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encode

Translate constraint problems into SMT-LIB2 or Z3 Python API code. Handles common problem classes including scheduling, graph coloring, arithmetic puzzles, and verification conditions.

68

Quality

83%

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SKILL.md
Quality
Evals
Security

Quality

Content

88%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.

A lean, highly actionable skill body: executable commands, a complete parameter table, and a three-step workflow with an explicit validation feedback loop. Its only structural limitation is that all guidance lives inline with no reference files for deeper per-theory material.

DimensionReasoningScore

Conciseness

The body is dense and assumes competence: a theory-selection table, two executable commands, and a parameters table with no padding or explanation of known concepts. The repeated Action/Expectation/Result scaffolding around each step adds words that could be trimmed, keeping it just below the lean 5 anchor.

4 / 5

Actionability

Copy-paste-ready commands with realistic example problems in both formats ("python3 scripts/encode.py --problem ... --format smtlib2") plus a complete parameter table covering every flag. The referenced scripts/encode.py exists in the bundle, so the commands are genuinely executable.

5 / 5

Workflow Clarity

Three clearly sequenced steps with an explicit validation checkpoint (Step 3 syntax check via `z3 -in` parse-only mode), a feedback loop ("On parse error: fix the reported line and re-run"), and conditional routing of results (smtlib2 to solve; python executed directly). This matches the top anchor including error recovery.

5 / 5

Progressive Disclosure

Well-organized sections with the only bundle file (scripts/encode.py) clearly referenced and verified to exist; nothing that clearly belongs in a separate reference file is inlined. The body exceeds the ~50-line simple-skill threshold and offers no one-level-deep reference files for extended material (e.g., per-theory encoding patterns), so it sits at 'good structure' rather than the exemplary 5.

4 / 5

Total

18

/

20

Passed

Description

78%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 concise, specific, third-person description with good domain keywords and a clear niche. Its main gap is the absence of an explicit "Use when..." trigger clause, relying instead on an implied trigger via the enumerated problem classes.

DimensionReasoningScore

Specificity

"Translate constraint problems into SMT-LIB2 or Z3 Python API code" names two concrete outputs, and "scheduling, graph coloring, arithmetic puzzles, and verification conditions" enumerates specific coverage areas. Falls short of 5 because it does not mention companion capabilities (solving, proving, optimizing) that the body reveals, leaving minor coverage gaps.

4 / 5

Completeness

The 'what' is explicit (translate constraint problems into SMT-LIB2 or Z3 Python code), and 'when' is conveyed through the enumerated problem classes ("Handles common problem classes including scheduling, graph coloring..."). Not a 5 because there is no explicit "Use when..." clause; the trigger guidance is inferred from the class list rather than stated directly, and a strict reading of the cap guideline could place this at 3.

4 / 5

Trigger Term Quality

Strong natural keywords a user would say: "SMT-LIB2", "Z3", "constraint problems", "scheduling", "graph coloring", "verification conditions". Missing a few common synonyms such as "SAT", "solver", "constraint solving", or "encode" phrasing users often use, so not a 5.

4 / 5

Distinctiveness Conflict Risk

"SMT-LIB2", "Z3", and the named problem classes occupy a clear niche with distinct triggers; minimal overlap risk with adjacent skills (the body's references to solve/prove/optimize hint at sibling skills, but the description itself stays squarely in encoding).

5 / 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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
Z3Prover/z3
Reviewed

Table of Contents

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