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Tech Debt Audit Protocol · code-yeongyu | DecimalAI

code reviewby code-yeongyu

Tech Debt Audit Protocol

code-yeongyu/tech-debt-audit

Source code-yeongyu/oh-my-openagent ↗License NOASSERTION

Thorough, file-cited technical debt audit across 9 dimensions using AST-grep (tree-sitter), grep, language-native tooling, and optionally CodeGraph knowledge graph. Produces TECH_DEBT_AUDIT.md with severity, effort estimates, and prioritized fixes.

SkillScore75Maintained

Automatic

Coding pack

Last verified 8/7/2026

What it changes

Passed 14 of 19 test cases with the skill, and 7 of 19 without it.

Without skill38%

With skill75%

Checks it passed that the model alone missed

The output specifies effort must be estimated in hours.
The output states sub-agents cannot use graph search tools.
The output orders top 5 priorities by impact to effort ratio.

and 10 more in the full report

11 fixed7 passed either way4 still failing2 broke

Pass-rate lift: +38 pp
Tokens: +137%
Agent turns: 0%
Response time: -57%

Model gemini-3.6-flashMeasured 8/7/2026v118 of 24 cases passed with skill · 19 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/code-yeongyu-tech-debt-audit/SKILL.md https://app.decimal.ai/s/code-yeongyu-tech-debt-audit/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add code-yeongyu-tech-debt-audit, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/code-yeongyu-tech-debt-audit/ and pins it in .decimal/skills.lock

decimalai skills add code-yeongyu-tech-debt-audit

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/code-yeongyu-tech-debt-audit/SKILL.md https://app.decimal.ai/s/code-yeongyu-tech-debt-audit/SKILL.md

Raw file: /s/code-yeongyu-tech-debt-audit/SKILL.md ↗ · machine-readable summary at /s/code-yeongyu-tech-debt-audit.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 11

Prompt

In Phase 0 orientation, before full dimension auditing, what file size line count threshold is used to identify initial candidates for debt hot zones?

What was checked, without and with the skill
Checked	Without skill	With skill
The output identifies files exceeding 300 lines of code as orientation candidates.	Missed	Passed

case-19 · gemini-3.6-flash · 8/7/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md272 linessingle file

Tech Debt Audit Protocol

Model-agnostic technical debt audit for oh-my-openagent (OMO). Uses OMO's built-in tools (grep, glob, bash with sg, read, lsp_diagnostics, task) plus optional CodeGraph MCP for enhanced code graph analysis when available. Produces a grounded, citable TECH_DEBT_AUDIT.md artifact.

CodeGraph Enhancement (Optional)

If you have CodeGraph installed (check with codegraph status), its MCP tools (codegraph_search, codegraph_callers, codegraph_callees, codegraph_impact, codegraph_explore, etc.) can supersede or augment the standard tool searches in the dimensions marked below. CodeGraph gives you:

Symbol search — instant by-name lookup via FTS5
Call graph analysis — callers/callees for any function
Impact analysis — blast radius before changing any symbol
Smart context building — entry points, related symbols, and snippets in one call
Framework-aware routes — URL patterns linked to their handlers

To use CodeGraph, ensure the codegraph MCP server is configured in your project's .mcp.json or global MCP config. The skill will auto-detect CodeGraph by checking if codegraph MCP tools are available. Sub-agents spawned via task() cannot use CodeGraph — they use the standard tool fallback.

Output

Write results to TECH_DEBT_AUDIT.md in the repo root with:

Executive Summary — 3-5 sentences: overall health, worst dimension, quick wins count
Mental Model — the repo's architecture in 1 paragraph (what it does, stack, module boundaries)
Findings Table — columns: ID, Category, File:Line, Severity (Critical/High/Medium/Low), Effort (Hours), Description, Recommendation
Top 5 Priorities — ranked by impact/effort ratio
Quick Wins Checklist — items under 30 minutes each
"Looks Bad But Is Fine" — patterns that look like debt but are intentional
Open Questions — things the maintainer should clarify

Phase 0: Orient

Standard (always run)

glob("**/*.ts") / glob("**/*.py") / etc — map the language stack
glob("**/package.json") + read() — dependencies and build tooling
bash("git log --oneline -200") — churn: find highest-change files
glob("**/*") + basic math — find largest files (>300 LOC are candidates)
Cross-reference high-churn + large = debt hot zones
Write the mental model paragraph in your own working context

CodeGraph Enhancement (if available)

Track record

Benchmark

14 / 19

test cases passed, vs 7 / 19 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

code-yeongyu / oh-my-openagent

.agents/skills/tech-debt-audit/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Tech Debt Audit Protocol

CodeGraph Enhancement (Optional)

Output

Phase 0: Orient

Standard (always run)

CodeGraph Enhancement (if available)

Track record

Benchmark

Adoption

SkillSafety

Source

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