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performance-optimizer

Identifies and fixes performance bottlenecks in code, databases, and APIs. Measures before and after to prove improvements.

Quality

71%

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tessl review fix ./skills/performance-optimizer/SKILL.md

Quality

Content

63%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, highly actionable skill with clear sequencing and verification habits, but it is a long monolith that inlines a large catalog of optimization patterns Claude largely already knows, with no reference files for progressive disclosure. Splitting the catalog into references and trimming known-pattern boilerplate would improve both conciseness and disclosure.

Suggestions

Move the 'Common Optimizations' catalog, 'Performance Budgets', and 'Tools' sections into reference files (e.g. references/optimizations.md, references/tools.md) and keep SKILL.md as a concise overview linking to them.

Trim patterns Claude already knows (N+1 queries, React.memo, Promise.all, lodash imports) to one-line mentions or short entries instead of full before/after code blocks.

Add an explicit feedback loop when an optimization doesn't measurably improve (e.g. revert and profile the next bottleneck) to strengthen workflow validation.

Dimension	Reasoning	Score
Conciseness	The body is compact and code-forward with little prose padding, but the ~200-line 'Common Optimizations' catalog largely restates patterns Claude already knows (N+1 queries, React.memo, Promise.all, lodash imports, streaming). It is above anchor 2 because there is little explanatory filler, but below anchor 4 because so much of the catalog is boilerplate knowledge that could be trimmed.	3 / 5
Actionability	Guidance is mostly executable: concrete commands (`node --prof app.js`, `EXPLAIN ANALYZE`, `CREATE INDEX idx_users_email ON users(email)`) and complete before/after code pairs. It falls short of anchor 5 because some examples assume an unspecified ORM (`db.users.find().populate('posts')`) and are illustrative rather than copy-paste ready.	4 / 5
Workflow Clarity	The process is clearly sequenced (Measure First → Find the Bottleneck → Optimize) with a 'Measuring Impact' re-measurement section and an end-of-work checklist including 'Verified functionality still works' and 'No new bugs introduced'. Minor validation gaps (no explicit error-recovery loop when measurements don't improve) keep it below anchor 5.	4 / 5
Progressive Disclosure	Section headers organize the content well, but the skill is a ~390-line monolith with zero reference files: the optimization catalog, budgets, and tool lists are all inlined in SKILL.md where they belong in separate reference files. This matches anchor 3 (structure present, but content that should be separate is inline) rather than anchor 2, since navigation via headers is clear.	3 / 5
	Total	14 / 20 Passed

Description

66%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 solid description with concrete actions and good natural keywords, but it omits any explicit "when to use" trigger guidance, which caps its completeness. Adding a 'Use when...' clause with terms like slow, laggy, or optimize would raise it substantially.

Suggestions

Add an explicit trigger clause, e.g. "Use when the app is slow or laggy, or when the user mentions performance, speed, slow queries, or optimization."

Include common user phrasings and synonyms ("slow", "speed up", "latency", "laggy") to broaden natural trigger coverage.

Name one or two signature techniques (e.g. profiling, query indexing, caching) to sharpen distinctiveness against general debugging skills.

Dimension	Reasoning	Score
Specificity	The description lists several concrete actions — "Identifies and fixes performance bottlenecks", "Measures before and after to prove improvements" — and names its domains ("code, databases, and APIs"). It falls short of anchor 5 because the actions are broad categories rather than a comprehensive list of specific techniques.	4 / 5
Completeness	The "what" is clearly answered (identify, fix, and measure performance bottlenecks), but there is no "Use when..." clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines. It is not a 2 because the "what" is concrete and multi-part rather than vague.	3 / 5
Trigger Term Quality	Natural keywords like "performance", "bottlenecks", "code", "databases", and "APIs" match what users say when they need this skill. It misses common variations such as "slow", "speed up", "laggy", and "optimize", keeping it below anchor 5, but coverage is clearly better than anchor 3's partial synonym set.	4 / 5
Distinctiveness Conflict Risk	The performance-optimization niche with named targets (code, databases, APIs) is mostly distinct, with only minor overlap risk against general debugging or profiling skills. It is not a 5 because the description lacks distinctive trigger phrases that would fully separate it from adjacent debugging/tuning skills.	4 / 5
	Total	15 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
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
Path: skills/performance-optimizer/SKILL.md
Revision: 64adc55

Reviewed: 14 days ago

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