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tech-debt-tracker

Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, refactoring priority, debt scoring, cleanup sprints, or code health assessment. Also use for legacy code modernization planning and maintenance cost estimation.

69

Quality

84%

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The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Well-structured body with executable commands, a clear multi-step workflow, and an explicit validation loop backed by real bundle files. The main weakness is conciseness: the Overview and Common Pitfalls sections explain concepts and advice Claude largely already knows.

Suggestions

Trim the Overview's explanation of what tech debt is ('compounds over time', 'accrues interest') — Claude already knows this; keep only the skill-specific framing.

Replace or condense the five 'Common Pitfalls' generic problem/solution pairs with a concise checklist, or move them to a references file if needed at all.

Add a brief inline list of the most-used scanner and prioritizer flags so the Quick Start covers common cases without forcing a script read.

DimensionReasoningScore

Conciseness

Mostly efficient Quick Start, but the Overview explains tech-debt basics Claude already knows and the 'Common Pitfalls' section is generic advice that could be trimmed.

3 / 5

Actionability

Three concrete, executable bash commands with real flags and example values, plus described JSON output fields; minor gaps in covering all scanner/prioritizer options.

4 / 5

Workflow Clarity

Clear scan -> prioritize -> dashboard sequence with numbered commands and an explicit 'Verification loop' that asserts debt counts dropped — a genuine validation feedback loop.

5 / 5

Progressive Disclosure

Clear overview with well-signaled one-level-deep references; verified references/ files, scripts, and sample assets all exist and the detail (classification, prioritization, templates) is appropriately offloaded.

5 / 5

Total

17

/

20

Passed

Description

91%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description with explicit 'what' and 'when' clauses and rich natural trigger terms. Minor overlap risk on a couple of broad quality terms keeps it just below maximum on distinctiveness.

DimensionReasoningScore

Specificity

Names four concrete actions ('Scan codebases', 'score severity', 'track trends', 'generate prioritized remediation plans'); strong coverage but slightly short of the comprehensive breadth in the 5 anchor.

4 / 5

Completeness

Explicitly answers both what ('Scan codebases for technical debt...') and when ('Use when users mention tech debt...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural keyword coverage including synonyms ('tech debt', 'code quality', 'refactoring priority', 'debt scoring', 'cleanup sprints', 'code health assessment', 'legacy code modernization') that users would naturally say.

5 / 5

Distinctiveness Conflict Risk

Clear tech-debt niche with distinct triggers; minor overlap risk from broad terms like 'code quality' / 'code health' that could fire for generic code-quality skills.

4 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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