---

name: prompt-workflows description: "Compose, validate, and apply reusable Outlines prompt workflows with Template, Application, Chat, multimodal inputs, caching, and safe iterative prompting." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Prompt Workflows

Use this sub-skill when the task is to build or troubleshoot reusable prompt composition around Outlines rather than to choose a model backend or design a schema. It covers:

• Jinja prompt rendering with outlines.Template.
• Reusable outlines.Application objects that pair a template or callable with an optional output type.
• outlines.inputs.Chat conversations and multimodal Image, Audio, and Video inputs.
• Prompt/workflow caching controls.
• Bounded iteration patterns such as structured extraction refinement, self-consistency, task-list loops, and regex-guided structured-output debugging.

Do not call remote providers, start local model downloads, or execute model-generated code from this skill alone. After prompt/input assembly is validated, route execution to the appropriate sibling skill: ../structured-generation/ for schemas and output types, ../remote-providers/ for hosted model capability/credential routing, or ../local-models/ for local model setup and capability routing.

Quick Route

1. Render first. Use Template.from_string(content, filters={}) for inline prompts. Use Template.from_file(path, filters={}) only when the template file and all includes/extends are bundled under the same template directory boundary.
2. Fail early on variables. Outlines templates use Jinja StrictUndefined; render with a complete variable mapping before any model call.
3. Package repeated tasks. Use Application(template_or_callable, output_type=None) when the same prompt/output contract will be applied repeatedly. Call it as application(model, {"var": value}, **inference_kwargs).
4. Choose the right input carrier. Import chat and assets from outlines.inputs: Chat, Image, Audio, Video. Keep each chat message shaped as {"role": "system"|"user"|"assistant", "content": ...}.
5. Validate assets before routing. Image requires a PIL image with a real format and base64-encodes it. Audio and Video are wrappers; actual support is model/provider-specific.
6. Control caching deliberately. Use OUTLINES_CACHE_DIR, outlines.caching.cache, cache_disabled(), outlines.disable_cache(), and outlines.clear_cache() for deterministic workflow components and stale-cache recovery.
7. Keep iteration bounded and safe. Self-consistency and agent-loop patterns should have fixed budgets, deterministic parsers, no credential assumptions, no network requirements unless separately authorized, and no eval/exec of generated code.

Load These References

• references/api-reference.md: exact APIs, imports, Jinja filters, Application, Chat, multimodal inputs, and cache controls.
• references/workflows.md: reusable workflow recipes distilled from Outlines examples without source-checkout dependencies.
• references/troubleshooting.md: failure modes for missing variables, include boundaries, paths, chat/content shape, image/base64 conversion, provider capability mismatches, cache behavior, and generated-code safety.

Bundled Script

Use scripts/render_template.py to render an inline Outlines Template with JSON variables or a small built-in fixture before wiring a prompt into an Application, Chat, or generator call. The script performs only local template rendering; it does not read repository-relative prompt files by default, call providers, or execute generated code.

Example:

python scripts/render_template.py \
  --template 'Hello {{ name }}!' \
  --vars-json '{"name":"Ada"}'

Expected output:

Hello Ada!