modbender 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
| Stage | Agent ID | Input | Output |
|---|---|---|---|
| market | market | idea.md | market.md |
| planner | planner | idea.md, market.md | plan.md |
| arch | arch | plan.md | arch.md |
| builder | builder | arch.md, plan.md | skill/ |
| auditor | auditor | skill/ | audit.md |
| docs | docs | skill/, audit.md | docs_review.md |
| pricer | pricer | all outputs | pricing.md |
Read references/agents.md for full agent descriptions, models, and prompt guidance.
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.pydirectly - 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