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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

73%

Does it follow best practices?

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

Quality

Content

67%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 unusually concrete per-step prompts, explicit validation and rollback guardrails, and good use/do-not-use scoping. The main costs are the padded extended-thinking narrative, repetitive delegation scaffolding, and a fully inlined 160-line body that would benefit from splitting detailed prompts into reference files.

Suggestions

Delete the bracketed '[Extended thinking: ...]' paragraph — it narrates what the workflow does rather than instructing, and Claude can infer the strategy from the phase structure itself.

Move the 13 detailed subagent prompts (or the Example Success Criteria tables) into a one-level-deep reference file (e.g., references/prompts.md), keeping SKILL.md as a lean overview with per-phase summaries.

Add an explicit feedback loop after Phase 4, e.g. 'If regression tests or load tests miss the agreed targets, return to the relevant optimization phase with the failing metrics as context' — this would complete the validate→fix→retry cycle.

DimensionReasoningScore

Conciseness

Most of the body is dense, operative instruction (13 concrete subagent prompts, config options, success criteria), but the bracketed "[Extended thinking: ...]" paragraph is a ~90-word narrative summary that adds nothing Claude cannot infer, and the 13-fold repetition of the 'Use Task tool with subagent_type' scaffolding plus the redundant Example section could be tightened. This matches 'mostly efficient but includes some unnecessary explanation or could be trimmed' rather than the noticeably-padded anchor below.

3 / 5

Actionability

Every phase step supplies a concrete subagent type and a copy-paste-ready prompt naming specific tools ("k6/Gatling/Artillery", "DataDog/New Relic/Dynatrace", "Redis/Memcached", "OpenTelemetry"), and the body hedges delegation appropriately ("verify that they exist in the client, or perform the corresponding analysis directly"). It is not a 5 because placeholders like "{context_from_phase_1}" have no defined substitution mechanism and no literal executable commands (e.g., an actual k6 invocation) are given.

4 / 5

Workflow Clarity

The five phases with 13 numbered steps, per-step Context and Output fields, and a dedicated validation phase (load testing, regression tests, "automatic rollback triggers") form a clear sequence with most checkpoints present. It falls short of 5 because there is no explicit error-recovery feedback loop (e.g., 'if regression tests fail, return to phase 2') and the four-step Instructions summary restates the phases at a vague altitude.

4 / 5

Progressive Disclosure

The body has no bundle files but is well organized with clear section headers (Use/Do not use, Instructions, Safety, five phases, Configuration, Success Criteria, Limitations) and each step is scannable via its Prompt/Context/Output fields. It is not a 5 because at ~160 lines everything is inlined — the 13 detailed delegation prompts and the success-criteria tables are natural candidates for one-level-deep reference files that would keep SKILL.md a lean overview.

4 / 5

Total

15

/

20

Passed

Description

67%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 what the skill does and when to use it, with concrete capability areas and an explicit trigger clause. Its main weakness is narrow trigger coverage — it misses the natural phrases users say for performance problems (slow, latency, speed up, load testing) — which caps trigger term quality and completeness below the top anchors.

Suggestions

Broaden the 'Use when' clause with natural user phrasings, e.g. 'Use when the user reports slow responses, high latency, or scalability concerns, asks to speed up an app, or needs load testing or capacity planning'.

Include common synonym keywords such as 'latency', 'throughput', 'load testing', and 'scalability' so the description matches how users actually phrase performance problems.

Add one or two more granular actions (e.g., 'establish baselines', 'set up monitoring and alerts') to lift specificity from area-level to operation-level coverage.

DimensionReasoningScore

Specificity

The description lists several concrete capability areas — "profiling, observability, and backend/frontend tuning" — beyond just naming the performance domain. It falls short of a 5 because the actions stay at the area level ("tuning") rather than enumerating specific operations, matching the anchor 'several specific actions; minor gaps in coverage'.

4 / 5

Completeness

Both parts are explicitly present: the 'what' ("Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning") and an explicit 'when' ("Use when coordinating performance optimization across the stack"). It is not a 5 because the 'when' clause covers a single narrow scenario and omits broader concrete triggers (e.g., slow endpoints, load testing, capacity planning).

4 / 5

Trigger Term Quality

Relevant keywords like "profiling", "observability", and "performance optimization" appear, and the trigger phrase "Use when coordinating performance optimization across the stack" is natural for orchestration requests. However, common user variations such as "app is slow", "latency", "speed up", "load testing", or "scalability" are missing, matching the anchor 'some relevant keywords but missing common variations or synonyms' rather than the good-coverage anchor above.

3 / 5

Distinctiveness Conflict Risk

The cross-stack framing ("end-to-end application performance", "across the stack") carves out a distinct orchestration niche versus single-layer skills, and the trigger reinforces it. It is not a 5 because the broad performance domain still overlaps with dedicated frontend-, database-, or observability-focused skills whose requests could plausibly match this trigger.

4 / 5

Total

15

/

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

sickn33/agentic-awesome-skills

Path

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

Reviewed

Table of Contents

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