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ai-development-guide

Applies technical decision criteria, anti-pattern detection, debugging, and quality gates for backend and frontend code. Use when implementing, reviewing, or diagnosing code, code smells, failures, or implementation completeness.

Quality

79%

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tessl review fix ./dev-workflows-frontend/skills/ai-development-guide/SKILL.md

The canonical home for this skill is ai-development-guide in shinpr/claude-code-workflows

SKILL.md
Quality
Evals
Security

Quality

Content

67%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.

A well-structured, substantive criteria collection with concrete checklists, templates, and a clearly signaled one-level-deep reference file. Its main cost is token weight: the body re-explains basics Claude already knows and carries long abstract prose that could be trimmed or offloaded to references.

Suggestions

Trim explanations of concepts Claude already knows, such as the glosses on SRP, DRY, and version control in the anti-patterns list, keeping only the project-specific detection guidance.

Tighten the abstract Value-First Engineering prose (e.g., the 'Judge total complexity across every activated surface' paragraph) into a short checklist of what to compare and when to stop.

Move detail with low always-on value (e.g., the expanded Impact Analysis report template and per-pattern avoidance methods) into reference files like references/frontend.md to reduce the main file's token footprint.

DimensionReasoningScore

Conciseness

The body is mostly prescriptive rather than padded, but it explains concepts Claude already knows ('Violates Single Responsibility Principle (SRP)', 'Disabling code with comments - Should use version control', 'Error suppression - Hiding problems creates technical debt') and contains long abstract passages that could be tightened. This matches anchor 3 ('mostly efficient but includes some unnecessary explanation or could be tightened') rather than anchor 2, since there is no tutorial-style filler.

3 / 5

Actionability

As an instruction-only skill it provides concrete guidance: a 4-step 'Before Implementing Any Fallback' checklist, an impact-analysis report template, explicit review triggers, and a modification decision tree. It stays at anchor 4 rather than 5 because several sections state abstract principles ('Prioritize current simplicity over future extensibility') without operational specificity.

4 / 5

Workflow Clarity

The impact analysis is explicitly sequential (Discovery, Understanding, Identification) with a 'Proceed when...' completion criterion, and the quality workflow states 'Completion requires every applicable configured check to pass'. It is below anchor 5 because other processes (e.g., the quality workflow and fallback design) lack feedback loops for error recovery, though no destructive/batch cap applies.

4 / 5

Progressive Disclosure

The single reference (references/frontend.md) is real, one level deep, and clearly signaled with a usage trigger ('read when implementing, reviewing, fixing, or diagnosing frontend code'). It remains at anchor 4 rather than 5 because the ~250-line main file inlines considerable detail (failure patterns, impact-analysis depth) that could be split into reference files.

4 / 5

Total

15

/

20

Passed

Description

80%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 description that clearly states capabilities and provides an explicit, multi-trigger 'Use when' clause in third person. Its main weakness is breadth: the triggers cover nearly any coding task, creating overlap risk with generic code-review and debugging skills.

DimensionReasoningScore

Specificity

The description lists several specific actions ('anti-pattern detection, debugging, and quality gates for backend and frontend code') with only minor abstraction ('Applies technical decision criteria'). It falls short of anchor 5 because the actions are not fully concrete and comprehensive, but exceeds anchor 3 by naming more than 1-2 distinct capabilities.

4 / 5

Completeness

It explicitly answers both 'what' ('Applies technical decision criteria, anti-pattern detection, debugging, and quality gates for backend and frontend code') and 'when' ('Use when implementing, reviewing, or diagnosing code, code smells, failures, or implementation completeness') with concrete trigger phrases, matching the anchor 5 example pattern exactly.

5 / 5

Trigger Term Quality

Natural user phrases like 'implementing, reviewing, or diagnosing code, code smells, failures' map well to what a user would actually say. A few natural terms are missing (e.g., 'refactoring', 'bug fixing', 'testing'), keeping it below anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The 'what' is fairly distinctive (decision criteria + anti-patterns + quality gates), but the trigger 'Use when implementing, reviewing, or diagnosing code' is broad enough to overlap with general code-review and debugging skills. It is somewhat specific, matching anchor 3, rather than anchor 4's 'minor overlap risk with closely related skills'.

3 / 5

Total

16

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

shinpr/claude-code-workflows

Path

dev-workflows-frontend/skills/ai-development-guide/SKILL.md

Reviewed

Table of Contents

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