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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Config Reference
The full sssf.config.yaml spec: every field, how defaults merge, and how model / thinking / tools / extensions map onto the coding agent.
It lives at adws/adw_sssf_config/sssf.config.yaml — the default path every adw_*.py and the justfile resolve, and where install.py / make_config.py stamp it. Pass --config <path> to any ADW (or set SSSF_CONFIG for the justfile) to run against a different roster.
Shape
defaults:
coding_agent: pi
model: google/gemini-3.6-flash # ALWAYS provider/model-id
thinking: medium
harness_engineering: []
tools: [read, bash, edit, write, grep, find, ls]
data_dir: adws/adw_data
observability:
db: adws/adw_data/sssf.db
poll_ms: 500
agents:
- name: planner
coding_agent: pi
model: google/gemini-3.6-flash # ALWAYS provider/model-id
thinking: high
color: "#a78bfa"
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
harness_engineering:
- json-enforcer
tools:
- read
- bash
Fields
defaults
| Field | Type | Meaning |
|---|---|---|
coding_agent |
pi | claude_code |
Which interface runs the agent. v1 implements pi only; claude_code is specced and stubbed in agent_cc.py, landing in v2. |
model |
string | Model id. For Pi, any id registered in ~/.pi/agent/models.json. Default gemini-3.6-flash. |
thinking |
enum | Reasoning effort — see below. Default medium. |
color |
hex string | Lane color for every agent that does not set its own. Default empty — the visualizer falls back to its own palette. |
harness_engineering |
list[string] | Coding-agent extensions. Pi: extension names. Claude Code: reserved (MCP, hooks). |
tools |
list[string] | Roster-wide tool allowlist. Every agent that omits its own tools inherits this. Unset = all tools usable. |
protected_files |
list[string] | Paths no agent may modify unless it names them in its own writes. Default: adws/adw_modules/, adws/adw_sssf_config/, adws/adw_*.py — an agent must not be able to edit the machinery that decides whether its work passed. |
data_dir |
path | Runtime home. Sessions land at {data_dir}/sessions/{adw_id}/{agent_name}/. Default adws/adw_data. |
observability
| Field | Type | Meaning |
|---|---|---|
db |
path | SQLite trace db. tracer.py writes it directly; the visualizer polls it. Default adws/adw_data/sssf.db. |
poll_ms |
int | Visualizer live-poll cadence in ms. History uses the same queries, lazy-paged. Default 500. |
agents[]
| Field | Required | Meaning |
|---|---|---|
name |
yes | The identifier ADW scripts use. ADWs name agents, never models. |
purpose |
yes | One sentence: what this agent is for. Should match its system.md Purpose. |
prompt_engineering.system |
yes | Path to the system prompt — who the agent is, its single purpose, its output contract. |
prompt_engineering.user |
yes | Path to the default user prompt — the task template with {{prompt}}, {{previous_envelope}}, {{context_handoff_dir}}. |
color |
no | Hex swatch ("#a78bfa") for this agent's lane in the visualizer. Travels config → agent_sessions.color → /api/sessions/:adw_id, and rides the agent_start event so a lane is colored while the agent is still running. Unset = the UI's fallback palette. |
coding_agent, model, thinking, color, harness_engineering |
no | Override the corresponding defaults key. |
tools |
no | Allowlist. Omitting the key means all tools usable. A capability list, not a boundary — see writes. |
writes |
no | What this agent may modify in the repo, enforced after every call. Omitted = unrestricted (still barred from protected_files). [] = no repo writes at all. A list = only those paths: a trailing / is a directory prefix, * matches within one path segment, ** crosses segments, anything else is an exact path. Naming a protected_files path here is what unlocks it. The session runtime under data_dir is always writable — writes: [] means read-only with respect to the repo, not unable to write its own report. |
Output types are deliberately absent: config defines who an agent is; the ADW call site defines how it's used. One agent serves many calls — same system prompt, different user prompt + output type per call.
Defaults merging
agents.py merges each entry over defaults, key by key. An entry states only what differs; anything unset inherits. agents.validate(cfg, REQUIRED_AGENTS) then confirms every name an ADW declares exists, resolves to a usable coding agent + model, and has both prompt files present on disk. Any miss fails the run immediately — no agent is ever spawned against a half-valid config.
Thinking levels
Pi's reasoning-effort ladder, lowest to highest:
off | minimal | low | medium | high | xhigh | max
Mapped to Pi's reasoning effort control and honored when the model is registered with reasoning: true in ~/.pi/agent/models.json. On a non-reasoning model the setting is inert — no error, no effect. Rough guidance: high/xhigh for planners and reviewers, medium for builders, low for mechanical read-and-report agents. (For Claude Code in v2, the same field maps to the thinking budget.)
Model resolution
Always write model as provider/model-id. agents.py hands the string to the Pi interface, which resolves it against pi's merged catalog — ~/.pi/agent/models.json plus pi's built-in providers. The same model is usually carried by more than one provider (gemini-3.6-flash lives under google and under openrouter as google/gemini-3.6-flash), and a bare id that matches several raises at resolution:
agent 'scout': model pattern 'gemini-3.6-flash' is ambiguous:
[('google', 'gemini-3.6-flash'), ('openrouter', 'google/gemini-3.6-flash'), ...]
That is agents.validate() doing its job — it fails before anything spawns rather than silently billing the wrong provider — but it means every agent in the roster inheriting that default is grounded until the pattern is qualified. Qualifying is the whole fix: google/gemini-3.6-flash, openai/gpt-5.6-terra, fireworks/accounts/fireworks/models/kimi-k3. The leading segment is matched against the provider list first, so the rest of the string can contain slashes.
