Skill Factory

A 7-stage multi-agent pipeline that takes a raw skill idea and produces a complete, market-ready skill package. Each stage is an isolated CLI agent call — no nested sessions, no parallelism.

Overview

idea → market → planner → arch → builder → auditor → docs → pricer → done

All state is written to disk between stages. Any stage can be re-run independently.

Workflow

Step 1: Initialize a skill idea

python3 {baseDir}/scripts/init_pipeline.py "My Skill Idea" --workspace /tmp/sf-my-skill

This creates a workspace directory with idea.md pre-filled. Review and edit idea.md before running the pipeline.

Step 2: Run the full pipeline

bash {baseDir}/scripts/pipeline.sh --workspace /tmp/sf-my-skill

The pipeline runs all 7 agents sequentially. Each agent reads previous outputs and writes its own report into the workspace.

Step 3: Collect outputs

After the pipeline completes, the workspace contains:

workspace/
├── idea.md          # Your original idea (input)
├── market.md        # Market research report
├── plan.md          # Product plan with requirements
├── arch.md          # Architecture and file structure
├── skill/           # The built skill (SKILL.md + scripts/ + references/)
├── audit.md         # Quality audit report
├── docs_review.md   # Documentation review
└── pricing.md       # Pricing and positioning

Step 4: Review gates

Each stage has a gate. If a gate fails, the pipeline stops with a non-zero exit code and reports which stage failed. Fix the issue and re-run from that stage:

bash {baseDir}/scripts/pipeline.sh --workspace /tmp/sf-my-skill --from auditor

Stages at a glance

Read references/pipeline.md for detailed gate definitions, failure modes, and re-run strategies.

Key Constraints

• No nested sessions — every agent runs as a top-level isolated CLI call
• No parallelism — stages run strictly sequentially; each depends on the previous
• File-based state — all inter-stage communication goes through the workspace filesystem
• Idempotent stages — re-running a stage overwrites its output without affecting others

When NOT to Use This Skill

• You only need a quick, simple skill with no research phase — use init_skill.py directly
• You are iterating on an existing skill — edit files directly
• You want parallel research across multiple skill ideas — run multiple pipeline instances in separate workspaces