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 ardhaecosystem/fable-method --skill fable-loopgit clone --depth 1 https://github.com/ardhaecosystem/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/ardhaecosystem/fable-method/fable-loop)<a href="https://agentmods.dev/skills/ardhaecosystem/fable-method/fable-loop"><img src="https://agentmods.dev/badge/skills/ardhaecosystem/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/ardhaecosystem/fable-method/fable-loop"><img src="https://agentmods.dev/badge/skills/ardhaecosystem/fable-method/fable-loop.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 | $0.00108 | $0.02497 |
| Opus 5.5 | $0.00043 | $0.00999 |
| Sonnet 5.5 | $0.00022 | $0.00499 |
| Haiku 4.5 | $0.00011 | $0.00250 |
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 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fable-loop
fable-method is the single-thread loop: one agent classifies, gathers, decides, edits, verifies, reports. fable-loop is the same loop with the expensive parts parallelized: evidence gathering fans out to subagents during planning, and verification turns adversarial with attacker subagents that try to refute the finished work. The structure does not change - only who does the searching and who does the attacking.
Division of labor, binding. The main thread decides and edits. Subagents only gather (evidence) and attack (refute). Nothing that changes the working tree, the build, or any external surface is ever delegated. If a subagent returns a finding that demands a decision, that decision is made in the main thread.
Model economy. Evidence subagents and attacker subagents are cheap-model-friendly: they run narrow, well-scoped tasks (search this, read that, try to break this) where the cost of a wrong first pass is a retry, not a damaged repo. Run the main thread on the strongest model you have - it holds the plan, makes the irreversible calls, and writes the edits. A cheap-model subagent that returns a wrong file path costs one re-dispatch; a strong-model main thread that made the wrong architectural call costs the whole task.
Usage
/fable-loop <task> full four-stage loop
/fable-loop plan <task> Stage 1 only: deliver the plan artifact and stop for approval
/fable-loop resume <task> continue from Stage 2 after an approved plan
Load fable-method first. This skill assumes its steps 0-6 and its rules (intent gate, recall gate, authorization gate, twin check, artifact gate). When this skill and fable-method disagree on a rule, fable-method wins; this skill only adds orchestration on top.
When NOT to use fable-loop
- Trivial. fable-method's triviality gate (one file, under ~10 lines, no new behavior, you already know the change) fires before this skill. Trivial work gets the change, the one obvious check, and a one-sentence report. Spinning up subagents for that is waste.
- Pure questions and assessments. Step 0 classified the ask as a question or assessment with no change required: run fable-method single-threaded. There is nothing to fan out edits for, and adversarial verification of a non-change is theater.
- Already-orchestrated phases. If the user has already set up a multi-agent workflow, a pipeline, or a fable-judge suite run, do not wrap it in this loop. This skill orchestrates work that is not yet orchestrated; layering two orchestrators hides which one owns each decision.
- Attended and high-touch. When the user is actively steering each step, the parallelism buys nothing and the decision gates just add latency. Run fable-method inline.
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 First seen · 91 lines · 108 tokens per session scan A bd3387216dd7
fable-loop is a skill published in the GitHub repository ardhaecosystem/fable-method (13 stars, last pushed 2mo ago), licensed MIT. It adds 108 tokens to every session and 2,497 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 2026-08-31.
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