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1k-performance

Performance optimization for React/React Native — re-renders, memoization, FlashList, memory leaks, and bundle size.

58

Quality

69%

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tessl review fix ./.skillshare/skills/1k-performance/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%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 overview skill: executable batched-request code, repo-specific 'already optimized' knowledge Claude cannot infer, and clean progressive disclosure to a single verified reference file. The main gap is the absence of any diagnostic or measurement workflow, leaving it a pattern catalog rather than a guided procedure.

Suggestions

Add a short diagnostic step (e.g., 'identify the category from the Quick Reference table, check the Built-in Optimizations table before making changes, then apply the matching pattern from performance.md').

Include a validation checkpoint such as measuring render counts or profiling before and after applying an optimization, since performance changes are easy to get wrong without evidence.

Trim the 'Topics covered' bullet list or fold it into the reference pointer line to save tokens without losing navigation value.

DimensionReasoningScore

Conciseness

The body is dominated by dense, high-value tables and executable code with almost no explanation of concepts Claude already knows (e.g., no primer on what memoization is). A few tokens could be trimmed — the 'Topics covered' bullet list partially duplicates the reference file's own contents and the 'Related Skills' section is optional context — so it sits just below the 'lean and efficient' anchor.

4 / 5

Actionability

The 'executeBatched' implementation is complete, executable TypeScript with a usage example, and the tables give concrete values ('windowSize={5}', 'contentVisibility: hidden', 'Limit to 3-5'). Not 5 because most of the nine listed topic areas (bridge optimization, image optimization, race conditions, etc.) are only named, not demonstrated, so common cases beyond request batching require reading the reference.

4 / 5

Workflow Clarity

The Quick Reference table functions as a problem-to-optimization routing guide, but there is no diagnostic sequence (how to identify which category a perf issue falls into) and no validation steps such as profiling or measuring before/after an optimization. The content is reference material rather than a multi-step workflow, so it lands at 'sequence/checkpoints missing or implicit' rather than higher.

3 / 5

Progressive Disclosure

The body is a concise overview with a single clearly signaled, one-level-deep reference ('see [performance.md](references/rules/performance.md)'), which exists in the bundle at references/rules/performance.md, plus a topic index that aids navigation. Content is appropriately split between quick rules inline and the detailed guide in the reference file.

5 / 5

Total

16

/

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 well-scoped, specific description with genuinely natural trigger terms for a React performance context. Its main deficiency is the absence of any explicit 'Use when...' trigger guidance, which caps completeness and leaves invocation conditions to inference.

Suggestions

Add an explicit trigger clause, e.g., 'Use when the user reports slow renders, laggy lists, app hangs, jank, or growing memory in React/React Native apps.'

Include natural synonyms users might say (slow, laggy, janky, app hang) alongside the technical terms to broaden trigger coverage.

State one or two concrete actions per area (e.g., 'batch concurrent requests, memoize expensive renders') to move from topic labels toward comprehensive action coverage.

DimensionReasoningScore

Specificity

Names the domain ('React/React Native') and lists five concrete optimization areas ('re-renders, memoization, FlashList, memory leaks, and bundle size'), matching the 'several specific actions; minor gaps' anchor. Not 5 because these are topic labels rather than an exhaustive set of concrete actions (e.g., no mention of what is actually done for each area).

4 / 5

Completeness

The 'what' is clear (performance optimization for React/React Native across the listed areas), but there is no 'Use when...' clause or equivalent explicit trigger guidance, capping completeness at 3 per the judging guidelines. Not 2 because the 'what' is specific and well-scoped rather than vague.

3 / 5

Trigger Term Quality

're-renders', 'memoization', 'FlashList', 'memory leaks', and 'bundle size' are natural phrases developers actually say when facing performance problems, giving good keyword coverage. Not 5 because common variations like 'slow', 'laggy', 'jank', or 'app hang' are absent.

4 / 5

Distinctiveness Conflict Risk

Scoping to 'React/React Native' performance with named tools like FlashList makes it mostly distinct with only minor overlap risk against a general React coding-patterns skill (the body itself lists '/1k-coding-patterns' as related). Not 5 because the trigger surface still overlaps somewhat with general React performance advice found in coding-convention skills.

4 / 5

Total

15

/

20

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

relative_links

Relative link issues: 1 deeper-than-1-level

Warning

referenced_paths_exist

Referenced path issues: 1 deeper-than-1-level

Warning

Total

13

/

16

Passed

Repository
OneKeyHQ/app-monorepo
Reviewed

Table of Contents

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