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AB technical-debt-register

Document and prioritize a technical debt backlog with business impact, effort estimates, and resolution strategy. Use when asked to audit technical debt, create a debt register, prioritize tech debt for a quarter, document architectural shortcuts, or build a debt reduction roadmap. Produces a structured technical debt register covering debt inventory by category, business impact per item, effort and priority scores, top-item resolution plans, and a quarterly debt reduction roadmap.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 4 files body ≈ 3 314 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Document and prioritize a technical debt backlog with business impact, effort estimates, and resolution strategy.

As a process B 78/100 · Nearly there — weak spots: failures and branches, running it twice

AnalyzerOperations 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
78/100
Nearly there
Failures and branches w 10
0
Running it twice w 4
30
When it triggers w 12
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: technical-debt-register (mohitagw15856/pm-claude-skills)

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 12): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 78/100

    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 45 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3314 tokens
    • 100Progress reporting. Reports progress
    • low 13 top-level sections: this looks like several domains in one skill

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 486: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 45 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.