explain

Parse and interpret Z3 output for human consumption. Handles models, unsat cores, proofs, statistics, and error messages. Translates solver internals into plain-language explanations.

Quality

76%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.github/skills/explain/SKILL.md

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is actionable and well-structured with executable commands, a clear sequenced workflow, and error-recovery guidance. The only weakness is mild verbosity from the repeated Action/Expectation/Result framing.

Suggestions

Collapse the repetitive Action/Expectation/Result headers into tighter prose or a single step table to improve token efficiency.

Dimension	Reasoning	Score
Conciseness	The body is lean with no concept-padding, but the repeated Action/Expectation/Result scaffolding across three steps adds overhead that could be tightened, matching the 'mostly efficient but could be tightened' anchor.	2 / 3
Actionability	Provides concrete, copy-paste-ready commands ('python3 scripts/explain.py --file output.txt', '--stdin', '--debug') plus a full parameters table, matching the level-3 executable-guidance anchor.	3 / 3
Workflow Clarity	A clear three-step sequence (identify type, run explainer, interpret) with explicit error-recovery guidance ('If detection fails, re-run with an explicit --type flag') and an auto-detection fallback.	3 / 3
Progressive Disclosure	Well-organized into labeled steps and tables with a single one-level-deep bundle reference (scripts/explain.py) that exists on disk; no nested references.	3 / 3
	Total	11 / 12 Passed

Description

67%

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 clearly conveys what the skill does with concrete, domain-specific actions, but omits an explicit 'when to use it' trigger clause, capping completeness. It is distinctive within the Z3 solver-output niche.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when Z3 solver output is large, nested, or hard to read directly' to lift completeness and trigger-term quality.

Include natural user-facing phrasing variations (e.g. 'Z3 models', 'unsat core', 'solver statistics') to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Handles models, unsat cores, proofs, statistics, and error messages' and 'Translates solver internals into plain-language explanations' — matching the level-3 anchor of several specific concrete actions.	3 / 3
Completeness	The 'what' is clearly answered, but there is no explicit 'Use when...' trigger clause, so per the rubric guidelines completeness is capped at 2.	2 / 3
Trigger Term Quality	Relevant Z3-domain terms (models, unsat cores, proofs, statistics, error messages) are present, but the phrasing leans technical and lacks the broader natural-term coverage of the level-3 example.	2 / 3
Distinctiveness Conflict Risk	The 'Z3 output' niche is narrow and specific, making it unlikely to trigger for unrelated skills.	3 / 3
	Total	10 / 12 Passed

Validation

100%

Warnings & errors only

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
Path: .github/skills/explain/SKILL.md
Commit: 1c89937

Reviewed: 3 days ago

Table of Contents

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