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name: structured-tasks description: "Routes Promptify task construction, prompt building, parsing, async and batch execution, custom Pydantic schemas, and provider-side task failures." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Structured Tasks

Use this sub-skill when the user wants to create or run Promptify task objects, customize prompts, debug structured JSON output, or understand how task kwargs flow into the model and template layers.

Covered workflows

• NER, classification, QA, summarization, extraction, question generation, SQL generation, text normalization, topic extraction, and generic custom Task objects.
• Prompt rendering with built-in Jinja templates or custom template files.
• Parsing model output into Pydantic models, including fallback parsing when a provider does not return strict JSON.
• Sync, async, and batch task execution.
• Provider and LiteLLM failures such as auth, timeout, rate limit, and malformed response errors.

Read first

• references/task-catalog.md for the built-in task families and their key arguments.
• references/api-reference.md for verified signatures and the split between model kwargs and template kwargs.
• references/workflows.md for practical recipes, mock-engine usage, async calls, and batch patterns.
• references/troubleshooting.md for provider, parse, template, and legacy-API mismatch errors.
• ../../scripts/check_promptify.py --mode tasks for a no-network smoke test.

Route boundaries

Include here

• Choosing the correct task class for a natural-language request.
• Supplying domain, labels, examples, schema, rules, max_length, key_points, num_questions, and num_topics.
• Building prompts with built-in or custom templates.
• Handling model kwargs such as temperature, top_p, max_tokens, stop, presence_penalty, frequency_penalty, timeout, and max_retries.
• Interpreting ParserError, TemplateNotFoundError, and provider-level LiteLLM errors.

Exclude or route elsewhere

• Dataset loading, metric selection, ROUGE, and evaluator results go to ../evaluation/SKILL.md.
• Repo maintenance or publication tasks do not belong here.
• Archived notebook or tutorial names such as Prompter, OpenAI, and Pipeline are not the current API.

Mental model

Promptify task objects are thin routers around a shared execution stack:

1. A task class chooses a built-in template and output schema.
2. BaseTask splits model kwargs from template kwargs.
3. PromptBuilder renders the prompt into OpenAI-style messages.
4. LLMEngine sends the messages to LiteLLM.
5. The response is parsed into the requested Pydantic shape.

This sub-skill exists so future agents do not need to reopen the source code to remember that flow.

Use this route when the user asks for

• "Create a medical NER task with labels and examples"
• "Run QA with a custom question and domain"
• "Build a custom structured output schema"
• "Use batch mode or async with Promptify"
• "Fix a parser or template failure"
• "Understand which kwargs belong to the model and which belong to the template"

When to switch to evaluation

If the user asks about load_dataset, evaluate, exact match, accuracy, F1, precision, recall, or ROUGE, switch to the evaluation sub-skill instead of expanding this one.