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Fable

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codejunkie99
fable

Use the locally authenticated Claude Code Fable 5.1 model only as the orchestrator for a task, then execute implementation with GPT-5.6 Luna or DeepSeek V4 Flash. Use when the user invokes $fable or asks Fable to orchestrate Codex agents.

Overview

Publishercodejunkie99
Repositoryfable-orchestrator
Skill namefable
Stars
614
Forks
108
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by codejunkie99 on GitHub. Read the source before you install it.

Installation

Install the Fable 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/codejunkie99/fable-orchestrator/tree/main/skill/fable
  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/codejunkie99/fable-orchestrator.git /tmp/fable-orchestrator
mkdir -p .claude/skills
cp -r /tmp/fable-orchestrator/skill/fable .claude/skills/fable
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fable 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 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 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 orchestrator

Claude Fable 5.1 supplies orchestration decisions only. Codex remains the runtime that spawns workers, owns files, runs tools, verifies the result, and reports to the user.

Invocation

Treat everything after $fable as the objective. Fable 5.1 always owns orchestration. Implementation workers are restricted to GPT-5.6 Luna and DeepSeek V4 Flash:

  • $fable build the feature
  • $fable debug this; implementer: gpt-5.6-luna

Model names are requests, not guesses. Before dispatch, inspect the current spawn_agent tool description and custom agent roles. Use only models or roles that are currently callable. If a requested model is unavailable, say so and use the closest available choice only when that substitution is low-risk; otherwise ask for a replacement.

For the simplest automatic path, the user can provide only an objective. Apply this ordered classifier when they did not explicitly choose a route:

  • loop construction, repeated iteration, or high-throughput mechanical work: use a callable OpenCode Go agent pinned to opencode-go/deepseek-v4-flash;
  • implementation: use a callable OpenCode Go agent pinned to opencode-go-responses/gpt-5.6-luna, then opencode-go/deepseek-v4-flash;
  • planning, research, review, and other work: choose by normal task fit.

Prefer an exposed agent_type that pins both model and provider. Never infer callability from a config file or send a raw model override across providers. An explicit implementation choice wins only when it is GPT-5.6 Luna or DeepSeek V4 Flash. Do not assign implementation to any other model. After any applicable approval gate, state only <Agent> — <Model>: <bounded responsibility>, then immediately start. Classify by the callable model pin, not the agent's display name. Do not show the full model catalog unless asked.

OpenCode Go compatibility

Codex Router owns OpenCode Go provider setup, model discovery, and credentials; Fable must not duplicate them. A user adds the OpenCode Go API key once through the router's local secure setup. Never request or paste an API key in chat or store it in a Fable packet. Fable consumes only callable opencode-go/ and opencode-go-responses/ agents supplied by Codex, so it needs no proxy, dashboard, or second static model list. Start a new Codex task after changing the provider or agent definitions.

Workflow

  1. Read the objective and relevant local instructions. Inspect enough of the workspace to give Fable facts rather than assumptions.
  2. Build a compact orchestration packet containing the objective, acceptance criteria, workspace context, constraints, protected files, evidence already gathered, callable worker menu, concurrency limit, and user preferences.
  3. Send the packet to scripts/ask_fable.sh. Use Claude's fable alias; do not read, copy, print, or modify Claude credentials.
  4. Require a bounded task graph with role, model or agent type, owned files or responsibility, dependencies, expected output, verification, and a stop condition for every node. Reject any implementation node assigned to a model other than GPT-5.6 Luna or DeepSeek V4 Flash. Fable 5.1 adjudication remains outside the worker graph.
  5. Validate the graph against the actual task and current tools. Codex has final responsibility for safety and scope. Do not execute invented models, unsafe actions, or work outside the user's request.
  6. Spawn independent ready nodes in parallel, up to the live collaboration limit. Tell every code-writing worker its ownership and that other agents share the workspace, so it must preserve and accommodate their edits.
  7. Collect results, inspect changed files, and run proportionate verification. For complex work, send a concise results packet back through the helper for the next graph or final adjudication. Cap this at three Fable calls unless the user asks to continue.
  8. Finish only when acceptance criteria and verification pass. Report selected models, material changes, and concrete proof.

Whenever the helper returns Fable's orchestration output, display it verbatim under this exact heading:

text
Fable 5.1 speaks:

Do not relabel ordinary Codex or worker-agent output as Fable speech.

Boundaries

  • Fable plans and adjudicates; it does not silently replace the Codex workers.
  • Exchange decisions, evidence, task packets, diffs, test results, and blockers, not hidden reasoning.
  • Orchestration does not expand authorization. Publishing, deployment, destructive operations, spending, and external messages retain their normal approval boundaries.
  • If delegation adds no value, use one worker or execute directly after the Fable plan.

Calling Fable

Pass the packet as standard input:

bash
printf '%s' "$PACKET" | "$HOME/.codex/skills/fable/scripts/ask_fable.sh"

Do not place secrets in the packet. The helper uses the existing local Claude Code authentication and creates no persistent Claude session.

Bundled files

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

Frequently asked questions

What does the Fable AI skill do?

Use the locally authenticated Claude Code Fable 5.1 model only as the orchestrator for a task, then execute implementation with GPT-5.6 Luna or DeepSeek V4 Flash. Use when the user invokes $fable or asks Fable to orchestrate Codex agents.

Why use Fable on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codejunkie99/fable-orchestrator/tree/main/skill/fable. 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 Fable?

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?

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

Is the Fable AI skill free?

Yes. It is published on GitHub by codejunkie99 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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