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python-performance-optimization

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

50

Quality

63%

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/python-performance-optimization/SKILL.md

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

SKILL.md
Quality
Evals
Security

Quality

Content

32%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.

The body is a hollow skeleton: the use-case bullets are relevant, but the instructions are generic boilerplate with no concrete profiling guidance, and the sole reference points to a missing file. Structure and brevity are adequate, yet the skill delivers almost no actionable content on its own.

Suggestions

Restore or inline the missing `resources/implementation-playbook.md`, or replace the deferral with concrete guidance in the body, e.g. actual cProfile invocations (`python -m cProfile -s cumulative app.py`) and a named memory profiler workflow.

Replace the generic instruction bullets with a sequenced optimization workflow (profile → identify hotspots → optimize → re-profile to verify) that includes an explicit validation step such as comparing before/after timings.

Replace the tautological 'Do not use' bullets with genuine boundary guidance (e.g., defer to a database-specific skill for query tuning) so those tokens earn their place.

DimensionReasoningScore

Conciseness

The body is lean (~30 lines) with no over-explanation of concepts Claude already knows, but the "Comprehensive guide to profiling..." intro sentence and the tautological "Do not use" bullets ("The task is unrelated to python performance optimization") are minor trims. This fits anchor 4 (efficient with minor trimming possible) rather than 5, where every token earns its place.

4 / 5

Actionability

The Instructions are pure boilerplate — "Clarify goals, constraints, and required inputs", "Apply relevant best practices and validate outcomes", "Provide actionable steps and verification" — with no concrete code, commands, or tool invocations, and all substance deferred to a file that does not exist in the bundle. This matches anchor 1 (entirely abstract, only describes rather than instructs); not 2, because even high-level domain-specific hints are absent — these bullets could appear in any skill unchanged.

1 / 5

Workflow Clarity

A rough sequence is implied (clarify goals → apply practices → validate outcomes), but the steps are poorly defined, no validation checkpoints or methods are given, and the ordering is generic. This matches anchor 2 (rough sequence present but many gaps, validation absent); not 3, which requires actually listed steps even if checkpoints are missing.

2 / 5

Progressive Disclosure

The body defers all detailed patterns to `resources/implementation-playbook.md`, but no such file or directory exists in the bundle, leaving the skill with no substantive content behind the reference. Scored against the actual bundle structure, this matches anchor 2 (minimal structure, broken/dangling reference); not 4, because the clearly signaled reference does not resolve to real content, and not 1, because the reference is one level deep and clearly surfaced rather than nested.

2 / 5

Total

9

/

20

Passed

Description

78%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 description following the recommended what-plus-when pattern with natural trigger phrases and clear Python scoping. Its main weaknesses are a short action list softened by vague "best practices" padding and a few missing common trigger synonyms.

DimensionReasoningScore

Specificity

The description names the domain and two concrete actions ("Profile and optimize Python code using cProfile, memory profilers") with named tools, but "performance best practices" is vague padding and the action list is not comprehensive. This matches anchor 3 (domain plus 1-2 concrete actions) rather than 4, which requires several distinct specific actions.

3 / 5

Completeness

It explicitly answers both what ("Profile and optimize Python code using cProfile, memory profilers") and when ("Use when debugging slow Python code, optimizing bottlenecks, or improving application performance") with concrete trigger phrases, closely matching the anchor 5 example. Not 4, since the 'when' clause is fully explicit rather than merely serviceable.

5 / 5

Trigger Term Quality

"Use when debugging slow Python code, optimizing bottlenecks, or improving application performance" contains natural phrases users would actually say. It misses common variations like "speed up", "latency", or "memory leak", placing it at anchor 4 (good coverage, a few natural terms missing) rather than 5.

4 / 5

Distinctiveness Conflict Risk

Explicitly scoped to Python performance with distinct triggers (profiling, cProfile, bottlenecks), so it is unlikely to fire for unrelated skills. Minor overlap risk remains from the broad phrase "improving application performance", which could collide with adjacent performance-tuning skills, matching anchor 4 rather than 5.

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

Path

plugins/AI-Agents-Safe-Coding-Skills/skills/python-performance-optimization/SKILL.md

Reviewed

Table of Contents

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