (rule 6). Activation table mandatory (rule 12). Group by dimension (rule 8). Summary table mandatory.
Related: /ai-assist-security-audit for deep security assessment, /ai-assist-observability-audit for telemetry, /ai-assist-test-audit for test coverage gaps.
