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pseudocode-to-python-code

Convert pseudocode, algorithm descriptions, or specifications into complete, executable Python code. Handles natural language descriptions, structured pseudocode, and formal algorithm specifications. Generates production-ready code with type hints, docstrings, error handling, and test cases. Use when users need to (1) convert pseudocode to Python, (2) implement algorithms from descriptions, (3) translate algorithm specifications to code, (4) generate Python implementations from textbook pseudocode, or (5) create executable code from high-level algorithm designs.

63

Quality

74%

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tessl review fix ./skills/pseudocode-to-python-code/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

60%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 well-structured with a clear workflow, concrete templates, and properly linked, real one-level-deep bundle files. Its main liability is token efficiency: roughly a third of the content (Python idiom tutorials, error-handling boilerplate, and code-quality notes) re-teaches knowledge Claude already has, much of it duplicating the reference files.

Suggestions

Cut 'Apply Python Idioms', 'Add Error Handling', and the 'Important Notes' code-quality/testing subsections down to one-line pointers to references/python-idioms.md — Claude already knows list comprehensions, isinstance validation, and Google-style docstrings, and the reference file already covers this material.

Remove the duplicated mapping tables in 'Map to Python Constructs' (control/data structures, common operations) in favor of a single pointer to references/pseudocode-patterns.md, keeping only two or three illustrative mappings inline.

Add an explicit verification step to the workflow (e.g., 'Step 9: run the generated tests with python3 output.py and fix any failures before presenting the code') to close the feedback loop that workflow_clarity currently lacks.

DimensionReasoningScore

Conciseness

Sections 5 and 6 teach Python fundamentals Claude already knows — list comprehensions, 'total = sum(items)', enumerate/zip, isinstance checks, 'raise ValueError' boilerplate — and 'Important Notes' reiterates PEP 8 naming, 'use built-in functions when possible', and docstring conventions. This is several padded sections of unnecessary explanation (anchor 2), not merely 'some' (anchor 3); only the mapping tables and template structure genuinely add value.

2 / 5

Actionability

Concrete, mostly executable guidance throughout: complete docstring/test/main-block templates, explicit mappings like 'REPEAT-UNTIL → while True: with break' and 'Queues → collections.deque', and a worked bubble-sort pattern. It stays at 4 rather than 5 because templates contain placeholders ('result = implementation_here') and no fully copy-paste-ready end-to-end example of a real algorithm.

4 / 5

Workflow Clarity

The 8-step workflow (understand → analyze → map → generate → idioms → error handling → tests → summary) is clearly sequenced, with test generation built into the template and a mapping summary as an output checkpoint. It falls short of the score-5 anchor because there is no explicit 'run the tests and fix failures' feedback loop — verification is implied by the main block rather than stated as a checkpoint.

4 / 5

Progressive Disclosure

Structure is good: three real one-level-deep bundle files (references/pseudocode-patterns.md, references/python-idioms.md, assets/template.py), each linked at its point of use and again in a Resources section. It does not reach 5 because SKILL.md sections 3 and 5 inline condensed versions of material that already lives in the reference files — duplicated content that keeps organization a notch below 'appropriately split'.

4 / 5

Total

14

/

20

Passed

Description

88%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 that clearly states what the skill does and enumerates five explicit use-when triggers in natural user language. The only weakness is trigger-term coverage that stops just short of synonyms and file-extension terms, creating minor overlap risk with generic implementation requests.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'Convert pseudocode, algorithm descriptions, or specifications into complete, executable Python code', 'Handles natural language descriptions, structured pseudocode, and formal algorithm specifications', and 'Generates production-ready code with type hints, docstrings, error handling, and test cases' — covering the domain comprehensively, matching the top anchor rather than the score-4 anchor with 'minor gaps in coverage'.

5 / 5

Completeness

It explicitly answers both questions: three sentences state what the skill does, and 'Use when users need to (1) convert pseudocode to Python... (5) create executable code from high-level algorithm designs' gives concrete, enumerated when-triggers — a direct match for the top anchor.

5 / 5

Trigger Term Quality

Natural phrases users would say are present ('convert pseudocode to Python', 'implement algorithms from descriptions', 'textbook pseudocode'), but coverage misses common synonyms and concrete artifacts like 'Python script', '.py', or 'translate pseudocode'. This sits between the 'few natural terms missing' anchor (4) and the fully comprehensive synonym/extension coverage anchor (5).

4 / 5

Distinctiveness Conflict Risk

The core niche ('convert pseudocode to Python') is distinct with dedicated triggers, but the broad '(2) implement algorithms from descriptions' phrasing overlaps with general coding-implementation skills, keeping it at 'mostly distinct; minor overlap risk' rather than the minimal-conflict top anchor.

4 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
ArabelaTso/Skills-4-SE
Reviewed

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