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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

64

1.66x
Quality

51%

Does it follow best practices?

Impact

80%

1.66x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/codebase-cleanup-tech-debt/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%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.

The body is a thorough, well-structured technical-debt framework with concrete thresholds and executable code, but it is markedly verbose — explaining concepts Claude already knows and inlining ~380 lines of illustrative templates that should live in reference files. Splitting detail into bundle files and trimming the conceptual padding would materially improve both conciseness and progressive disclosure.

Suggestions

Move the metrics-dashboard YAML, trend-analysis code, and stakeholder report templates into separate reference files (e.g., references/metrics-template.md, references/communication-templates.md) and link to them from SKILL.md to improve progressive_disclosure and conciseness.

Cut the definitional debt-type taxonomies (e.g., explanations of god classes, feature envy, duplicated code) down to the actionable thresholds and quantification prompts, since Claude already knows these concepts.

Add explicit validation checkpoints to the workflow (e.g., "Confirm the debt inventory is complete and metrics are gathered before prioritizing") to lift workflow_clarity above 3.

DimensionReasoningScore

Conciseness

The ~380-line body extensively enumerates debt types with definitions Claude already knows (god classes, duplicated code, feature envy) and pads with illustrative fake-number templates (dashboard YAML with cyclomatic_complexity 15.2, trend dicts, ROI figures), matching the "noticeably verbose; several unnecessary explanations or padded sections" anchor; not a 1 because the structure is purposeful rather than undirected rambling, and not a 3 because the padding is substantial, not occasional.

2 / 5

Actionability

Provides concrete, executable guidance — specific thresholds (complexity >10, methods >50 lines, god classes >500 lines/>20 methods), real Python facade/refactoring code, and concrete YAML configs for pre-commit hooks and CI gates; not a 5 because several examples (ROI projections, trend analysis) are illustrative templates with invented numbers rather than copy-paste-ready instructions for the user's actual codebase.

4 / 5

Workflow Clarity

The eight numbered sections (Inventory → Impact → Metrics → Plan → Implementation → Prevention → Communication → Success Metrics) give a clear sequence, but validation/checkpoint steps are implicit or absent (no "verify inventory completeness" or "confirm metrics before planning" gates), matching the "steps listed but validation gaps; checkpoints missing or implicit" anchor; not a 4 because no explicit checkpoints are present despite the multi-step process.

3 / 5

Progressive Disclosure

No bundle files exist and the entire ~380-line guide is inlined in SKILL.md with well-organized section headers but no references to separate files, matching the "some structure but content that should be separate is inline" anchor; not a 2 because sectioning is genuinely good rather than minimal, and not a 4 because substantial content (metrics templates, refactoring examples, report templates) that belongs in reference files is not split out.

3 / 5

Total

12

/

20

Passed

Description

53%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.

The description clearly states what the skill does with several concrete actions but is written in second-person voice and omits any "Use when" trigger guidance, capping completeness at 3 and costing a specificity point. It is also truncated mid-sentence ("create acti"), which slightly undermines its polish. Adding an explicit trigger clause and converting to third person would raise it into the 4 range.

Suggestions

Add an explicit "Use when..." clause naming natural triggers (e.g., "Use when the user asks about technical debt, tech debt, refactoring priorities, or codebase health") to lift completeness above 3.

Rewrite in third person ("Identifies, quantifies, and prioritizes technical debt...") instead of "You are.../Analyze..." to recover the specificity penalty and align with skill-description conventions.

Complete the truncated sentence ("create acti" → "create actionable remediation plans") and add synonyms like "refactoring", "legacy code", and "tech debt" to broaden trigger-term coverage.

DimensionReasoningScore

Specificity

Lists several concrete actions ("identifying, quantifying, and prioritizing technical debt", "Analyze the codebase to uncover debt, assess its impact, and create actionable remediation plans") which would anchor at 4, but the second-person voice ("You are", "Analyze") triggers the mandated -1 specificity penalty, bringing it to 3; not a 2 because the verbs are domain-specific and go beyond a single generic action.

3 / 5

Completeness

The "what" is clear (identify, quantify, prioritize, assess, remediate technical debt) but there is no "Use when..." clause or equivalent trigger guidance, so completeness is capped at 3 per the judging guideline; not a 4 because "when" is entirely absent rather than merely weak.

3 / 5

Trigger Term Quality

"technical debt" and "codebase" are relevant natural keywords, but coverage stops there — missing common variations like "refactoring", "legacy code", "tech debt", or "code cleanup", matching the anchor where some relevant keywords exist but synonyms are missing; not a 4 because keyword coverage is thin rather than just a few terms short.

3 / 5

Distinctiveness Conflict Risk

"technical debt" is a fairly distinct niche with minor overlap risk only against closely related refactoring or code-review skills; not a 5 because no explicit trigger phrases carve out a clearly unique surface from those adjacent skills.

4 / 5

Total

13

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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