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.

Quality

63%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/python-performance-optimization/SKILL.md

Quality

Content

42%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 well-structured and brief, but it is almost entirely abstract boilerplate with no concrete profiling code or commands, and its single external reference points to a file that does not exist. It reads as a template stub rather than executable guidance.

Suggestions

Replace the generic Instructions bullets with a concrete profiling workflow, e.g. a cProfile command (`python -m cProfile -o prof.out script.py`) and how to inspect it with pstats/snakeviz.

Create the referenced 'resources/implementation-playbook.md' or remove the dangling reference, since progressive_disclosure currently points to a non-existent file.

Tighten the boilerplate 'Do not use this skill when' section and the vague Instructions into specific, actionable guidance that assumes Claude's competence.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean, but the generic 'Instructions' bullets ('Clarify goals...', 'Apply relevant best practices...') and the redundant two-item 'Do not use this skill when' section are boilerplate padding Claude does not need.	3 / 5
Actionability	There is no executable code, no commands, and no concrete profiling recipe — only high-level hints and a pointer to 'resources/implementation-playbook.md', which does not exist in the bundle, leaving minimal real guidance.	2 / 5
Workflow Clarity	A rough sequence is implied (clarify → apply → verify) and 'validate outcomes'/'verification' is mentioned, but there are no explicit checkpoints or error-recovery feedback loops for risky operations like profiling production apps.	3 / 5
Progressive Disclosure	Sections are organized and a one-level reference to 'resources/implementation-playbook.md' is signaled in both Instructions and Resources, but the referenced file is absent from the bundle, so navigation leads to a dead end.	3 / 5
	Total	11 / 20 Passed

Description

83%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.

The description is strong: it states concrete capabilities and tools, gives explicit 'Use when' triggers, and carves out a clear niche. The only weakness is a slightly broad 'improving application performance' phrasing and the generic 'best practices' term.

Dimension	Reasoning	Score
Specificity	Names the domain plus several concrete actions/tools ('Profile and optimize Python code using cProfile, memory profilers, and performance best practices'), with only the generic 'best practices' phrase leaving a minor gap.	4 / 5
Completeness	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.	5 / 5
Trigger Term Quality	Includes natural phrases users would say ('debugging slow Python code', 'optimizing bottlenecks', 'improving application performance'), though common synonyms like 'speed up' or 'latency' are absent.	4 / 5
Distinctiveness Conflict Risk	Has a clear Python-performance niche with distinct triggers, but the broad 'improving application performance' phrase creates minor overlap risk with general performance or debugging skills.	4 / 5
	Total	17 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: rmyndharis/antigravity-skills
Path: skills/python-performance-optimization/SKILL.md
Commit: 3eff4af

Reviewed: 22 days ago

Table of Contents

Discovery Implementation Validation

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.