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imperative-to-coq-model-extractor

Extract abstract mathematical models from imperative code (C, C++, Python, Java, etc.) suitable for formal reasoning in Coq. Use when the user asks to model imperative code in Coq, create Coq specifications from imperative programs, extract mathematical models for verification, or translate imperative algorithms to Coq for formal reasoning and proof.

85

1.03x
Quality

77%

Does it follow best practices?

Impact

99%

1.03x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/imperative-to-coq-model-extractor/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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 and actionable with concrete Coq code and a clear multi-step workflow, supported by a real one-level reference file. Its main weaknesses are redundant restated sections that inflate length, placeholder (`Admitted`) and broken examples that reduce executable reliability, and a verification step without an explicit fix-and-retry loop.

Suggestions

Consolidate 'Key Differences: Imperative vs Coq Model', 'Best Practices', and 'Limitations' into the existing Overview/workflow sections to remove redundant content and tighten conciseness.

Replace `Admitted` proof placeholders and fix the `sum`/`sum_aux` example so all inline Coq code is type-checkable and semantically correct, raising actionability.

Turn the 'Verify and Test' step into an explicit feedback loop (coqc fails → read error → fix → re-run `coqc` → only proceed when valid) to strengthen workflow_clarity.

DimensionReasoningScore

Conciseness

Mostly efficient but several sections are redundant — 'Key Differences: Imperative vs Coq Model', 'Best Practices', and 'Limitations' largely restate the Overview, workflow steps, and Quick Reference table, padding the body unnecessarily.

3 / 5

Actionability

Provides concrete, mostly copy-paste-ready Coq code, real `coqc` and `Compute` commands, and a Quick Reference table; gaps exist because several specifications use `Admitted`/placeholder proofs and the `sum`/`sum_aux` example has a termination/semantics mismatch.

4 / 5

Workflow Clarity

Six clearly sequenced steps (Analyze, Design, Extract, Specify, Verify, Refine) with a type-check and Compute validation step; the verify step lacks an explicit fix-and-re-validate feedback loop, leaving minor checkpoint gaps.

4 / 5

Progressive Disclosure

Good structure with an overview, a well-signaled one-level-deep reference (references/extraction_patterns.md, which exists), a Quick Reference table, and inline examples; minor gaps because substantial pattern/example content is inlined rather than split into additional reference files.

4 / 5

Total

15

/

20

Passed

Description

87%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: it states concrete capabilities and provides explicit 'Use when...' trigger guidance covering multiple natural phrasings, with a clearly distinct niche. The main weakness is mild verbosity and repetition among the trigger phrases, which keeps trigger term quality and specificity just below the top anchor.

DimensionReasoningScore

Specificity

Lists several concrete actions (extract mathematical models, model imperative code in Coq, create Coq specifications, extract models for verification, translate algorithms to Coq) with only minor gaps in coverage.

4 / 5

Completeness

Clearly and explicitly answers both what ('Extract abstract mathematical models from imperative code... suitable for formal reasoning in Coq') and when ('Use when the user asks to model imperative code in Coq...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good coverage of natural phrases users would say ('model imperative code in Coq', 'translate imperative algorithms to Coq for formal reasoning and proof'), though some trigger phrasing is slightly verbose and a few common synonyms are absent.

4 / 5

Distinctiveness Conflict Risk

A clear niche (imperative-to-Coq model extraction for formal reasoning) with distinct, specific triggers and minimal overlap risk with other skills.

5 / 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

Table of Contents

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