Other consequences worth knowing:
- A model must be in the catalog before any agent can name it. An unknown id fails at resolution, before spawn.
pi --list-modelsis the catalog the resolver actually reads. - Ambiguity can appear without you touching the config. Registering a new provider that carries a model you already use turns a formerly-fine bare pattern ambiguous. If a roster stops validating and nobody edited it, that is why.
- Provider credentials come from the environment, not the config — the key that matches the provider you named (
GEMINI_API_KEYforgoogle/...,OPENROUTER_API_KEYforopenrouter/...). - The resolved model is recorded per session in
agent_map.jsonand mirrored into theagent_sessionstable. Changing an agent's model invalidates its session: a joined run starts that agent fresh instead of resuming a context window built by a different model.
Tools
tools maps to pi --tools. Pi's seven builtin tool names:
| Tool | Purpose | Pi's own default |
|---|---|---|
read |
read file contents | on |
bash |
execute bash commands | on |
edit |
find/replace edits | on |
write |
create/overwrite files | on |
grep |
search file contents | off |
find |
find files by glob | off |
ls |
list directory contents | off |
grep, find, and ls are off in bare Pi, so an agent that does not name them will shell out through bash to do the same work. The starter roster therefore sets defaults.tools to all seven and lets each agent narrow from there.
Resolution order: an agent's own tools list wins; an agent that omits the key inherits defaults.tools; if neither is set, tools stays None and all tools are usable. An empty list is not "all tools" — it is a tool-less agent, and it will stall.
Write permissions — writes and protected_files
tools cannot express a safety boundary, because two of the tools are general
purpose. bash runs anything, including git checkout, which discards an
engineer's uncommitted work; write reaches any path, not only the one report
file an agent was granted it for. So "this agent changes nothing" is a claim a
tool list can state but never keep.
adw_modules/permissions.py keeps it, the same way every other claim in this
system is kept — after the fact, against the repo. Before an agent's first
prompt the working tree's change-set is fingerprinted; after its last send
(including JSON retries and gate corrections) it is fingerprinted again. Any
path that appeared, vanished, or changed is attributed to that agent.
Comparing change-sets rather than watching writes is deliberate: a path that was
modified before the agent ran and is clean afterwards has been reverted, and
a reversion is a modification. That is what catches git checkout.
A breach is not a gate violation. Gates are for work an agent can be asked to redo; a write has already happened, so re-prompting fixes nothing. Instead:
- every unauthorized change the agent introduced is rolled back — tracked
files with
git checkout --, untracked files by deletion; - a path that was already dirty before the agent ran is left untouched. The operator had uncommitted work there, and discarding it to tidy up would be the same harm this module exists to prevent;
- the phase fails and names every path with what happened to it.
defaults:
protected_files: [adws/adw_modules/, adws/adw_sssf_config/, "adws/adw_*.py"]
agents:
- name: builder # no `writes` key -> unrestricted, minus protected_files
- name: scout
writes: [] # no repo writes; its findings still land in context_handoff/
- name: planner
writes: [specs/]
- name: documenter
writes: [app_docs/, docs/, "**/*.md", "*.md"]
The session runtime under data_dir is always writable, for every agent.
context_handoff/ is how agents hand work to each other, and each agent's
prompts, raw_output.jsonl, and envelope.json sit beside it. That grant comes
from data_dir rather than from .gitignore: the runtime is normally ignored,
so it never even appears in a snapshot, but an agent's ability to record its own
work must not depend on a gitignore line someone can delete.
Narrow by role, not by reflex. Anything that must produce a context_handoff/ artifact needs write, or it will resort to a bash heredoc. Withhold edit/write only where the restriction is the guarantee — a reviewer that cannot edit cannot quietly fix what it was asked to report.
Extension tools must be named explicitly
pi --tools is an allowlist over built-in, extension, and custom tools alike — not just builtins. So the moment an agent has a tools list at all (its own, or one inherited from defaults), any tool registered by its harness_engineering extensions is excluded unless it appears in that list by name.
This fails quietly. The extension still loads, the run still succeeds, and the tool the extension exists to provide is simply never offered to the model — you find out by noticing the agent never called it.
- name: reviewer
harness_engineering:
- .pi/extensions/ast_query.ts # registers tool: ast_query
tools:
- read
- grep
- find
- ls
- bash
- ast_query # REQUIRED — the extension's tool, named or lost
Rule: every entry in harness_engineering that registers a tool must have that tool name added to the agent's tools list. Adding an extension is therefore a two-line change, never one. The alternative is dropping the tools key and leaving defaults.tools unset so the agent resolves to None (all tools) — but with a roster-wide defaults.tools in place, that escape hatch is closed; naming the tool is the only path.
Harness engineering
harness_engineering entries are pi extension file paths, passed through as pi -e <path>, one flag per entry, scoped to that agent only. This is where per-agent harness changes live — e.g. an output-tightening extension for an agent that keeps wrapping its envelope in prose. The starter roster ships with none. On Claude Code the field is reserved for MCP config and hooks in v2.
If the extension registers a tool, name that tool in the agent's tools list too — --tools filters extension tools exactly like builtins, so an unnamed extension tool is silently unavailable no matter that the extension loaded fine. See Extension tools must be named explicitly above. Extensions that only shape output or add flags (no tool registration) need no tools change.