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 Rylaa/fable5-opus5-orchestrator --skill playbookgit clone --depth 1 https://github.com/Rylaa/fable5-opus5-orchestratorWrote 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/rylaa/fable5-opus5-orchestrator/playbook)<a href="https://agentmods.dev/skills/rylaa/fable5-opus5-orchestrator/playbook"><img src="https://agentmods.dev/badge/skills/rylaa/fable5-opus5-orchestrator/playbook/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/rylaa/fable5-opus5-orchestrator/playbook"><img src="https://agentmods.dev/badge/skills/rylaa/fable5-opus5-orchestrator/playbook.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 | $0.00057 | $0.00895 |
| Opus 5 | $0.00028 | $0.00447 |
| Sonnet 5 | $0.00011 | $0.00179 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
playbook 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 5d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrator Playbook
Applies to both chair profiles (FABLE and OPUS). The injected core profile always wins on routing and limits; this file is the detail behind its one-liners.
Research pipeline — parallel fan-out, no mid-flight dumps
YOU pick the questions and sources — never a fetch worker. ONE
sonnet (medium) per source: it fetches the source VERBATIM to
./.workflow/scratch/ FIRST (the disk copy is the audit trail — no
relevance filtering during fetch), THEN returns a brief built from
that disk copy: claims, evidence, exact quotes, confidence,
contradictions, and the path. A final sonnet (high) synthesizes
across the briefs. YOU check the synthesis and its verbatim evidence
against the ledger and decide. Intermediates never enter your
context.
Subagent output contract (enforced)
Every subagent returns:
- ledger items addressed, by number
- summary
- VERBATIM code/config/errors/quotes the conclusion depends on — at most 10 lines inline; anything longer goes to ./.workflow/scratch/ and the report carries the path
- confidence: "confident" / "uncertain because X"
- "out of scope but noticed"
Reports are at most 40 lines TOTAL. A violating return is rejected and re-run — never silently accepted.
Spawn economics — batch before you multiply
Every spawn pays a fixed overhead (system prompt, project rules,
tool schemas) before doing any useful work. Batch similar mechanical
steps into ONE worker with a checklist; spawn separately only when
true parallelism or isolation pays for that overhead. Read-only
agents share the repo concurrently; parallel EDITORS each run with
isolation: "worktree".
Forks
subagent_type: "fork" clones your FULL conversation at your model
and spends the usage limit: at most 2 per session, only while the
conversation is still short, and only for bounded follow-ups that
lean on context a spec cannot carry. Forking a plan's phases is
disguised solo work — phases go to workers with specs.
Named teammates — the user watches the work
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.
- 5d ago Changed · -193 lines · +2 tokens per session 1fff076f4186
- 9d ago First seen · 282 lines · 55 tokens per session scan A 2eeb84c0eee2
playbook is a skill published in the GitHub repository Rylaa/fable5-opus5-orchestrator (71 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 895 once invoked, about $0.0003 per session on Opus 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.
Other skills, from other repositories
cco-budget
Configure token budget limits, auto-compact settings, and view current budget status (model-aware — Claude 5 lineup, Opus 5 default fallback, full 1M context at standard price).
cco-config
View and tune CCO's behavior thresholds — re-read warnings, cache staleness, prompt-coach length bands, and the /cco-pack budget cap.
cco-patterns
Share learned file patterns across a team — export an anonymized digest of what's usually waste/useful/co-edited, and import a teammate's so a fresh clone benefits day one.
cco-tools
Show what tools actually cost in tokens — learned per-tool averages from observed results, replacing the hardcoded MCP/Agent guesses.
cco-clean
Clean up old tracking data and reset statistics.
github-pr-creation
Creates GitHub Pull Requests with automated validation and task tracking. Use when user wants to create PR, open pull request, submit for review, or check if ready for PR. Analyzes commits, validates task completion, generates Conventional Commits title and description, suggests labels. NOTE - for merging existing…