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ai-development-guide

Applies language-agnostic and backend technical decision criteria, anti-pattern detection, debugging, and quality gates. Use when reviewing general/backend implementation choices, code smells, failures, or implementation completeness.

53

Quality

59%

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tessl review fix ./dev-skills/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

55%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 content is a well-organized, instruction-only engineering guide with clearly sequenced workflows (3-stage impact analysis, quality-check workflow) and useful decision templates. It is held back by verbose, clause-heavy prose and some duplication between the anti-pattern and failure-pattern sections, plus largely principle-level rather than executable actionability.

Suggestions

Tighten long multi-clause sentences into imperative bullet points and de-duplicate the Code Quality Anti-patterns list against the Common Failure Patterns section to reduce tokens.

Convert principle-level guidance into more concrete, executable steps or small templates (e.g., show the exact 5-Whys output format and a worked error-context propagation example) to raise actionability.

Make validation checkpoints in the Quality Check Workflow explicit (e.g., 'Run all applicable static/build/behavior checks; do not proceed until each passes') to push workflow clarity toward a 5.

DimensionReasoningScore

Conciseness

The body is dense and mostly free of basic-concept padding, but it restates principles at length and uses long, clause-heavy sentences that could be tightened; some sections re-explain ideas (e.g., anti-patterns appear in both the Red Flag list and Common Failure Patterns).

3 / 5

Actionability

It offers concrete checklists and two template blocks (Impact Analysis, error-handling pattern) plus a decision tree, but much guidance is principle-level prose rather than executable steps or commands, and the templates are skeletons the model must still fill in.

3 / 5

Workflow Clarity

The Risk-Scaled 3-Stage Impact Analysis is a clearly sequenced Discovery→Understanding→Identification process with an explicit proceed-when gate, and the Quality Check Workflow lists ordered check categories with a completion criterion; minor validation checkpoints are implicit rather than enforced.

4 / 5

Progressive Disclosure

Content is well sectioned with clear headers and no external bundle files (references/scripts/assets are absent), so navigation within the file is fine; however the SKILL.md is a single ~260-line monolith with no split references for the substantial pattern catalog, which is appropriate for an instruction-only skill but sits between anchors.

3 / 5

Total

13

/

20

Passed

Description

63%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 covers both what the skill does and when to use it, with concrete capability areas and a Use-when clause, placing it solidly above average. It could improve by using more natural user-spoken trigger language and narrowing its niche to reduce overlap with general code-review skills.

Suggestions

Lead the 'Use when' clause with plain-language phrases users actually say (e.g., 'Use when reviewing code, spotting code smells, debugging a backend failure, or checking implementation completeness').

Add file/technology triggers or concrete synonyms (e.g., 'backend APIs', 'service layers') to improve keyword coverage and distinctiveness from generic review skills.

Tighten the broad 'language-agnostic and backend technical decision criteria' wording so the niche is less likely to overlap with general code-review skills.

DimensionReasoningScore

Specificity

Names the domain (language-agnostic/backend technical decisions) and lists several concrete capabilities — 'anti-pattern detection, debugging, and quality gates' — though the action phrasing is somewhat generic relative to the breadth claimed.

4 / 5

Completeness

It explicitly answers both 'what' (applies decision criteria, anti-pattern detection, debugging, quality gates) and 'when' (Use when reviewing implementation choices, code smells, failures, or completeness); the 'when' is present and concrete but bundled into one clause.

4 / 5

Trigger Term Quality

It includes 'Use when reviewing general/backend implementation choices, code smells, failures, or implementation completeness', providing several natural trigger phrases, but it relies on formal review vocabulary rather than the plain language a user would spontaneously say.

3 / 5

Distinctiveness Conflict Risk

The 'general/backend implementation review' niche is broad and overlaps with generic code-review skills, but the language-agnostic/backend framing and anti-pattern focus give it some distinguishability.

3 / 5

Total

14

/

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
shinpr/claude-code-workflows
Reviewed

Table of Contents

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