CtrlK
BlogDocsLog inGet started
Tessl Logo

android-performance

Gather and interpret Android performance evidence on an adb target using Simpleperf CPU profiles, Perfetto or Compose traces, gfxinfo frame data, dumpsys meminfo snapshots, Java heap dumps, and native allocation traces. Use when asked to profile an Android app flow, find CPU-heavy functions, diagnose jank, capture startup or frame timing evidence, compare before/after performance, explain what code is taking time, or gather memory/leak profiling artifacts.

80

Quality

100%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

100%

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

The body is a well-organized, executable guide across four profiling methodologies with concrete commands, explicit validation checkpoints, and report generation correctly offloaded to bundled scripts. It earns the top anchor on every content dimension.

DimensionReasoningScore

Conciseness

The body is dense and actionable rather than padded: each section leads with executable adb/simpleperf/perfetto commands and limits prose to domain-specific interpretation guidance (e.g. self vs. inclusive samples, profiler limits) that Claude would not reliably know. It is not the level-2 case because there is little generic concept explanation to trim.

3 / 3

Actionability

Guidance is copy-paste ready throughout — parameterized adb/simpleperf/perfetto/gfxinfo/meminfo/shark-cli commands, a complete heapprofd text-config, and explicit report-generation invocations. It matches the anchor for fully executable, specific examples.

3 / 3

Workflow Clarity

A 5-step Core Workflow is sequenced with explicit validation checkpoints: preflight debuggable/profileable check, discard-on-failure for gfxinfo, wait-for-stable-trace-size before pulling, and fallback paths (use Perfetto/gfxinfo when not debuggable). These feedback loops satisfy the top anchor and avoid the cap for missing validation.

3 / 3

Progressive Disclosure

SKILL.md stays an overview plus per-tool commands, while detailed report generation is delegated one level deep to real bundled scripts (scripts/simpleperf_hotspots.sh and scripts/heapprofd_reports.sh, both present), with clearly signaled paths and no nested reference chains.

3 / 3

Total

12

/

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.

The description is concrete, third-person, and answers both what the skill does and when to use it with explicit, natural-language triggers. It is among the strongest reference examples in specificity and trigger coverage.

DimensionReasoningScore

Specificity

Lists multiple concrete actions and instruments — "Simpleperf CPU profiles, Perfetto or Compose traces, gfxinfo frame data, dumpsys meminfo snapshots, Java heap dumps, and native allocation traces" — matching the anchor for listing several specific concrete actions.

3 / 3

Completeness

It answers both what ("Gather and interpret Android performance evidence...") and when ("Use when asked to profile...") with an explicit trigger clause, satisfying the top anchor. It is not capped at 2 because the 'Use when' trigger is present and explicit.

3 / 3

Trigger Term Quality

Natural user phrasings are well covered: "profile an Android app flow", "find CPU-heavy functions", "diagnose jank", "compare before/after performance", "explain what code is taking time", mirroring the anchor for good coverage of terms users would actually say.

3 / 3

Distinctiveness Conflict Risk

The niche is sharply scoped to Android performance profiling on an adb target with named tooling, making overlap with unrelated skills unlikely. It is clearly distinguishable and would not trigger for non-performance tasks.

3 / 3

Total

12

/

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.

Repository
openai/plugins
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.