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performance

Deep performance optimization knowledge for the Lichtblick codebase. Covers profiling techniques, common bottlenecks, memory management patterns, and optimization strategies specific to real-time data visualization.

Quality

79%

Does it follow best practices?

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tessl review fix ./.github/skills/performance/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

82%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.

An excellent, codebase-specific knowledge file that is token-efficient and highly actionable, with specific thresholds, real file/symbol names, and a validated leak-hunting loop. Its main gaps are fragmentary code snippets that aren't quite copy-paste ready and no feedback-loop guidance for acting on profiling findings.

DimensionReasoningScore

Conciseness

The body is lean and dense: it assumes competence with Comlink, THREE, and Chart.js internals without explaining them, and every line carries project-specific value (e.g. 'IterablePlayer caps at 300ms per tick', '600MB player-level message cache budget (CachingIterableSource)'). It matches anchor 5 — no padding, no basic-concept explanation, every token earns its place.

5 / 5

Actionability

Mostly executable guidance: runnable snippets (Comlink.transfer, THREE.Vector3 pooling, queueMicrotask debouncing), exact DevTools navigation, and a concrete benchmark command ('cd benchmark && yarn start'). It falls short of anchor 5 because several snippets are illustrative fragments rather than copy-paste ready — '#scheduleEmit' references an enclosing class that isn't shown, 'mergeSubscriptions' and 'ComlinkWrap' are invoked without import paths or definitions.

4 / 5

Workflow Clarity

The leak-detection workflow is a clear 5-step sequence with an explicit validation checkpoint (force GC, compare snapshots A/B), and bottlenecks use a symptom → cause → fix diagnostic structure. It sits at anchor 4 rather than 5 because the profiling workflow lists tools to check but lacks explicit error-recovery feedback loops (what to do when a metric is abnormal beyond the one-line fixes).

4 / 5

Progressive Disclosure

No bundle files exist (no references/, scripts/, or assets/), so the skill is a single self-contained file with well-organized sections and no nested or buried references. This fits anchor 4's 'good structure; most content appropriately placed' — the optimization-pattern code blocks and benchmarking details could arguably live in reference files, which is the minor gap keeping it from 5, and it is well above anchor 3 since nothing is misfiled or unreferenced.

4 / 5

Total

17

/

20

Passed

Description

65%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 distinctive, domain-anchored description that clearly states what the skill covers, but it reads as a topic catalog rather than an action list and entirely lacks a 'Use when…' trigger clause, which caps its completeness and weakens natural-language triggering.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the Lichtblick app feels slow or laggy, when profiling playback or render performance, or when investigating memory leaks or frame drops.'

Rewrite topic nouns as concrete actions: 'Profile playback and rendering with Chrome DevTools, diagnose common bottlenecks, apply memory-management and optimization patterns specific to the Lichtblick codebase.'

Include natural user phrasings and synonyms — slow, laggy, jank, frame drops, FPS, memory leak — alongside the existing technical terms.

DimensionReasoningScore

Specificity

The description names the domain and enumerates topic areas — 'profiling techniques, common bottlenecks, memory management patterns, and optimization strategies' — but these are subject-matter categories, not concrete actions Claude performs (no 'Profiles', 'Diagnoses', 'Optimizes' verbs). It matches anchor 3 (domain plus limited concrete description) rather than 4, which expects several specific actions; it clears anchor 2 because the coverage is more specific than a generic 'handles performance'.

3 / 5

Completeness

The 'what' is clear (deep performance knowledge covering four areas for the Lichtblick codebase), but there is no 'Use when…' clause or equivalent explicit trigger guidance — 'specific to real-time data visualization' only weakly implies a context. Per the judging guidelines, a missing 'Use when' clause caps completeness at 3, and anchor 3 is exactly this case.

3 / 5

Trigger Term Quality

Terms like 'performance', 'optimization', 'profiling', and 'memory' are natural words a user would say when needing this skill. It falls short of anchor 5 because common variations users actually utter — 'slow', 'lag'/'laggy', 'frame drops', 'FPS', 'memory leak' — are absent, but coverage is better than anchor 3's 'some relevant keywords'.

4 / 5

Distinctiveness Conflict Risk

The description is anchored to a named codebase ('the Lichtblick codebase') and a narrow niche ('real-time data visualization'), giving it a clear domain with minimal overlap against generic performance or visualization skills. It matches anchor 5's 'clear niche with distinct triggers' and is more distinguishable than anchor 4's 'minor overlap risk with closely related skills'.

5 / 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

lichtblick-suite/lichtblick

Path

.github/skills/performance/SKILL.md

Reviewed

Table of Contents

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