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 kpab/claude-fable-5-skills --skill effort-calibratorgit clone --depth 1 https://github.com/kpab/claude-fable-5-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/kpab/claude-fable-5-skills/effort-calibrator)<a href="https://agentmods.dev/skills/kpab/claude-fable-5-skills/effort-calibrator"><img src="https://agentmods.dev/badge/skills/kpab/claude-fable-5-skills/effort-calibrator/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/kpab/claude-fable-5-skills/effort-calibrator"><img src="https://agentmods.dev/badge/skills/kpab/claude-fable-5-skills/effort-calibrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00118 | $0.01273 |
| Opus 5.5 | $0.00047 | $0.00509 |
| Sonnet 5.5 | $0.00024 | $0.00255 |
| Haiku 4.5 | $0.00012 | $0.00127 |
Grade A, and why
effort-calibrator 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 1mo 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Effort Calibrator
On Fable 5 and 5.1, effort is the primary dial trading intelligence against latency and cost. Settings inherited from earlier models are usually wrong here, and so are settings inherited from Fable 5 by 5.1: an effort name does not buy the same amount of thinking across generations, so re-run your sweep on 5.1 even if you already tuned Fable 5. Reference points: Fable 5 at lower effort frequently beats earlier models at xhigh; Fable 5.1 at medium roughly matches Fable 5 at lower cost, and at low it often scores higher than Opus and Sonnet models at a similar cost per task, so include it wherever you would otherwise run a smaller model at higher effort.
Starting points by workload
| Workload | Start at |
|---|---|
| Routine transforms, classification, short edits, chat, subagents | medium (try low if latency matters) |
| Most analysis and writing | high (the general default) |
| Coding and agentic/tool-heavy work | high (the API and Claude Code default on Fable 5 and 5.1) — even for workloads that ran at xhigh on earlier Opus models; escalate to xhigh only for the most capability-sensitive tasks |
| Hardest capability-sensitive work: large migrations, multi-day autonomous runs, novel research | xhigh — on 5.1 this is where the gains over Fable 5 are largest, at the price of longer thinking before the first response |
Frontier problems only, where evals show headroom above xhigh and token spend is unconstrained |
max |
The signal for max is evals showing headroom above xhigh on your actual task: on most workloads it adds significant cost for small gains and can tip into overthinking.
Adjustment signals
Lower effort when:
- Tasks complete correctly but take longer than the work warrants
- The session is interactive and waiting hurts more than marginal quality helps
- Output shows over-deliberation: long context-gathering before trivial actions
Raise effort when:
- First-shot correctness matters more than turnaround (one-way-door changes, unattended runs)
- The task benefits from rigorous self-verification, which higher effort does noticeably better
- A task failed at the current level in a way that looks like shallow reasoning, not missing information
- At
low, 5.1 answers from memory where it should have called a search or retrieval tool — raise effort for the turns that need fresh information, or add a prompt line saying that recognizing a name is not the same as knowing its current state
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.
- 1mo ago Changed · +8 lines · +27 tokens per session 1e974528ae73
- 1mo ago First seen · 41 lines · 91 tokens per session scan A 6d138104685d
effort-calibrator is a skill published in the GitHub repository kpab/claude-fable-5-skills (19 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 1,273 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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