dabbledabble-IND-da-air/prd-tdd-agentic-workflow

PRD/TDD Agentic Workflow Dataset

A purpose-built SFT dataset for teaching an autonomous coding agent to decompose any user prompt into a strict 5-stage agentic workflow before writing code.

Purpose

The model is trained to act as a planning agent — it does NOT write implementation code. It takes a raw user feature request and emits a structured workflow prompt that an executor agent follows. This enforces spec-driven, test-first discipline on every task.

The 5-Stage Scaffold (strict, every example)

Every assistant response contains exactly these five sections in this order:

1. PRD — problem, scope, acceptance criteria, non-goals
2. Spec — interfaces, data models, contracts
3. TDD Plan — test list, red/green/refactor order
4. Pass/Test Guardrails — stop conditions, failure definitions
5. Workflow Prompt — self-contained prompt for an executor agent, ending with EXECUTE

Each section is introduced by an exact marker on its own line: == PRD ==, == Spec ==, == TDD Plan ==, == Pass/Test Guardrails ==, == Workflow Prompt ==

Schema

Each example is a ChatML-style message list:

{
  "id": "category:prompt_prefix_0001",
  "category": "CLI|API|refactor|bugfix|library|web",
  "complexity": "simple|moderate|complex",
  "messages": [
    {"role": "system", "content": "<5-stage scaffold system prompt>"},
    {"role": "user", "content": "<raw feature request>"},
    {"role": "assistant", "content": "<5-stage structured workflow>"}
  ],
  "teacher": "zai-org/GLM-5.2",
  "provider": "zai-org",
  "prompt_tokens": 488,
  "completion_tokens": 3198,
  "usd_cost": 0.0060
}

Provenance

• Teacher model: zai-org/GLM-5.2 (MIT)
• Generator route: Hugging Face Inference Providers (provider=zai-org)
• System prompt: Locks the 5-stage scaffold format with strict word budgets and an EXECUTE terminator.
• Validator: Every response is programmatically checked for all 5 markers in correct order, the EXECUTE terminator, and reasonable length. Invalid responses are retried (up to 3 attempts).

Composition

• 824 examples (deduplicated, all validated)
• 6 categories: CLI, API, refactor, bugfix, library, web
• 3 complexity tiers: simple, moderate, complex
• High architectural variance via constraint twists (language, scale, persistence, async, observability, deployment)

Category	Count
CLI	141
API	132
refactor	146
bugfix	146
library	130
web	129

Complexity	Count
simple	270
moderate	270
complex	284

Cost

• Total spend: $4.79 (well under the $25 budget cap)
• Avg cost/example: $0.0058

Intended Use

Phase 2 of an autonomous-coding model build: SFT (LoRA) onto Qwen/Qwen3.6-35B-A3B, mixed with Glint-Research/Fable-5-traces and Opus-4.6 reasoning sets, then converted to MLX 6-bit for local agentic-coding use on a 64 GB Mac.

Generated by ML Intern

This dataset repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from datasets import load_dataset

dataset = load_dataset('dabbledabble-IND-da-air/prd-tdd-agentic-workflow')