Port the sssf skill from ~/.agents/skills/sssf into this repo so it can be distributed and installed with the skills CLI (skills add INDigitalStudio/skills --skill sssf). - Copy the skill (SKILL.md, cookbooks, references, scripts, templates, and the visualizer app source) into sssf/. - Gitignore build/runtime artifacts: the visualizer's node_modules/ and dist/, Python bytecode, and the machine-specific repos.json. - Make the skill location-independent: install.py now stamps the skill's real path into the stamped justfile's skill_dir (replacing the hardcoded ~/.agents/skills/sssf), so 'just obs' finds the visualizer wherever the CLI installed the skill. - Update cookbooks to use <skill>/scripts/... instead of the hardcoded path, and document the skills CLI install command. - Update the repo README with install instructions.
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| name | description | argument-hint |
|---|---|---|
| sssf | Super Simple Software Factory — deploy and operate repeatable agents+code workflows (ADWs) in any codebase. Use when the user says /sssf install, wants to create/run/update an ADW, manage the agent roster in sssf.config.yaml, or observe running agent workflows. Keywords - sssf, software factory, ADW, AI developer workflow, agent pipeline, install factory. | [install | create adw | run adw | update config | ...] |
Super Simple Software Factory (SSSF)
Reusable combination of agents plus code: deterministic Python ADW scripts own sequencing, retries, and acceptance; coding agents (Pi in v1) work inside bounded phases; typed JSON envelopes carry context between them; everything streams into SQLite for the polled visualizer. Agent proposes, code disposes.
Startup
Three steps. Then stop.
- Read cookbooks/sssf_overview.md — the system map.
ls adws/adw_*.pyand read each file'sPhases:docstring line.- Print the ADWs as a table — name, the chain, one line on when to reach for it — and wait for the engineer's request.
| ADW | Chain | Use when |
|---|---|---|
| adw_scout | engineer → scout | read-only recon; nothing changes |
| adw_simple_sdlc | plan → build → test → review → document, 3 commits | the work is real and its shape is not obvious |
Nothing else. No trace-db queries, no reading the config or the ADW scripts' bodies, no repo inventory, no last-runs summary, no diagnosing an old failure, no "current state" dashboard. None of it was asked for, and it is not free:
- Volunteered state is guessed state. An orchestrator that improvised a status board queried a
runstable and apayloadcolumn — neither exists (sessions,payload_json). The spec that would have said so isreferences/observability.md, one lazy read away. Probing to look prepared is how you end up confidently wrong in your first message. - It spends the context the real task needs, before you know what the task is.
- It is stale on arrival. State printed before the request describes a system that the very next run changes.
Everything else — the db schema, the roster, the handoff contract — is lazy-loaded through the routing table below, when a request actually calls for it. Reading it early defeats the mechanism.
Two exceptions, both narrow: if the engineer's first message already contains a request, skip the waiting and route it; and if the factory is plainly not installed (no adws/, no config), say that in one line instead of the table.
Orchestrator rules
You run the system, observe the system, and help the user interact with it. You do no ADW work yourself:
- Never implement, plan, or test in an agent's place — launch the ADW and watch it.
- Never edit files inside
adws/adw_data/sessions/— that is the run record. - Observe by querying
adws/adw_data/sssf.db(WAL — reads never block writers) when observing is the task. This is a capability, not a startup step: query it to follow a run you launched or one the engineer asked about, never to volunteer a status report nobody requested. - Report phase status plainly: name, owner, status, error if any.
Request routing (lazy-load the cookbook, then follow it)
| Request | Cookbook |
|---|---|
/sssf install, set up the factory in this repo |
cookbooks/install.md |
| create a new ADW / workflow | cookbooks/create_adw.md |
| modify an existing ADW chain | cookbooks/update_adw.md |
| create the config / agent roster | cookbooks/create_config.md |
| add or retune an agent (model, thinking, tools, prompts) | cookbooks/update_config.md |
| extend adw_modules with new low-level logic | cookbooks/update_modules.md |
| run / monitor an ADW | cookbooks/how_to_prompt_for_the_eng.md first, then cookbooks/run_adw.md |
| turn a request into an ADW prompt | cookbooks/how_to_prompt_for_the_eng.md |
Deep specs, when needed: references/config.md · references/handoff.md · references/observability.md
Hard rules (enforced across everything the factory generates)
- Validate before running — every ADW declares
REQUIRED_AGENTSand callsagents.validate()first; a missing/misnamed agent fails before anything spawns. - Typed outputs only — every agent call pairs with a concrete
EnvelopeBasesubclass inadw_modules/data_types.py; parse failures re-prompt the same session (context intact), never restart. The output contract is a synced triad: (a) the type indata_types.py, (b) the JSON example in the agent'suser.md## Reportsection, (c)output_type=at every call site. These are ONE contract — change any one, update all three in the same edit (grep the type name to find every call site). - Gates validate claims, not guesses —
gate(envelope, run) -> list[str]violations; failures return to the same session as corrections. - Four-param rule — any function with more than 4 parameters takes one concrete data type instead (
AgentCall,PhaseParamsare the pattern). - One agent, one prompt, one purpose — identity lives in
system.md; task shape (user prompt + output type) lives at the call site. - ADW scripts stay thin — all low-level logic lives in
adw_modules/. - Every phase earns a description — one sentence on what it does and why, never a restatement of its name. It is the only intent the trace, the console, and the UI ever show;
commit_plan: "Commit the plan"is rejected at construction, blank is too. - A known command is code, not an agent — if you can write the invocation down (
bun test,ruff check), it belongs in akind="code"phase viaadw_modules/quality.py. Agents are for the parts that need reading and deciding; failures come back to the builder as an envelope either way. tools:is a capability list,writes:is the boundary —bashruns anything (includinggit checkout) andwritereaches any path, so a tool list can never make "this agent changes nothing" true.writes:per agent andprotected_filesin defaults are enforced inadw_modules/permissions.pyafter every agent call: unauthorized changes are rolled back and the phase dies. The session runtime underdata_diris always writable — a read-only agent is read-only with respect to the REPO, never mute.- Every ADW ends in
run.finish()— phases passing is not the same as the run being accepted. A test phase that ran a red suite succeeded at its job. Passaccepted=so the exit code, the session status, and the banner are decided together and cannot disagree.
v1 scope
Pi or OMP coding agent (coding_agent: pi or omp), chosen at install time. claude_code is schema-valid but stubbed until v2. The visualizer app (just obs) ships with the skill — a multi-repo trace UI over each repo's sssf.db.