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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

74%

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tessl review fix ./dev-workflows-fullstack/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

60%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 well-structured collection of decision criteria with one properly signaled bundle reference, and its strongest workflow (Impact Analysis) is clearly sequenced with an explicit gate. However, it relies heavily on abstract policy prose and pseudocode rather than executable examples, restates duplication guidance across multiple sections, and explains familiar concepts (SRP, DRY, YAGNI) that cost tokens without adding information.

Suggestions

Replace the pseudocode error-handling block with one real, executable language-specific example (e.g., Go/Python error wrapping), since the rubric penalizes pseudocode unless flexibility is explicitly justified beyond the one-line 'Adaptation' note.

Consolidate the three separate duplication/commonalization discussions (Technical Anti-patterns, Criteria for Code Duplication, Timing of Abstraction) into a single section and remove explanations of SRP, DRY, and YAGNI that Claude already knows.

Add an explicit validate-and-retry feedback loop to the Quality Check Workflow (e.g., 'if a check fails, fix the issue and re-run that check before proceeding') to match the checkpoint quality already present in the Impact Analysis section.

DimensionReasoningScore

Conciseness

The body is mostly novel project policy, but it explains concepts Claude already knows ("Violates Single Responsibility Principle (SRP)", "Violates DRY principle", "Be conscious of YAGNI", "Identify root cause with 5 Whys") and duplicates guidance across sections (duplication criteria appear in "Technical Anti-patterns", "Criteria for Code Duplication", and "Situations Requiring Technical Decisions"). Long multi-clause sentences like "Treat behavior-preserving maintenance inside the assigned responsibility as current maintainer value when repository evidence shows..." could be tightened considerably.

3 / 5

Actionability

Concrete artifacts exist (the Impact Analysis report template, the "Required by the requested change? No → Preserve" decision tree, the numbered "Before Implementing Any Fallback" checklist), but the only code example is pseudocode ("AVOID: Silent fallback that hides errors / <handle error>: return DEFAULT_VALUE") and no section gives an executable command or worked example. This matches the anchor for some concrete guidance with pseudocode instead of executable code and missing key details.

3 / 5

Workflow Clarity

The Impact Analysis section gives a clearly sequenced 3-stage process ("1. Discovery", "2. Understanding", "3. Identification") with an explicit proceed gate ("Proceed when discovery and understanding cover the accepted outcome..."), and the quality workflow states "Completion requires every applicable configured check to pass". It falls short of 5 because no workflow includes an explicit fix-and-retry feedback loop, and the Quality Check Workflow defers command selection to discovery rather than specifying steps.

4 / 5

Progressive Disclosure

Structure is good: clear section headers throughout, and a single well-signaled one-level-deep reference ("[references/frontend.md](references/frontend.md): read when implementing, reviewing, fixing, or diagnosing frontend code") that exists in the bundle and adds complementary React/TypeScript criteria. Not 5 because the main file is a ~250-line full criteria set rather than a lean overview, and content like the Fail-Fast Fallback design section is dense enough that further splitting could aid navigation.

4 / 5

Total

14

/

20

Passed

Description

76%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 follows the strongest structural pattern: an explicit what-clause listing multiple capabilities followed by an explicit 'Use when' clause with concrete triggers. Its main weakness is distinctiveness — by claiming all implementing, reviewing, and diagnosing of backend and frontend code, it has high conflict risk with the many existing skills that target those same tasks.

Suggestions

Narrow the trigger scope to the skill's distinctive niche (e.g., 'Use when making structural or design trade-off decisions in code, or when diagnosing repeated implementation failures') to reduce conflict with general code-review and debugging skills.

Replace the abstract "Applies technical decision criteria" with a more concrete capability verb, such as 'Evaluates implementation approaches against complexity and anti-pattern criteria'.

Add one or two distinguishing concrete terms users would say (e.g., 'design decisions', 'root cause', 'change impact analysis') that separate this skill from generic code-editing skills.

DimensionReasoningScore

Specificity

It lists several distinct actions in a named domain ("Applies technical decision criteria, anti-pattern detection, debugging, and quality gates for backend and frontend code"), matching the several-specific-actions anchor. Not 5 because "applies technical decision criteria" is abstract rather than a concrete operation, leaving minor gaps in coverage.

4 / 5

Completeness

It explicitly answers both questions: 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 top anchor's structure.

5 / 5

Trigger Term Quality

"Use when implementing, reviewing, or diagnosing code, code smells, failures, or implementation completeness" includes natural phrases users would say, plus "debugging" in the what-clause. A few common natural terms are missing (e.g., "fixing bugs", "refactoring", "code review"), which matches good-but-not-comprehensive keyword coverage.

4 / 5

Distinctiveness Conflict Risk

The triggers "implementing, reviewing, or diagnosing code" cover essentially every coding task, so this description would fire alongside nearly any code-review, debugging, refactoring, or testing skill — matching the very-broad/high-overlap anchor. It is above 1 only because it is scoped to code quality rather than being entirely generic.

2 / 5

Total

15

/

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-fullstack/skills/ai-development-guide/SKILL.md

Reviewed

Table of Contents

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