---

name: outlines description: "Operate the Outlines structured-output library across output types, local models, provider integrations, and prompt workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Outlines Repo Skill

Use this repo skill when a task involves Outlines as a Python package for structured outputs from large language models: defining schemas or regex/CFG constraints, wrapping local or hosted models, rendering prompt templates, composing chat/multimodal inputs, and troubleshooting package/provider/runtime behavior.

This skill is self-contained. Do not reopen the source checkout to use it. The source repository was used only as evidence for these bundled routes, references, and scripts.

Install and Import Check

Base install:

pip install outlines

Optional integrations are installed separately; choose only the backend/provider you need. Examples:

pip install transformers torch       # local Transformers path
pip install llama-cpp-python         # llama.cpp path
pip install vllm                     # vLLM offline/server client path as appropriate
pip install openai                   # OpenAI, SGLang, vLLM server SDK clients
pip install huggingface_hub          # TGI client

Minimal import check:

import outlines
from outlines import Generator, Template
from outlines.inputs import Chat, Image
from outlines.types import JsonSchema, Regex, CFG, Choice

Important: in this source revision, Chat is imported from outlines.inputs, not from the top-level outlines namespace.

Route by Task

Task signal	Read
JSON/Pydantic/dataclass/TypedDict/GenSON output, Literal/Enum/Choice, regex, CFG, Generator, backend selection, custom logits processor, parsing generated JSON	sub-skills/structured-generation/SKILL.md
Local/offline model setup with Transformers, Transformers multimodal, llama.cpp, MLX-LM, vLLM offline, tokenizers, device dtype, CUDA/MPS/VRAM, local batch/stream behavior	sub-skills/local-models/SKILL.md
Hosted or server-based providers: OpenAI/Azure, Anthropic, Gemini, Mistral, Ollama, LM Studio, SGLang, TGI, vLLM server, Dottxt; credentials/endpoints; provider exceptions	sub-skills/remote-providers/SKILL.md
Prompt templates, Application, Chat, Image/Audio/Video, Jinja filters, cache controls, safe self-consistency/task-loop recipes	sub-skills/prompt-workflows/SKILL.md

Repo-Level References

• references/api-overview.md: package map, public imports, model categories, and high-level call contracts.
• references/compatibility.md: optional dependencies, backend/provider/hardware boundaries, and what is or is not verified by CPU inspection.
• references/troubleshooting.md: cross-cutting install/import, optional dependency, provider, schema, cache, and safety failures.
• references/repo-provenance.md: source version and evidence baseline.
• references/repo-routing-metadata.json: structured router metadata for managed imports.

Safe Helper Scripts

• scripts/inspect_outlines_api.py: read-only import/signature smoke for a local Python environment.
• sub-skills/structured-generation/scripts/validate_structure.py: local JSON Schema or regex validator.
• sub-skills/local-models/scripts/check_local_model_prereqs.py: no-network optional module/device probe.
• sub-skills/remote-providers/scripts/check_provider_prereqs.py: no-network provider SDK/env-var probe with secrets redacted.
• sub-skills/prompt-workflows/scripts/render_template.py: local Template renderer.

Operating Rules

1. Keep output-type design separate from model/provider selection. First define the structure, then pick a compatible execution route.
2. Treat Outlines generation outputs as raw strings unless a provider wrapper explicitly returns something else; parse or validate after generation.
3. Do not install broad extras or GPU stacks unless the task explicitly needs that runtime.
4. Do not call provider services, download models, or start long-running servers during planning or static troubleshooting.
5. Never execute model-generated code (eval, exec, shell) while using this skill. Validate with schemas, regex, parsers, and bounded tests.
6. For remote errors, catch outlines.exceptions.APIError subclasses and use retryable, provider, status_code, request_id, and hint to decide recovery.
7. For local hardware, verify the actual framework/backend runtime before claiming CUDA, MPS, vLLM, or llama.cpp acceleration works.