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testland/flame-graph-analyzer

Reads CPU flame-graph output from py-spy (Python), async-profiler (JVM), Go pprof, or Node.js `perf_hooks` / clinic.js: identifies the hot path (top sample-time frames), classifies the bottleneck (CPU-bound vs lock contention vs allocator pressure), and proposes the next investigation step. Use when a perf regression is bisected to a commit but the hot path inside it is unclear; for tail-latency percentiles use latency-percentile-analyzer, for GC pauses specifically use jvm-gc-tuning, and for a slow SQL hot path use db-query-plan-analyzer.

80

Quality

100%

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SecuritybySnyk

Passed

No findings from the security scan

Overview
Quality
Evals
Security
Files

Quality

Content

100%

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

A well-structured, actionable skill body: lean prose, executable commands, a full worked example, a sequenced workflow with a re-profile validation step, and clean one-level-deep progressive disclosure to a real reference file. No significant weaknesses.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — commands, a signature table, a worked example, and an anti-patterns table, with no padding explaining general programming concepts Claude already knows.

3 / 3

Actionability

Provides fully executable, copy-paste-ready commands ('sort -k2 -n -r folded.txt | head -5', 'npx clinic flame -- node app.js') plus a complete worked example with a concrete output template and specific remediation library names.

3 / 3

Workflow Clarity

The 'How to use' section gives a clearly sequenced 5-step process with an explicit validation checkpoint — step 5 re-profiles to confirm the delta closes before handing off — forming a verify-then-hand-off feedback loop.

3 / 3

Progressive Disclosure

SKILL.md is a clear overview that points one level deep to references/capturing-and-interpreting-flame-graphs.md (a real, present file) for capture commands, extended cases, and the remediation catalog, with well-signaled links.

3 / 3

Total

12

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12

Passed

Description

100%

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, third-person description that states concrete capabilities, gives an explicit 'Use when' trigger, and actively de-conflicts against sibling skills. It is concise yet complete with no fluff or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'identifies the hot path (top sample-time frames)', 'classifies the bottleneck (CPU-bound vs lock contention vs allocator pressure)', and 'proposes the next investigation step' — matching the multiple-specific-actions anchor.

3 / 3

Completeness

Clearly answers both what (reads/classifies/proposes) and when via the explicit 'Use when a perf regression is bisected to a commit but the hot path inside it is unclear' clause.

3 / 3

Trigger Term Quality

Natural domain terms a user would say ('flame-graph', 'perf regression', 'hot path', 'bottleneck', 'CPU-bound') appear alongside the tool names, giving good coverage rather than pure jargon.

3 / 3

Distinctiveness Conflict Risk

A clear niche (flame-graph hot-path analysis) with explicit de-confliction routing to latency-percentile-analyzer, jvm-gc-tuning, and db-query-plan-analyzer, making wrong-skill triggering unlikely.

3 / 3

Total

12

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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.

Reviewed

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