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Sssf

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disler
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.

Overview

Publisherdisler
Repositorysuper-simple-software-factory
Skill namesssf
Stars
966
Forks
254
Bundled files
91
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 91 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by disler on GitHub. Read the source before you install it.

Installation

Install the Sssf AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/disler/super-simple-software-factory/tree/main/.claude/skills/sssf
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/disler/super-simple-software-factory.git /tmp/super-simple-software-factory
mkdir -p .claude/skills
cp -r /tmp/super-simple-software-factory/.claude/skills/sssf .claude/skills/sssf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sssf in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sssf on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sssf is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

  1. Read cookbooks/sssf_overview.md — the system map.
  2. ls adws/adw_*.py and read each file's Phases: docstring line.
  3. 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 runs table and a payload column — neither exists (sessions, payload_json). The spec that would have said so is references/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)

RequestCookbook
/sssf install, set up the factory in this repocookbooks/install.md
create a new ADW / workflowcookbooks/create_adw.md
modify an existing ADW chaincookbooks/update_adw.md
create the config / agent rostercookbooks/create_config.md
add or retune an agent (model, thinking, tools, prompts)cookbooks/update_config.md
extend adw_modules with new low-level logiccookbooks/update_modules.md
run / monitor an ADWcookbooks/how_to_prompt_for_the_eng.md first, then cookbooks/run_adw.md
turn a request into an ADW promptcookbooks/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)

  1. Validate before running — every ADW declares REQUIRED_AGENTS and calls agents.validate() first; a missing/misnamed agent fails before anything spawns.
  2. Typed outputs only — every agent call pairs with a concrete EnvelopeBase subclass in adw_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 in data_types.py, (b) the JSON example in the agent's user.md ## Report section, (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).
  3. Gates validate claims, not guesses — gate(envelope, run) -> list[str] violations; failures return to the same session as corrections.
  4. Four-param rule — any function with more than 4 parameters takes one concrete data type instead (AgentCall, PhaseParams are the pattern).
  5. One agent, one prompt, one purpose — identity lives in system.md; task shape (user prompt + output type) lives at the call site.
  6. ADW scripts stay thin — all low-level logic lives in adw_modules/.
  7. 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.
  8. A known command is code, not an agent — if you can write the invocation down (bun test, ruff check), it belongs in a kind="code" phase via adw_modules/quality.py. Agents are for the parts that need reading and deciding; failures come back to the builder as an envelope either way.
  9. tools: is a capability list, writes: is the boundary — bash runs anything (including git checkout) and write reaches any path, so a tool list can never make "this agent changes nothing" true. writes: per agent and protected_files in defaults are enforced in adw_modules/permissions.py after every agent call: unauthorized changes are rolled back and the phase dies. The session runtime under data_dir is always writable — a read-only agent is read-only with respect to the REPO, never mute.
  10. 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. Pass accepted= so the exit code, the session status, and the banner are decided together and cannot disagree.

v1 scope

Pi coding agent only (coding_agent: pi), default model gemini-3.6-flash via openrouter, thinking medium. claude_code is schema-valid but stubbed until v2. The visualizer app ships in a later pass — observe via sqlite queries until then.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 31 more files.

Frequently asked questions

What does the Sssf AI skill do?

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.

Why use Sssf on TypingMind?

Because you install it once and use it with any model. Sssf is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sssf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/disler/super-simple-software-factory/tree/main/.claude/skills/sssf. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sssf?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sssf?

As many as you like. As long as a model supports skills, you can use Sssf with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sssf AI skill free?

Yes. It is published on GitHub by disler under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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