# Pi Fabric skills

Pi Fabric uses a core-first, user-opt-in skill hierarchy.

## Invocation contract

- The model can invoke one execution reference: `fabric-exec`. Its physical path selects the configured TypeScript or Python implementation. It covers Pi core work through `fabric_exec`, `pi.*`, discovery, and stable provider proxies.
- The user invokes every advanced workflow. Each one declares `disable-model-invocation: true` and stays out of the model catalog. Agent policy forbids reading one autonomously or delegating from one user-only skill to another. The policy governs agent behavior. It is not a filesystem authorization boundary.
- `/skill:fabric-guide` is the user-only router. It names one exact advanced command and stops there. The router never invokes the recommendation.
- Each user-facing description summarizes its command. Only the selected execution reference spends always-on model context.
- Both `skillsets/typescript/` and `skillsets/python/` contain the same thirteen canonical skill names, each with its own reference files. Fabric contributes only the selected tree through Pi resource discovery. Changing the kernel reloads Pi resources; see [kernel-specific skills](kernels.md#kernel-specific-skills-and-guidance). Third-party skills remain under Pi's normal discovery rules.

The parent agent behaves like regular Pi until the user explicitly opts into orchestration, recursion, Schema, Jev programs, ambient actors, or swarm coordination.

## Information hierarchy

1. Put required ordered actions and checkable completion criteria in `SKILL.md`.
2. Use a **hard pointer** for material that the run must load before execution.
3. Use a **branch pointer** when some runs need the material and others do not.
4. Use a **soft pointer** for optional depth that improves quality. Correctness does not require it.
5. Keep skill-owned references beside the skill that owns them. Package-level profiles may point to that single source of truth.

In a packaged `SKILL.md`, write every cross-document path with the `<skill-dir>` marker, for example `<skill-dir>/references/setup.md` or `<skill-dir>/../shared/references/setup.md`. Fabric replaces the marker inline with the directory that contains the loaded `SKILL.md`. Slash-invoked skills resolve through Pi's expanded `<skill location="...">` form. For a direct `SKILL.md` read, Fabric uses the actual read path. The marker is an explicit opt-in by the author. Fabric does not match skill names, enumerate directories, or alter ordinary document reads.

A mandatory pointer serves legibility and single-source maintenance. Per-run token savings are only a side effect. Keep always-required executable code whole even when a split would shorten the skill.

## Authoring rules

- Give each meaning one source of truth.
- Prefer stable leading words that Fabric already uses: **one program**, **bounded**, **verifier**, **decision point**, **evidence loop**, **CAS claim**, and **outside observer**.
- Add a checkable **Completion criterion** only when it matches runtime behavior and does not push the agent toward whole-flow retries.
- Classify each dependency as hard, branch-conditioned, or soft.
- Apply the no-op test sentence by sentence: cut any text that leaves model behavior unchanged.
- State the target behavior in positive terms. Reserve prohibitions for safety or invocation boundaries.
- Preserve both executable language variants and their contract tests. Python uses host actions and native loops/asyncio.gather, not TypeScript callback helpers. Expensive fan-out returns `success`, `partial`, or `failed`. A `partial` result on its own implies no automatic whole-flow retry.

## User-invoked workflows

- `/skill:fabric-guide`: choose a workflow.
- `/skill:fabric-workflow`: run finite fan-out or pipeline work with verification.
- `/skill:fabric-council`: get role diversity from the same model.
- `/skill:fabric-fusion`: run multi-model deliberation (compare) or acting (read-only references + one actor).
- `/skill:fabric-rlm`: decompose context recursively.
- `/skill:fabric-schema`: gate mutation behind evidence.
- `/skill:fabric-jev`: compose typed System One judgments and bounded foreground/background loops; see [Jev](jev.md).
- `/skill:fabric-advisor`: get persistent peer advice.
- `/skill:fabric-supervisor`: supervise a persistent goal.
- `/skill:fabric-spec`: supervise spec compliance persistently.
- `/skill:fabric-ambient`: route directly to an advisor or supervisor profile.
- `/skill:fabric-swarm`: coordinate durable actors.