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python-design

Python design patterns for CLI scripts and utilities — type-first development, deep modules, complexity management, and red flags. Use when reading, writing, reviewing, or refactoring Python files, especially in .trellis/scripts/ or any CLI/scripting context. Also activate when planning module structure, deciding where to put new code, or doing code review.

67

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

78%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 content is highly actionable and token-efficient, with concrete code and well-structured checklists. Its main weakness is progressive disclosure: everything is inlined into a single long file rather than split into clearly signaled reference files.

Suggestions

Move the 'Red Flags Quick Reference' table and the larger BAD/GOOD example blocks into separate reference files (e.g., references/red-flags.md, references/examples.md) linked from SKILL.md to improve progressive disclosure.

Trim the closing 'Strategic Investment' section or fold its key point into the existing checklists to tighten conciseness.

Consider adding a brief 'Quick start' or single canonical example near the top so the core pattern is visible before the detailed principles.

DimensionReasoningScore

Conciseness

The body is dense and high-signal with no padding about concepts Claude already knows, though a few prose sections (e.g., the closing 'Strategic Investment') could be trimmed.

4 / 5

Actionability

Abundant executable BAD/GOOD code pairs, concrete patterns (frozen dataclasses, TypedDict, NewType, match statements), a red-flags table, and checklists make the guidance fully copy-paste ready for common cases.

5 / 5

Workflow Clarity

Sequenced 'Before Writing Code' and 'During Code Review' checklists give clear guidance; this is a design-principles skill rather than a destructive/batch workflow, so explicit validate-fix-retry loops are not expected.

4 / 5

Progressive Disclosure

All ~450 lines are inlined in SKILL.md with no bundle files; sections are well-organized, but content like the red-flags table and full example sets could live in separate one-level-deep references.

3 / 5

Total

16

/

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, clearly stating what the skill does and when to activate it with concrete, natural trigger phrases. Minor gaps in trigger synonym coverage keep specificity and trigger_term_quality just below the top anchor.

Suggestions

Add the '.py' file extension and a couple of common synonyms (e.g., 'linting', 'style review') to broaden natural trigger coverage.

Consider enumerating one or two more concrete capabilities (e.g., 'naming and module boundary decisions') to reach comprehensive coverage on specificity.

DimensionReasoningScore

Specificity

Lists several concrete actions ('reading, writing, reviewing, or refactoring Python files', 'planning module structure', 'deciding where to put new code') plus named concepts, with only minor gaps versus comprehensive coverage.

4 / 5

Completeness

Explicitly answers 'what' (design patterns, type-first development, deep modules, complexity management, red flags) and 'when' with concrete 'Use when...' trigger phrases.

5 / 5

Trigger Term Quality

Natural terms like 'Python files', 'refactoring', 'code review', 'module structure', and '.trellis/scripts/' appear, but synonyms and the '.py' extension are missing.

4 / 5

Distinctiveness Conflict Risk

The 'CLI scripts and utilities' framing plus the '.trellis/scripts/' niche is mostly distinct, with only minor overlap risk against generic Python skills.

4 / 5

Total

17

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mindfold-ai/Trellis
Reviewed

Table of Contents

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