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

67%

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

Quality

Content

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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-organized and brief, but it relies on generic process platitudes instead of concrete profiling/optimization guidance, and its only external reference points to a file that is missing. This leaves Claude without actionable, executable instructions despite the strong description.

Suggestions

Replace the abstract instruction bullets with executable examples, e.g. `python -m cProfile -o profile.out script.py` and a memory_profiler snippet, so guidance is copy-paste ready rather than 'apply best practices'.

Spell out a concrete profile -> identify hotspot -> optimize -> re-measure workflow with an explicit validation checkpoint (compare timings before and after) instead of merely saying 'validate outcomes'.

Create the missing 'resources/implementation-playbook.md' or remove the broken reference, since both the Instructions and Resources sections point to a file that does not exist.

Dimension	Reasoning	Score
Conciseness	The body is short and sectioned, but includes low-value filler such as 'Comprehensive guide to profiling, analyzing, and optimizing...' and generic 'Do not use this skill when' bullets ('The task is unrelated to python performance optimization'), plus platitudes like 'Apply relevant best practices and validate outcomes.' It is not a 3 because several tokens restate what Claude already knows or repeat the description; it is not a 1 because it never bloats into explaining basic concepts.	2 / 3
Actionability	The Instructions are abstract — 'Apply relevant best practices and validate outcomes', 'Provide actionable steps and verification' — with no concrete code, commands, or named techniques (e.g., no cProfile invocation, no memory_profiler usage), and the referenced playbook does not exist. It matches the 'describes rather than instructs' anchor; it is not a 2 because there is no concrete or even pseudocode-level guidance to build on.	1 / 3
Workflow Clarity	A loose sequence is implied (clarify goals -> apply practices -> validate -> open playbook), and 'validate outcomes' is mentioned, but there are no concrete checkpoints or a real profile->optimize->re-measure loop. It is not a 3 because validation is only named, not made explicit; it is not a 1 because some sequenced steps and a validation mention are present.	2 / 3
Progressive Disclosure	Sections are well organized and the playbook reference is one level deep and signaled in two places, but the referenced 'resources/implementation-playbook.md' does not exist, so navigation is broken. It is not a 3 because the signaled reference leads nowhere; it is not a 1 because the body itself is not a monolithic wall of text and is reasonably structured.	2 / 3
	Total	7 / 12 Passed

Description

100%Weight 40%Scale 1-3

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, concise description that clearly states concrete capabilities with named tools and provides explicit, natural trigger guidance. It scores at the top of the scale across all dimensions with no over-claims or fluff.

Dimension	Reasoning	Score
Specificity	Names concrete actions and tools — 'Profile and optimize Python code using cProfile, memory profilers, and performance best practices' — listing multiple specific capabilities rather than vague language, matching the 'lists multiple specific concrete actions' anchor. It is not a 2 because it goes beyond naming only a domain and a couple of actions to enumerate specific profilers and practices.	3 / 3
Completeness	It explicitly answers both 'what' (profile and optimize Python code with named tools) and 'when' via an explicit 'Use when...' clause, mirroring the anchor example. It is not a 2 because the trigger guidance is explicit rather than missing or merely implied.	3 / 3
Trigger Term Quality	The 'Use when debugging slow Python code, optimizing bottlenecks, or improving application performance' clause supplies natural phrases a user would actually say, with several relevant variations. It is above a 2 (which has only one relevant keyword) because it offers three distinct natural trigger phrases rather than a single generic term.	3 / 3
Distinctiveness Conflict Risk	Scoped to Python performance with distinct triggers (slow Python code, bottlenecks), giving it a clear niche unlikely to fire for unrelated skills. It is not a 2 because the Python-specific framing and concrete profiling triggers meaningfully narrow the scope.	3 / 3
	Total	12 / 12 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: e63f7dd

Reviewed: 13 days ago

Table of Contents

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