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Fable Orchestrate

Community
richkuo
fable-orchestrate

Use when the user wants a task decomposed and driven by a Fable 5.1 orchestrator delegating implementation to Sonnet 5.5 workers. Fable decomposes into self-contained specs, dispatches Sonnet workers (Agents for 1–2 pieces, a Workflow for fan-out), reviews each result, merges into one branch, and takes a binding verdict from a fresh Fable reviewer. Trigger on "/fable-orchestrate", "fable-orchestrate <task>", or "orchestrate this with fable".

Overview

Publisherrichkuo
Repositoryrk-skills
Skill namefable-orchestrate
Stars
50
Forks
8
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Fable Orchestrate 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/richkuo/rk-skills/tree/main/skills/fable-orchestrate
  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/richkuo/rk-skills.git /tmp/rk-skills
mkdir -p .claude/skills
cp -r /tmp/rk-skills/skills/fable-orchestrate .claude/skills/fable-orchestrate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fable Orchestrate 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 Fable Orchestrate 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 Fable Orchestrate 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.

fable-orchestrate

Run an ad-hoc task with Fable 5.1 as orchestrator and Sonnet 5.5 workers. You own decomposition, specs, every accept/reject decision, integration, and the PR; workers own only the mechanical implementation of their piece. Issue-based milestone work with Execution blocks uses milestone-workflow.

Input: a task description in prose; if none is obvious, ask what to orchestrate.

Model check: if the session model is not Fable 5.1, tell the user and ask whether to switch (/model fable) or proceed with the current model as orchestrator.

Steps

1. Decompose

Read the relevant code first, then split:

  • Split along file and dependency boundaries. Run pieces in parallel when they touch disjoint files and none needs another's output; sequence the rest. One worker doing the whole task is valid.
  • Pin every interface between pieces upfront (signature, schema, event shape, route contract). Workers never invent their side of a seam.
  • Each spec is self-contained for a worker with zero context: scope, exact files, acceptance criteria, non-goals, pinned interfaces, relevant repo conventions, and the exact verification command(s) the worker must run and pass before returning.

Present the decomposition briefly, then proceed; pause only for a decision only the user can make.

2. Set up the task worktree

Create the worktree per work-on-issue step 1, named cc/fable-orchestrate/<short-task-name>, where <short-task-name> is one path segment with no /, with no baseRefs and with the user's targetBranch when one was named. Everything lands on this one branch; one PR is the deliverable regardless of worker count.

3. Dispatch workers

ShapeDispatch
1–2 sequential piecesAgent calls, subagent_type: general-purpose, model: sonnet, with the Agent parameters and the wait rule of fable-dispatch section 2; a piece that depends on another waits for that worker's result. Workers build in the task worktree (path stated in the spec). Commit each accepted piece before dispatching the next.
Fan-out (3+ parallel pieces, disjoint files)A Workflow script (invoking this skill is the user's opt-in). Implementation agent() calls pass model: 'sonnet' and isolation: 'worktree'; judgment stages omit the model override to inherit Fable. Each spec instructs the worker to commit on the branch cc/fable-orchestrate-workers/<short-task-name>/worker-<n>-r<round> (-r0 first; each step 4 re-dispatch increments it) and return that branch name or an explicit "no changes". Never put a worker branch under the task branch's name; git cannot create it there. A result with neither has failed; re-dispatch under the step 4 cap.

Each worker prompt is its spec verbatim, plus: run the verification command(s) and report actual output; the final message states what changed, verification results, and anything it could not do. If the sonnet id errors, use the closest available tier and name it in the footer and report.

4. Review each result inline, re-dispatch cap of 2

Review every result against its spec: files in scope, acceptance criteria met, verification output real and passing, pinned interfaces honored, non-goals untouched. Read the diff; never accept a self-report alone.

