outlines

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

Quality

66%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

High

Do not use without reviewing

Fix and improve this skill with Tessl

tessl review fix ./backend/cli/skills/llm-tools/outlines/SKILL.md

Quality

Content

65%

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

The body is highly actionable with executable examples throughout, but it is verbose and repetitive, functions as a reference catalog without workflow checkpoints, and keeps detail in SKILL.md that should live in its reference files.

Suggestions

Reduce repetition by loading the model once in a setup snippet and reusing it, or by pushing repeated backend-specific config into references/backends.md.

Move the six 'Common Patterns' and detailed backend configuration into references/examples.md and references/backends.md, leaving SKILL.md as a concise overview with inline 'See X for Y' links.

Add validation/error-recovery guidance for batch generation (e.g. catching per-item failures and retrying) to give the multi-step operations explicit feedback loops.

Dimension	Reasoning	Score
Conciseness	Content is mostly actionable rather than explaining known concepts, but it is heavily padded — the model-loading line `outlines.models.transformers('microsoft/Phi-3-mini-4k-instruct')` repeats across nearly every example and overlapping patterns/backends could be consolidated or moved to references.	2 / 3
Actionability	Provides fully executable, copy-paste-ready code for every generator, backend, and pattern with concrete prompts and expected outputs, matching the 'fully executable code/commands' anchor.	3 / 3
Workflow Clarity	The 'How it works' section offers a clear 4-step sequence, but the skill is a feature catalog with no validation checkpoints or error-recovery feedback loops, and the batch-processing pattern performs repeated generation with no verification.	2 / 3
Progressive Disclosure	Three real reference files (json_generation.md, backends.md, examples.md) exist and are listed in 'See Also', but they are only signaled at the very end rather than inline, and large blocks of content (full backend configs, all six patterns) that belong in references are inlined in SKILL.md.	2 / 3
	Total	9 / 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 is specific and clearly distinguishes Outlines via concrete capabilities and a named niche, but it omits an explicit 'Use when' trigger and uses technical terms over natural user phrasings.

Suggestions

Append an explicit trigger clause, e.g. 'Use when generating guaranteed-valid JSON/XML from local models, constraining outputs to Pydantic schemas or regex, or needing zero-overhead structured generation.'

Add natural user-facing terms like 'structured output', 'valid JSON', and 'JSON schema' alongside the existing technical vocabulary.

Lead with the core action ('Generate guaranteed-valid structured output') before enumerating backends to improve trigger discoverability.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Guarantee valid JSON/XML/code structure during generation', 'use Pydantic models for type-safe outputs', 'support local models (Transformers, vLLM)', 'maximize inference speed' — matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Clearly states what the skill does but lacks any explicit 'Use when...' trigger for when Claude should invoke it; per guidelines a missing explicit trigger caps completeness at 2.	2 / 3
Trigger Term Quality	Contains relevant domain terms (JSON, XML, Pydantic, Transformers, vLLM) but leans technical and misses common user phrasings like 'structured output', 'valid JSON', or 'schema generation'.	2 / 3
Distinctiveness Conflict Risk	Tied to a named niche ('Outlines - dottxt.ai's structured generation library') with concrete, distinctive features, making it unlikely to trigger for the wrong skill.	3 / 3
	Total	10 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (654 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/llm-tools/outlines/SKILL.md
Commit: 52845c3

Reviewed: about 14 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.