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BD tech-debt-tracker

Scan codebases for technical debt with AST parsing, prioritize by impact, and generate trend dashboards. Use when tracking tech debt, prioritizing refactoring, calculating cost-of- delay, planning sprint debt, or reporting debt to execs.

borghei/Claude-Skills Agent Skills author: borghei 21 files · 6 scripts body ≈ 2 085 tokens Open the sourcegithub.com↗ analyzed 32 h ago

Scan codebases for technical debt with AST parsing, prioritize by impact, and generate trend dashboards.

As a process D 40/100 · Unfinished process — References files that are not bundled: references/*.md

GeneratorSoftware developmentData and analyticsOperations and projectstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
98
Quality 40%
86
Run on models
none yet
Process rating
D
40/100
Unfinished process
References files that are not bundled: references/*.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-password-literal assets/sample_codebase/src/frontend.js:5
    Hard-coded password / key literal (may be an example)
    const API_KEY = "abc1…456"; // FIXME: Should be in environment
  • low Secrets in code secret-password-literal assets/sample_codebase/src/user_service.py:14
    Hard-coded password / key literal (may be an example) (placeholder value)
    API_KEY = "sk-1…def"  # FIXME: This should be in environment variables
    placeholder

Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/*.md

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: references/*.md
  • 0Tools and files. 1 referenced file(s) missing: references/*.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 100Steps. 25 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2085 tokens
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 237: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)
  • +3All 3 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.