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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 the latency-percentiles reference in k6-load-testing, and for a slow SQL hot path use db-query-plan-analyzer.

72

Quality

91%

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SecuritybySnyk

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Overview
Quality
Evals
Security
Files

Quality

Content

78%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.

Well-structured, actionable content with concrete commands, a worked example, and clean one-level-deep progressive disclosure to a verified reference file. Slightly verbose in places and the workflow's error-recovery branch could be more explicit.

Suggestions

Tighten the worked example's Hypothesis/Recommended-next-step prose - the sample-share table already conveys the rank-1+rank-5 sum, so the arithmetic narrative is redundant.

Make the re-profile validation step an explicit feedback loop: 'if the dominant leaf's share did not drop as expected, re-examine the classification' rather than a single confirm step.

Collapse the inline category table's interpretation prose where the 'Signature in the flame graph' cell repeats the frame names already implied by the category.

DimensionReasoningScore

Conciseness

Mostly efficient and assumes Claude's competence (defers to a Brendan Gregg link rather than re-explaining flame graphs), but the worked example and category table carry some explanatory padding that could be trimmed.

4 / 5

Actionability

Provides concrete executable commands ('sort -k2 -n -r folded.txt | head -5', 'npx clinic flame -- node app.js') and a complete worked example with specific remediation; minor gaps where classification guidance is interpretive rather than executable.

4 / 5

Workflow Clarity

A clear 5-step 'How to use' sequence with an explicit re-profile validation checkpoint ('Re-profile after the change to confirm the delta, then hand off to perf-budget-gate'); the feedback loop is present but error-recovery branching is implicit.

4 / 5

Progressive Disclosure

A concise overview that repeatedly and clearly signals one-level-deep references to the real references/capturing-and-interpreting-flame-graphs.md file for per-runtime wiring, the remediation catalog, and extended cases.

5 / 5

Total

17

/

20

Passed

Description

100%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 strong, third-person description that names concrete capabilities, gives an explicit 'Use when' trigger, and proactively disambiguates from adjacent skills. It is comprehensive without padding.

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' - with comprehensive coverage across four runtimes.

5 / 5

Completeness

Explicitly answers both 'what' (reads/identifies/classifies/proposes) and 'when' via a concrete 'Use when a perf regression is bisected to a commit but the hot path inside it is unclear' trigger, with negative-scope guidance for adjacent cases.

5 / 5

Trigger Term Quality

Natural trigger phrases a user would say ('perf regression is bisected to a commit', 'hot path', 'flame graph') plus tool names (py-spy, async-profiler, pprof, clinic.js) and explicit redirect terms; comprehensive coverage.

5 / 5

Distinctiveness Conflict Risk

Clear niche (CPU flame-graph analysis) with explicit redirects to sibling skills (k6-load-testing for tail-latency, db-query-plan-analyzer for SQL) minimizing overlap and conflict.

5 / 5

Total

20

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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