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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.

Quality

75%

Does it follow best practices?

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Passed

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

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-sequenced orchestration workflow with genuinely actionable per-agent prompts, concrete configuration knobs, and measurable success criteria. It is held back by an unnecessary meta-commentary block, the absence of error-recovery feedback loops between phases, and a monolithic 156-line single-file layout that inlines all detailed prompt content instead of splitting it into references.

Suggestions

Delete the "[Extended thinking: ...]" paragraph — it describes the workflow's philosophy rather than instructing any action, and its claims ("data-driven optimization strategy") are already implied by the phase structure.

Move the 13 detailed Role/Prompt/Context/Output blocks into per-phase reference files (e.g., references/phase-1-profiling.md) and keep SKILL.md as a lean overview with phase summaries and links, enabling progressive disclosure.

Add an explicit feedback loop after Phase 4, e.g., "If load tests miss the Success Criteria, identify the highest-impact remaining bottleneck from the Phase 1 profile and re-run the relevant optimization phase before proceeding to monitoring."

DimensionReasoningScore

Conciseness

The "[Extended thinking: ...]" block (~100 words: "creates a data-driven optimization strategy that addresses real bottlenecks rather than theoretical improvements... emphasizes modern observability practices") is pure meta-commentary that instructs nothing, and each of the 13 prompts repeats the "for: [user request]" pattern. Not a 2 because the rest is mostly efficient with no basic-concept explanations; not a 4 because the meta block is more than a minor trim.

3 / 5

Actionability

The 13 steps give concrete, near copy-paste-ready agent prompts with specific tools and techniques ("Generate flame graphs... heap dumps", "query result caching with Redis/Memcached", "load testing... using k6/Gatling/Artillery"), plus concrete Configuration Options and numeric Success Criteria. Not a 5 because there are no executable tool invocations and context placeholders like {context_from_phase_1} have no defined threading mechanism.

4 / 5

Workflow Clarity

Clear 5-phase, 13-step sequence where every step states its Context ("Performance bottlenecks from phase 1") and Output, with a dedicated validation phase (load testing, regression tests with "automatic rollback triggers") and guardrail instructions. Not a 5 because there are no explicit feedback loops — nothing says what to do when a load test or success criterion fails; not a 3 because validation is an explicit phase, not an implicit gap.

4 / 5

Progressive Disclosure

Well-sectioned single file (Use/Do not use, Instructions, Safety, Phases 1-5, Configuration, Success Criteria) but at ~156 lines with zero bundle files, all 13 detailed agent prompts are inlined in SKILL.md when they could live in per-phase reference files. The under-50-line simple-skill exception does not apply, and content that should be separate is inline — the anchor-3 fit — though the good header structure keeps it above a 2.

3 / 5

Total

14

/

20

Passed

Description

75%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 that clearly states what the skill does and when to use it, with good natural trigger terms and a distinct full-stack performance niche. Its main limitation is that the 'Use when' clause echoes the skill name rather than enumerating concrete user-facing trigger phrases, and a few common synonyms (slow, latency, faster) are absent.

DimensionReasoningScore

Specificity

Lists several named actions — "profiling, observability, and backend/frontend tuning" — covering the domain with minor gaps (no mention of load testing, caching, or databases). Not a 3 because it goes beyond 1-2 actions; not a 5 because "observability" and "tuning" are category terms rather than the fully concrete actions of the top anchor.

4 / 5

Completeness

Explicitly answers both: what ("Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning") and when ("Use when coordinating performance optimization across the stack"). Not a 5 because the when-clause is circular — it restates the skill's own name rather than giving concrete user trigger phrases like "slow endpoints" or "latency issues".

4 / 5

Trigger Term Quality

Good natural keyword coverage: "performance", "optimization", "profiling", "tuning", "across the stack" — phrases users would plausibly say. Missing common variations like "slow", "faster", "latency", "bottleneck", or "load testing", which keeps it below the comprehensive synonym coverage of a 5.

4 / 5

Distinctiveness Conflict Risk

Has a clear niche — end-to-end, multi-layer performance optimization — distinguishable from generic skills. Minor overlap risk with narrower performance-adjacent skills (frontend optimization, database tuning, load testing), which "across the stack" explicitly embraces, so it is not the minimal-conflict 5.

4 / 5

Total

16

/

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

rmyndharis/antigravity-skills

Path

skills/application-performance-performance-optimization/SKILL.md

Reviewed

Table of Contents

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