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optimize-code

Analyze and optimize Python code performance in critical paths

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./.claude/skills/optimize-code/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 strong, highly actionable optimization playbook with a clear phased workflow and real validation checkpoints (baseline, incremental benchmarking, variance checks, test runs, final comparison). Its main weaknesses are inlined content that belongs in reference files, a few explanatory detours into concepts Claude already knows, and placeholder benchmark templates.

Suggestions

Move the project-specific test-data table (section 3.6) and the optimization-pattern catalog (2.1-2.6) into a references/ file, keeping SKILL.md as a lean overview with clearly signaled links.

Trim known-concept explanations — the recursion 'Benefits:' list and __slots__ percentage statistics — to the decision rule only; Claude already knows these trade-offs.

Replace placeholder bodies in the dedicated-benchmark example (variant_original(): ...) with runnable stubs, and add an explicit failure-recovery step for when tests fail or a benchmark regresses.

DimensionReasoningScore

Conciseness

The body is dense and mostly token-efficient, but includes unnecessary explanation Claude already knows (recursion-conversion 'Benefits:' list, __slots__ memory/attribute-speed statistics) and repeats timeit patterns across sections 1.4 and 3.2. Fits the 'mostly efficient but could be tightened' anchor; not 4 because several sections could be trimmed without losing guidance.

3 / 5

Actionability

Mostly executable guidance: bad/good code pairs, copy-paste timeit one-liners, bash benchmark loops, and a concrete report template. Minor gaps keep it from 5 — the dedicated-benchmark example uses 'variant_original(): ...' placeholder bodies and the final comparison uses '/path/to/main' pseudo-paths.

4 / 5

Workflow Clarity

A clear five-phase sequence with most checkpoints present: baseline benchmark before changes, one change at a time with benchmarking after each, 3-run variance checking, test verification, and a final devel-vs-optimized comparison. Not 5 because there is no explicit error-recovery loop (e.g. what to do when tests fail or a benchmark regresses).

4 / 5

Progressive Disclosure

Well-sectioned single file, but no bundle files exist and all content is inlined in ~285 lines, including a project-specific test-data table and a full optimization-pattern catalog that would fit reference files. Matches 'some structure but content that should be separate is inline'; not 2 because sections are well organized and nothing is buried.

3 / 5

Total

14

/

20

Passed

Description

53%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 concise and concrete about what the skill does, with a distinct Python-performance niche, but it completely lacks a 'when to use' trigger clause and natural trigger synonyms. Adding an explicit 'Use when...' clause with user-facing phrases would lift both completeness and trigger quality.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks to speed up, optimize, or profile Python code, or mentions slow code, hot paths, or performance in critical sections.'

Include natural synonyms users actually say — 'speed up', 'make it faster', 'hot path', 'profile' — alongside 'optimize' and 'critical paths'.

Optionally list 1-2 of the skill's signature activities (e.g. 'benchmarks before and after each change') to raise specificity from domain-plus-two-actions to several specific actions.

DimensionReasoningScore

Specificity

Names the domain ('Python code performance in critical paths') and two concrete actions ('Analyze and optimize'), matching the 'domain plus 1-2 concrete actions, not comprehensive' anchor. Not 4 because it omits specific activities like benchmarking, profiling, or inlining that the skill actually performs.

3 / 5

Completeness

Has a clear 'what' but no 'Use when...' clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines. Not 2 because the 'what' is concrete rather than vague.

3 / 5

Trigger Term Quality

Relevant keywords exist ('Python', 'optimize', 'performance', 'critical paths') but common natural variations users would say — 'speed up', 'make it faster', 'slow', 'profile' — are missing, matching the 'some relevant keywords but missing variations/synonyms' anchor.

3 / 5

Distinctiveness Conflict Risk

'Python code performance in critical paths' carves a clear niche distinct from general code-review or refactoring skills, with only minor overlap risk with profiling/linting skills. Not 5 because without explicit trigger phrases it could still fire for generic 'make this better' code requests.

4 / 5

Total

13

/

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

dlt-hub/dlt

Path

.claude/skills/optimize-code/SKILL.md

Reviewed

Table of Contents

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