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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.

77

1.71x
Quality

74%

Does it follow best practices?

Impact

89%

1.71x

Average score across 3 eval scenarios

SecuritybySnyk

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

The canonical home for this skill is application-performance-performance-optimization in rmyndharis/antigravity-skills

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-structured orchestration workflow with highly concrete per-step prompts, clear phase sequencing, and explicit success thresholds. Its weaknesses are an unnecessary meta-commentary block, unresolved context placeholders, no rollback/retry feedback loops, and a monolithic layout that inlines content better suited to separate reference files.

Suggestions

Delete the '[Extended thinking: ...]' paragraph and the trailing 'Performance optimization target: $ARGUMENTS' line; they add tokens without adding executable guidance.

Explain how to resolve '{context_from_phase_1}' placeholders — e.g., specify which prior step's output to summarize and pass into the subagent prompt — so the prompts become fully copy-paste ready.

Add an explicit feedback loop after Phase 4 (e.g., 'if load testing shows a regression versus baseline, roll back the offending change and re-run the affected phase') and move the per-phase subagent prompts into one-level-deep reference files to keep SKILL.md a concise overview.

DimensionReasoningScore

Conciseness

Most of the body is dense, actionable delegation content, but the bracketed '[Extended thinking: ...]' paragraph (~90 words of workflow philosophy) is pure padding, the trailing 'Performance optimization target: $ARGUMENTS' line is redundant, and several subagent prompts repeat the same caveat lists. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the minor-trimming level of 4.

3 / 5

Actionability

Each of the 13 steps gives an exact subagent_type and a fully written, copy-paste-ready prompt, which is concrete and executable. It is not a 5 because placeholders like '{context_from_phase_1}' appear in several prompts with no instruction on how to actually populate or pass context between subagents, leaving a real execution gap.

4 / 5

Workflow Clarity

The 5-phase, 13-step sequence is clearly ordered, each step declares its Context input and Output, and Phase 4 (load testing + regression tests) plus the Success Criteria act as validation checkpoints with defined thresholds. It is not a 5 because there is no explicit feedback loop (e.g., 'if P95 regresses, roll back and revisit phase 2') tying validation back into the sequence.

4 / 5

Progressive Disclosure

Sections and phase headers are well organized, but this is a ~150-line monolithic file: the 13 detailed subagent prompts and the configuration/success-criteria reference material plausibly belong in per-phase reference files, and there are no bundle files at all. This fits 'some structure but could be better organized'; the under-50-line exception for a 5 clearly does not apply.

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 explicitly states both capabilities and a trigger condition in third person, with good natural terminology. Its main limitations are a 'when' clause narrower than the skill's full scope and missing common synonyms (latency, load testing, scalability).

DimensionReasoningScore

Specificity

The description names the domain (application performance) and several concrete approaches — 'profiling, observability, and backend/frontend tuning' — matching the anchor for several specific actions with minor gaps. It is not a 5 because 'tuning' is generic and coverage of the skill's actual scope (load testing, budgets, capacity planning) is incomplete.

4 / 5

Completeness

It answers both explicitly: what — 'Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning'; when — 'Use when coordinating performance optimization across the stack.' It is not a 5 because the 'when' clause is narrower than the skill's actual scope (baselines, load tests, performance budgets), and not a 3 because the trigger guidance is explicit rather than implied.

4 / 5

Trigger Term Quality

Natural phrases users would say are present: 'application performance', 'profiling', 'optimization', 'coordinating performance optimization'. It is not a 5 because common synonyms like 'latency', 'slow', 'faster', 'scalability', or 'load testing' are missing, but coverage is good rather than partial.

4 / 5

Distinctiveness Conflict Risk

The 'coordinating... across the stack' framing carves out a distinct orchestrator niche versus single-layer skills, and the profile/observability scope is specific. It is not a 5 because 'optimize performance' overlaps with database, frontend, and infrastructure tuning skills that could claim the same trigger.

4 / 5

Total

16

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

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
Reviewed

Table of Contents

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