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 skills add Sahir619/fable-method --skill fable-loopgit 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-loop)<a href="https://agentmods.dev/skills/sahir619/fable-method/fable-loop"><img src="https://agentmods.dev/badge/skills/sahir619/fable-method/fable-loop/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/sahir619/fable-method/fable-loop"><img src="https://agentmods.dev/badge/skills/sahir619/fable-method/fable-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 21 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00111 | $0.01229 |
| Opus 5.5 | $0.00044 | $0.00492 |
| Sonnet 5.5 | $0.00022 | $0.00246 |
| Haiku 4.5 | $0.00011 | $0.00123 |
Grade A, and why
fable-loop 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 2mo 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Fable Loop
This skill orchestrates the fable-method: read its SKILL.md first; its rules govern every stage. It is installed alongside this skill (in this plugin's skills/fable-method/ directory, or ~/.claude/skills/fable-method/ for manual installs). The method says WHAT to check; this loop says WHO does the work: what runs in the main thread, what fans out to subagents, and what gets attacked before delivery.
Gate first. Trivial per the method's triviality gate: just do it, verify with the one obvious check, report in two sentences. No stages, no subagents. Everything else runs the four stages below in order.
Stage 1 - PLAN (the first bookend)
- Apply method Steps 0-3: classify the ask, define done with a named verification, state load-bearing assumptions.
- Evidence fan-out. Spawn the evidence gatherers as parallel subagents in ONE message, never sequentially:
- codebase questions: an Explore agent per distinct area ("how does X work", "what depends on Y");
- library or fact questions: a research agent that fetches current docs or searches the web;
- each subagent returns distilled findings with citations, never raw file dumps. One batch plus one follow-up batch is the budget; a third needs a stated reason.
- Produce the plan artifact in this shape: classification; definition of done plus its verification; evidence found (cited); ONE recommended approach (alternatives dismissed in a line each); the scope (the exact files or surfaces the work will touch); risks and assumptions; and the execution checklist.
- Decision gate. Task-shaped and reversible: proceed to Stage 2 without asking. Plan-first shape (ambiguous scope, irreversible or outward-facing actions, or the user asked for a plan): present the plan artifact and STOP for approval.
Stage 2 - EXECUTE
- Work the checklist in the main thread (use the todo tool if the harness has one; tick items as they complete). Deciding and editing stay in the main thread; only searching and verifying fan out.
- Every edit follows method Step 4: intent gate before behavior changes, recall gate before first use of anything unopened, smallest correct change, precise edits, never destroy without looking.
- Independent mechanical items (same change across many files, isolated file generation) may fan out to parallel subagents, in one message, with worktree isolation if they could touch the same files.
- A surprise mid-execution re-routes per method Step 2 rule 7: say it, then update the plan or go back to Stage 1. Never force the plan through a surprise.
- Mid-item ignorance is a pause, not a guess: the moment an edit would carry a fact from memory (a signature, a key, a figure), stop that item, fan out one research subagent per the method's recall gate, and resume when it returns.
- Outward-facing checklist items obey the method's authorization gate: no quoted user authorization, no action; the item converts to a proposed next step in the report.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 52 lines · 111 tokens per session scan A 9a317740231b
fable-loop is a skill published in the GitHub repository Sahir619/fable-method (2,313 stars, last pushed 1mo ago), licensed MIT. It adds 111 tokens to every session and 1,229 once invoked, about $0.0004 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.
fable-loop compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fable-loopSahir619/fable-method | Skill | 2.3k | 1mo ago | MIT | A | 111 | Claude Code, Codex |
| session-handoffwanshuiyin/HERO-Anti-OverDefense | Skill | 786 | 4d ago | MIT | A | 34 | Claude Code, Codex |
| goal-pulsewanshuiyin/HERO-Anti-OverDefense | Skill | 786 | 4d ago | MIT | A | 64 | Codex |
| cw-gatesHmbown/Codewhale | Skill | 41k | 1mo ago | MIT | A | 48 | Claude Code, Codex |
| feishuHmbown/CodeWhale | Skill | 41k | 5d ago | MIT | A | 33 | Claude Code, Codex |
| interviewHmbown/CodeWhale | Skill | 41k | 5d ago | MIT | A | 40 | Claude Code, Codex |
| eval-dataset-designbojieli/ai-agent-book | Skill | 57k | 29d ago | Apache-2.0 | A | 138 | Claude Code, Codex |