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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.

52

Quality

58%

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

Quality

Content

28%Scale 1-5

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

This skill is essentially a placeholder that delegates all substantive content to a referenced file that doesn't appear to exist in the bundle. The body provides no concrete profiling commands, no code examples, no specific tool usage (cProfile, memory_profiler, etc.), and no actionable workflow despite the description promising these. The instructions are generic enough to apply to any skill, not specifically to Python performance optimization.

Suggestions

Add concrete, executable examples of cProfile usage, memory profiling commands, and common optimization patterns directly in the skill body (e.g., `python -m cProfile -s cumulative script.py`)

Replace the generic instructions ('Clarify goals, constraints, and required inputs') with a specific profiling workflow: profile → identify bottleneck → apply optimization → verify improvement with before/after measurements

Include at least one complete code example showing a common optimization pattern (e.g., replacing a loop with a list comprehension, using generators for memory efficiency)

Either provide the referenced `resources/implementation-playbook.md` bundle file or inline the essential content that makes this skill actionable

DimensionReasoningScore

Conciseness

The 'Use this skill when' and 'Do not use this skill when' sections are somewhat padded with obvious items Claude would already know. The instructions section is brief but generic. The overall content is not severely verbose but includes unnecessary enumeration.

3 / 5

Actionability

The skill provides no concrete code, commands, or executable guidance. The instructions are entirely vague ('Clarify goals, constraints, and required inputs', 'Apply relevant best practices') with no specific profiling commands, code examples, or tool usage patterns.

1 / 5

Workflow Clarity

There is a rough implied sequence (clarify goals → apply practices → validate → provide steps) but it's extremely generic with no specific steps, no validation checkpoints, and no concrete workflow for profiling or optimization tasks.

2 / 5

Progressive Disclosure

The skill references `resources/implementation-playbook.md` for detailed patterns, which is appropriate progressive disclosure. However, no bundle files are provided to verify the reference exists, and the main file provides almost no substantive content itself, making the reference feel like a crutch rather than a complement.

3 / 5

Total

9

/

20

Passed

Description

87%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.

This is a strong skill description that clearly communicates both what the skill does and when to use it. It names specific tools and concrete use cases, making it easy for Claude to select appropriately. Minor improvements could include additional trigger term synonyms like 'speed up' or 'memory leak' and more specific optimization actions.

DimensionReasoningScore

Specificity

Lists several specific tools (cProfile, memory profilers) and actions (profile, optimize), plus mentions 'performance best practices.' However, it could be more comprehensive by listing additional concrete actions like line profiling, benchmarking, or specific optimization techniques.

4 / 5

Completeness

Clearly answers both 'what' (profile and optimize Python code using cProfile, memory profilers, and performance best practices) and 'when' (debugging slow Python code, optimizing bottlenecks, or improving application performance) with explicit trigger phrases.

5 / 5

Trigger Term Quality

Includes strong natural trigger terms like 'slow Python code', 'optimizing bottlenecks', 'performance', 'cProfile', and 'memory profilers.' Missing some natural variations users might say such as 'speed up', 'memory leak', 'profiling', 'runtime', or 'latency.'

4 / 5

Distinctiveness Conflict Risk

Highly distinct with a clear niche: Python performance profiling and optimization. The specific mention of cProfile, memory profilers, and performance bottlenecks makes it unlikely to conflict with general Python coding skills or other language-specific skills.

5 / 5

Total

18

/

20

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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