# Create Config

Generate `sssf.config.yaml` — the agent roster for a target repo.

## Generate it

```bash
uv run <skill>/scripts/make_config.py
```

`<skill>` is the directory this skill was installed into (e.g. `~/.agents/skills/sssf` or a repo's `.claude/skills/sssf`).

Writes `adws/adw_sssf_config/sssf.config.yaml` — creating the directory if needed — with the starter agents (planner, builder, scout, reviewer, documenter) wired to the prompt files `/sssf install` stamped into `adws/adw_data/prompt_engineering/`. That path is the default every ADW and the justfile look for; `--config` overrides it. `make_config.py` refuses to overwrite an existing config unless you pass `--force`, so retuning an existing roster is a hand edit — see `update_config.md`.

## The rule

**One agent, one prompt, one purpose.** An entry defines who an agent *is*: its coding agent, model, thinking level, and exactly one system prompt plus one user prompt. How it gets *used* — the output type, a per-call user prompt override — lives at the ADW call site, never here.

## Schema

```yaml
defaults:
  coding_agent: pi                 # v1: pi only (claude_code is specced, stubbed until v2)
  model: google/gemini-3.6-flash   # ALWAYS provider/model-id — a bare id is ambiguous
  thinking: medium                 # off | minimal | low | medium | high | xhigh | max
  harness_engineering: []          # pi extension names
  data_dir: adws/adw_data          # runtime home: {data_dir}/sessions/{adw_id}/{agent_name}/

observability:
  db: adws/adw_data/sssf.db        # tracer writes here; the UI polls it
  poll_ms: 500                     # visualizer live-poll cadence

agents:
  - name: planner                  # ADW scripts name agents, never models
    coding_agent: pi
    model: google/gemini-3.6-flash
    thinking: high
    color: "#a78bfa"               # optional hex — this agent's lane color in the visualizer
    purpose: Turn a request into a plan the builder can implement without asking questions.
    prompt_engineering:
      system: adws/adw_data/prompt_engineering/planner/system.md
      user: adws/adw_data/prompt_engineering/planner/user.md

  - name: scout
    thinking: high                 # unset keys fall through to defaults
    purpose: Find and report where things live; change nothing.
    prompt_engineering:
      system: adws/adw_data/prompt_engineering/scout/system.md
      user: adws/adw_data/prompt_engineering/scout/user.md
    tools:                         # optional allowlist — omit the key entirely for all tools
      - read
      - bash
```

Every agent entry merges over `defaults`, so an entry only states what differs. Pi's builtin tools are `read`, `bash`, `edit`, `write` — a read-only recon agent gets `[read, bash]`; a builder omits `tools` altogether.

## After generating

1. Each agent needs its prompt pair to exist on disk: `adws/adw_data/prompt_engineering/{name}/system.md` and `user.md`. `agents.validate()` fails the run at startup if either is missing.
2. Write `purpose` as one sentence and make the system prompt say the same thing — the two should not drift.
3. Validate by running the smallest ADW that names your agents; a bad entry fails fast, before anything spawns.

Full field-by-field spec, thinking-level mapping, and model resolution: `references/config.md`. Retuning an existing roster: `update_config.md`.