Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add richkuo/rk-skills --skill fable-orchestrategit clone --depth 1 https://github.com/richkuo/rk-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/richkuo/rk-skills/fable-orchestrate)<a href="https://agentmods.dev/skills/richkuo/rk-skills/fable-orchestrate"><img src="https://agentmods.dev/badge/skills/richkuo/rk-skills/fable-orchestrate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/richkuo/rk-skills/fable-orchestrate"><img src="https://agentmods.dev/badge/skills/richkuo/rk-skills/fable-orchestrate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00117 | $0.01764 |
| Opus 5.5 | $0.00047 | $0.00706 |
| Sonnet 5.5 | $0.00023 | $0.00353 |
| Haiku 4.5 | $0.00012 | $0.00176 |
Grade A, and why
fable-orchestrate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Shape | Dispatch |
|---|---|
| 1–2 sequential pieces | Agent 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. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 19d ago Changed 7d914307cfdd
- 23d ago Changed 716edfa903d6
- 26d ago Changed d2489b84c0fb
- 27d ago Changed · +2 tokens per session 5626a17fe17e
- 1mo ago Changed · -6 lines 27a32f4aa387
- 1mo ago Changed · -12 lines · -10 tokens per session 75e38e5481f6
- 1mo ago First seen · 92 lines · 125 tokens per session scan A 17d03d976fc9
fable-orchestrate is a skill published in the GitHub repository richkuo/rk-skills (50 stars, last pushed 3d ago), licensed MIT. It adds 117 tokens to every session and 1,764 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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