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

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

Quality

Content

43%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 token-efficient and well-sectioned but substantively hollow: it offers only generic process guidance with no executable profiling steps, and its sole detailed reference points to a missing file. The skill needs concrete cProfile/memory-profiler examples and a real measure→optimize→re-measure workflow.

Suggestions

Replace the abstract Instructions with an executable workflow, e.g. `python -m cProfile -o out.prof script.py` then `snakeviz out.prof`, and a memory example such as `python -m memory_profiler script.py`, so the skill is copy-paste actionable.

Add explicit validation checkpoints: profile a baseline → optimize the identified hotspot → re-profile to confirm the improvement, with a guard to stop if no speedup is measured.

Create the referenced `resources/implementation-playbook.md` (or remove the reference) so the one-level-deep disclosure actually resolves; currently the only detailed pointer is a dead link.

Dimension	Reasoning	Score
Conciseness	The body is lean with no concept-explanation padding Claude already knows, though the 9-bullet "Use this skill when" list duplicates the description and the Instructions bullets are generic boilerplate that could be trimmed.	4 / 5
Actionability	"Apply relevant best practices and validate outcomes" and "Provide actionable steps and verification" are pure abstraction with no code, commands, or concrete steps; the only specific pointer is to a playbook file that does not exist.	2 / 5
Workflow Clarity	The "clarify → apply → validate" sequence is generic boilerplate rather than a real profile→identify-bottleneck→optimize→re-profile workflow, and validation is abstract with no checkpoints.	2 / 5
Progressive Disclosure	Sections are reasonably organized and the playbook reference is one level deep and clearly signaled, but `resources/implementation-playbook.md` does not exist, so the disclosure path leads nowhere and navigation breaks.	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.

A strong description: it concisely pairs concrete capabilities with explicit, natural-language triggers and a clear Python-specific niche. The only soft spot is trigger synonym coverage and slight breadth in the performance phrasing.

Dimension	Reasoning	Score
Specificity	"Profile and optimize Python code using cProfile, memory profilers" names the domain, concrete actions, and specific tools; "performance best practices" is generic, leaving minor gaps rather than comprehensive coverage.	4 / 5
Completeness	It explicitly states what it does (profile/optimize with named tools) and when to use it (concrete "Use when" trigger phrases), satisfying both halves.	5 / 5
Trigger Term Quality	"debugging slow Python code, optimizing bottlenecks, improving application performance" are natural phrases users say, but common synonyms like "speed up" or "latency" are missing.	4 / 5
Distinctiveness Conflict Risk	The Python-specific niche and named tooling make it mostly distinct, though "improving application performance" is broad enough to risk minor overlap with general performance skills.	4 / 5
	Total	17 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
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-claude/skills/python-performance-optimization/SKILL.md
Commit: 57c135b

Reviewed: 2 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.