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application-performance-performance-optimization

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

64

Quality

75%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./skills/application-performance-performance-optimization/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-sequenced, highly actionable phased workflow that is let down by verbosity and a monolithic single-file structure with no validation feedback loops or external reference splitting.

Suggestions

Remove the [Extended thinking] prose block and trim tool-name enumerations from step prompts to reduce token load.

Add explicit validation/rollback checkpoints between phases (e.g. verify baseline improvements before moving to tuning) given the production and load-testing risk.

Move the per-step prompt templates and Configuration/Success-Criteria detail into reference files (e.g. PHASES.md, CONFIG.md) and link them one level deep from the overview.

DimensionReasoningScore

Conciseness

Mostly actionable but padded: the [Extended thinking] block narrates the whole workflow in prose that Claude doesn't need, and the 13 step prompts repeat tool brand-names and context Claude already knows; could be tightened substantially without losing clarity.

2 / 3

Actionability

Each step assigns a concrete subagent_type and supplies specific, copy-paste-ready prompts naming real tools (k6/Gatling/Artillery, DataDog/New Relic, OpenTelemetry) — fully executable guidance.

3 / 3

Workflow Clarity

Phases 1–5 and 13 steps are clearly sequenced with context/output chaining, but there are no explicit validation checkpoints or fix→retry feedback loops despite risky production changes and load testing, which caps clarity at 2.

2 / 3

Progressive Disclosure

No bundle files exist and all ~150 lines of detailed prompts live inline in a single monolithic body; phase structure gives some organization, but content that should be split into references is not, and no external references are signaled.

2 / 3

Total

9

/

12

Passed

Description

85%

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 strong, specific description that answers what and when with concrete actions and a distinct niche, weakened only by limited trigger-term coverage of the natural phrases users actually say.

Suggestions

Broaden the 'Use when' trigger with natural phrasings users say, e.g. 'slow app', 'high latency', 'make it faster', 'throughput', 'capacity'.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'profiling, observability, and backend/frontend tuning' — matching the score-3 anchor of named specific actions.

3 / 3

Completeness

Clearly answers both what it does and when to use it via the explicit 'Use when coordinating performance optimization across the stack' trigger clause.

3 / 3

Trigger Term Quality

Contains relevant keywords ('performance', 'optimization', 'profiling') but lacks common natural variations a user would actually say ('make it faster', 'slow', 'latency'); closer to the 'some relevant keywords but missing common variations' anchor than full coverage.

2 / 3

Distinctiveness Conflict Risk

Has a clear niche (end-to-end performance optimization across the stack) with distinct triggers unlikely to fire for unrelated skills.

3 / 3

Total

11

/

12

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
rmyndharis/antigravity-skills
Reviewed

Table of Contents

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