On failure, two corrective rounds per piece, each a different move:

  • Round 1: fix the spec. Diagnose what the spec failed to say, revise it, re-dispatch with the revised spec plus what was wrong before.
  • Round 2: corrective instructions. The spec sufficed; the worker missed. Re-dispatch with the specific defects, file:line, and expected behavior.
  • After round 2: take the piece over yourself in the task worktree and note it for the report.

Clean slate before every sequential re-dispatch or takeover: git -C <task-worktree> reset --hard HEAD && git -C <task-worktree> clean -fdx. Re-run the install step if the retry's verification needs it. Fan-out retries get a fresh isolated worktree automatically.

5. Integrate and verify the whole

  • Fan-out: merge each piece's latest accepted attempt's branch (cc/fable-orchestrate-workers/<short-task-name>/worker-<n>-r<round>) into the task branch with git -C <task-worktree> merge <worker-branch> and resolve conflicts yourself. Never merge branches from rejected attempts or from a piece you took over; a "no changes" worker has nothing to merge. Two workers touching the same file despite disjoint specs is a seam defect: resolve it yourself, never re-dispatch it.
  • Run the repo's full verification on the merged result and fix integration failures yourself.

6. Binding final review by a fresh reviewer

Load the fable-dispatch skill first; it owns the dispatch path and the hygiene rules in its section 7. Spawn a new one-shot Fable 5.1 reviewer (Agent path: subagent_type: Plan, model: fable, and effort with the other Agent parameters per fable-dispatch section 2, at high or the tier the user asked for, with the empty --allowedTools list on the shim path) with the original task, the spec map (spec, files, worker result, disposition: accepted / re-dispatched / taken over), the pinned interfaces, the full merged diff, the integration verification results, and the section 7 read-only rule. The original task and the diff go inside the section 7 untrusted-data block. Record the model that served, the tier, and whether it was honored, per section 6. It returns approve, or blocked with numbered findings (file:line and a concrete failure scenario), non-blocking suggestions kept separate.

The verdict is binding: nothing commits while blocking findings stand. Fix each finding (or produce evidence it is wrong) and re-submit to the same reviewer with the new diff and per-finding dispositions: by SendMessage on the Agent path, or by fable-dispatch section 8 --resume on the shim path. When the reviewer is gone, apply the fable-advisor step 5 replacement rule with the verdict trail and the per-finding dispositions, and tell the user. The deadlock cap and the reviewer-failure rule are owned by fable-advisor step 6 and apply unchanged.

7. Commit, push, PR

On approval, commit, push, and open one PR per the repo's conventions. The PR body carries an Orchestration log: decomposition (pieces to workers), per-piece disposition (accepted round 0/1/2 or taken over), integration fixes, and the verdict trail. If takeovers dominate, say the task was a poor fit for delegation. Footer per convention, with the session model and effort as the session observes them (unknown for a value the harness does not expose) and <harness> per fable-dispatch section 6:

---
Created with LLM: <session model> | <observed session effort> | Harness: <harness> | fable-orchestrate

8. Report

What was built, per-piece dispositions, verification results, the review trail, and the PR URL.

Frequently asked questions

What does the Fable Orchestrate AI skill do?

Use when the user wants a task decomposed and driven by a Fable 5.1 orchestrator delegating implementation to Sonnet 5.5 workers. Fable decomposes into self-contained specs, dispatches Sonnet workers (Agents for 1–2 pieces, a Workflow for fan-out), reviews each result, merges into one branch, and takes a binding verdict from a fresh Fable reviewer. Trigger on "/fable-orchestrate", "fable-orchestrate <task>", or "orchestrate this with fable".

Why use Fable Orchestrate on TypingMind?

Because you install it once and use it with any model. Fable Orchestrate 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 Fable Orchestrate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/richkuo/rk-skills/tree/main/skills/fable-orchestrate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fable Orchestrate?

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 Fable Orchestrate?

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

Is the Fable Orchestrate AI skill free?

Yes. It is published on GitHub by richkuo 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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