codebase-cleanup-tech-debt

You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti

Quality

61%

Does it follow best practices?

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tessl review fix ./skills/codebase-cleanup-tech-debt/SKILL.md

Quality

Content

65%

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

The body is a well-structured, highly actionable framework with concrete thresholds and runnable patterns, but it is verbose and monolithic with no external references or validation checkpoints, limiting conciseness, workflow clarity, and progressive disclosure.

Suggestions

Move detailed metric templates and refactoring code examples into reference files (e.g., METRICS.md, REFACTORING-PATTERNS.md) and link to them from a leaner overview to improve progressive disclosure and conciseness.

Trim definitions of concepts Claude already knows (e.g., what cyclomatic complexity or god classes are) and keep only the thresholds and quantification metrics.

Add an explicit validation/review checkpoint in the workflow (e.g., verify metrics against baseline before finalizing the remediation plan) to raise workflow clarity.

Dimension	Reasoning	Score
Conciseness	The body is long and partly explains concepts Claude already knows (e.g., defining god classes and cyclomatic complexity thresholds) plus extensive illustrative templates, fitting the score-2 anchor of mostly efficient but could be tightened.	2 / 3
Actionability	It provides concrete, executable guidance — specific thresholds (>10 complexity, >50 line methods, >500 line god classes), runnable Python/YAML refactoring patterns (PaymentFacade/strangler fig), and ROI/cost formulas — matching the score-3 anchor of specific, copy-paste-ready examples.	3 / 3
Workflow Clarity	The eight numbered sections (Inventory -> Impact -> Metrics -> Plan -> Implementation -> Prevention -> Communication -> Success Metrics) form a clear sequence, but there are no validation checkpoints or feedback loops, fitting the score-2 anchor of steps present but checkpoints missing.	2 / 3
Progressive Disclosure	The content is well-organized into sections but is a single monolithic file with no bundle/reference files, and detailed templates and code examples that could be split out are inline, matching the score-2 anchor.	2 / 3
	Total	9 / 12 Passed

Description

57%

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

The description clearly conveys a well-scoped niche (technical debt analysis) with concrete actions, but it is written in second person and lacks an explicit 'Use when' trigger clause, capping completeness and trigger quality at 2.

Suggestions

Add an explicit 'Use when...' clause naming natural user phrases (e.g., 'Use when the user asks about technical debt, code cleanup, refactoring priorities, or legacy code quality') to raise completeness and trigger_term_quality.

Rewrite in third person voice ('Identifies, quantifies, and prioritizes technical debt...') instead of second person ('You are a technical debt expert') to avoid the specificity penalty.

Broaden trigger keywords to include common variations such as 'refactoring', 'code quality', 'legacy code', and 'codebase cleanup'.

Dimension	Reasoning	Score
Specificity	It lists multiple concrete actions ('identifying, quantifying, and prioritizing', 'Analyze the codebase to uncover debt, assess its impact, and create actionable remediation plans'), which fits the score-3 anchor, but it uses second person ('You are a technical debt expert') which the guidelines penalize by reducing specificity by 1.	2 / 3
Completeness	It clearly states what the skill does but provides no 'Use when...' clause or equivalent explicit trigger guidance, which the guidelines cap at 2.	2 / 3
Trigger Term Quality	'technical debt' and 'codebase' are natural terms a user would say, but coverage is narrow and misses common variations like 'refactoring', 'code quality', 'legacy code', or 'codebase cleanup', fitting the score-2 anchor.	2 / 3
Distinctiveness Conflict Risk	'technical debt' analysis and remediation is a clear niche with distinct triggers that is unlikely to overlap with unrelated skills, matching the score-3 anchor.	3 / 3
	Total	9 / 12 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: rmyndharis/antigravity-skills
Path: skills/codebase-cleanup-tech-debt/SKILL.md
Commit: e63f7dd

Reviewed: about 5 hours ago

Table of Contents

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