Name: performance-optimizer
Rating: 54.400000000000006 (1 reviews)
Author: administrakt0r

performance-optimizer

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

Quality

61%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/performance-optimizer/SKILL.md

The canonical home for this skill is performance-optimizer in administrakt0r/AI-Agents-Safe-Coding-Skills

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.

The content is highly actionable with a clear measure-optimize-verify workflow and useful checklists, but it is a verbose, monolithic file that re-explains concepts Claude already knows and would benefit from splitting detailed pattern catalogs into reference files.

Suggestions

Trim explanations of well-known concepts (N+1 queries, React.memo, useMemo, Set dedup) and let the before/after code carry the lesson.

Move the large 'Common Optimizations' pattern catalog into a one-level-deep reference file (e.g. references/optimization-patterns.md) and keep SKILL.md as a lean overview with clear pointers.

Add an explicit validate/re-measure checkpoint inside the optimization steps (not just the checklist) so the feedback loop is visible in the workflow itself.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient code-focused before/after examples, but at ~380 lines it covers many optimization categories and explains patterns Claude already knows (N+1 queries, React.memo, useMemo, Set-based dedup), so it could be tightened — fitting the 'mostly efficient but includes some unnecessary explanation' anchor.	3 / 5
Actionability	Most sections give concrete, executable code (console.time, EXPLAIN ANALYZE, CREATE INDEX, Promise.all, React.lazy, srcset images) with minor gaps such as the high-level profiling instructions, matching the 'mostly executable guidance with minor gaps' anchor.	4 / 5
Workflow Clarity	A clear measure-first → find-bottleneck → optimize sequence is present, reinforced by a 'Measuring Impact' before/after loop and an Optimization Checklist serving as checkpoints, with only minor validation gaps — matching the 'clear sequence with most checkpoints' anchor.	4 / 5
Progressive Disclosure	The single SKILL.md is well-sectioned but at ~380 lines inlines a large catalog of optimization patterns (database, API, frontend, algorithm, memory) that would be better split into one-level-deep reference files, matching the 'some structure but content that should be separate is inline' anchor.	3 / 5
	Total	14 / 20 Passed

Description

58%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 states concrete capabilities and a distinct niche, but omits any explicit 'Use when...' trigger guidance and lacks common synonyms like 'slow' or 'optimize', leaving it solid but incomplete for triggering.

Suggestions

Add an explicit 'Use when...' clause with concrete trigger phrases, e.g. 'Use when the app is slow, an API or database query is lagging, or the user mentions performance, slow, lag, or optimize.'

Broaden trigger coverage with natural synonyms users say — 'slow', 'lag', 'optimize', 'latency' — rather than relying on 'performance bottlenecks' alone.

Optionally name 1-2 more concrete actions (e.g. 'adds indexes', 'enables caching') to push specificity toward the top anchor.

Dimension	Reasoning	Score
Specificity	Names the domain ('performance bottlenecks in code, databases, and APIs') and lists several concrete actions — 'Identifies and fixes', 'Measures before and after to prove improvements' — with only minor gaps in coverage, fitting the anchor for several specific actions rather than the fully comprehensive top anchor.	4 / 5
Completeness	The description clearly answers 'what' (identifies/fixes/measures) but contains no 'Use when...' clause or equivalent explicit trigger guidance, so per the guideline it is capped at 3 for a clear 'what' with 'when' missing.	3 / 5
Trigger Term Quality	'performance bottlenecks' is a natural term a user might say, but common variations and synonyms users actually use ('slow', 'lag', 'optimize') are absent, matching the anchor for some relevant keywords missing common variations.	3 / 5
Distinctiveness Conflict Risk	The performance-optimization niche across code, databases, and APIs is mostly distinct from related skills, with only minor overlap risk (e.g. database-design, code-review), matching the 'mostly distinct' anchor.	4 / 5
	Total	14 / 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: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/performance-optimizer/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.