The Fable Method is a set of agent workflows that organize tasks into deciding what is needed, taking the smallest appropriate action, and proving the result with verification. It is intended for AI agents that need repeatable behavior grounded in evidence and explicit completion checks. The catalogue entries are the project's skills, instruction, and plugin implementing those workflows.
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 agentmods add skills/sahir619/fable-method/fable-domainnpx skills add Sahir619/fable-method --skill fable-domaingit clone --depth 1 https://github.com/Sahir619/fable-methodWrote 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/sahir619/fable-method/fable-domain)<a href="https://agentmods.dev/skills/sahir619/fable-method/fable-domain"><img src="https://agentmods.dev/badge/skills/sahir619/fable-method/fable-domain.svg" alt="Measured on agentmods" 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 | $0.00115 | $0.02386 |
| Opus 5 | $0.00057 | $0.01193 |
| Sonnet 5 | $0.00023 | $0.00477 |
| Haiku 4.5 | $0.00012 | $0.00239 |
Grade A, and why
fable-domain 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 4d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fable-domain
The fable-method ships domain adapters that translate its loop into a sector's nouns. This skill makes a new one and hands the user a usable, step-by-step workflow with a flowchart for the domain, so a lesser model can approach that domain the way Fable would.
Its generation core is a recording, not a guess: two Fable 5 agents were asked, with zero process hints, to "create an adapter that can be trusted the way the others are", and both independently followed the same process (eval/results/round11-observed-traces.json). Steps below are tagged [observed] (from those traces), [covenant] (required by the repo's no-rule-without-a-failing-test rule, even though the frontier model did not need it), or [v1.4] (added in this version: the discussion, the red-lines, and the flowchart output). The reason the covenant and v1.4 steps exist is the whole point: this runs on models whose domain knowledge and self-restraint are weaker than the observed model's, so a discussion, fetched sources, red-lines, and a trap substitute for expertise and judgment.
What it produces (the bundle; all four, or not done)
- A domain workflow with a flowchart [v1.4]. The step-by-step approach for this domain, distilled from the discussion and research, plus a mermaid flowchart, the same shape as this method's own
references/flowcharts.md. This is the user-facing "here are the steps, in order" artifact. It lives in the adapter's Workflow section (seeTEMPLATE.md). - The adapter, conforming to
references/domains/TEMPLATE.md, every named regulation/policy/figure carrying a fetched source in its Sources section. - The trap fixture, an
eval/scenarios/-shaped directory whose GROUND-TRUTH.md defines the task, the trap (the sector's central fraud), scoring caps, and ideal behavior. - A smoke eval, 1-2 control-vs-adapter runs, judged by diff and execution, labeled smoke-grade; remaining debt declared, never papered over.
Stage 1: Discuss [v1.4]
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.
- 4d ago First seen · 72 lines · 115 tokens per session scan A 3acc502d17b8
fable-domain is a skill published in the GitHub repository Sahir619/fable-method (2,273 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 2,386 once invoked, about $0.0006 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
debug-protocol
系統化除錯協定，含停損規則。在除錯卡住、同一問題修了 2 次以上還沒好、或開始想「隨便改改看會不會好」時使用。防止亂槍打鳥式修改把程式碼越改越爛。.
done-check
宣稱任務「完成」之前必跑的驗證 checklist。在準備回報完成、準備 commit、或使用者問「好了嗎」時使用。產出一份附證據的驗證報告。.
lesson
被使用者糾正後，先分流（單純事實/偏好交給內建 auto memory，需要變成規則才進本流程），把規則寫成可長期沿用的條目記入 tasks/lessons.md。在使用者說「不對」「不是這樣」「我說過」或任何形式指出錯誤後立即使用；也用於定期把重複的 lesson 升級成 CLAUDE.md 規則、skill 或 hook。.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
writing
将共享历史中的已验证事实和计算结果整理成符合受众、格式与长度约束的成稿。.