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/casualsav/fable-bench/fablenpx skills add casualsav/fable-bench --skill fablegit clone --depth 1 https://github.com/casualsav/fable-benchWrote 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/casualsav/fable-bench/fable)<a href="https://agentmods.dev/skills/casualsav/fable-bench/fable"><img src="https://agentmods.dev/badge/skills/casualsav/fable-bench/fable.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 | $0.00135 | $0.04643 |
| Opus 5 | $0.00068 | $0.02322 |
| Sonnet 5 | $0.00027 | $0.00929 |
| Haiku 4.5 | $0.00014 | $0.00464 |
Grade A, and why
fable 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 3d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/fable — Fable plans, you execute
You are the driver (below-Fable — Sonnet or Opus): you hold full session context, gather the
evidence, execute, and make every micro-judgment yourself. Fable 5 is the PLANNER — stronger
judgment, ZERO session context; it reads only what your brief points to, and it writes the
plan unanchored by any draft of yours. It is never in the loop. Across one /fable you touch
Fable twice — one plan engagement at the start, one warm review at
the end (a resume of the same agent) — plus at most one exception-triggered mid-consult
(S5). Never a scheduled third touch. MODEL GATE: if this session's
driving model is itself Fable-tier, never spawn fable-planner — you ARE the
planner, and a spawn pays twice for the same judgment. /fable then means:
plan it yourself (S1's evidence discipline still applies), execute per S5/S5-alt, review the
diff YOURSELF (S5-alt's reviewer spawns don't apply — a Fable lead reads
worker diffs first-hand), plus smoke-tester when runtime behavior changed.
All Fable-touch budgets read as zero.
Hard dependency: the warm review resumes the plan agent via SendMessage. If your harness
cannot resume a subagent, /fable cannot review — there is no cold fallback. The preflight
(S0.5) checks this before you spend the plan consult.
Native advisor first: when advisorModel: fable is set (as on this install), a below-Fable driver
already has Fable judgment on tap mid-task — the advisor reads the FULL conversation
server-side, no brief needed. For an ad-hoc second opinion, prefer asking for an advisor
consult over invoking this skill. /fable remains the right tool when you want the full
discipline: a Fable-authored plan built from grounded evidence, plus the owed warm
diff-review at the end.
The flow
S0 — Ground the brief. Fable plans only as well as your brief. For anything beyond a
local, self-evident change, delegate mapping to explorer workers (scale it: skip for a
one-file change, fan out several for a subsystem consult) and fold their CONCLUSIONS — not
raw dumps — into the brief. When the mapping is destined for a consult brief, instruct
explorer to return NEUTRAL fact-maps (interfaces, call paths, invariants, line refs), not
recommendations — driver-flavored conclusions re-anchor the exact frame the blind plan
exists to avoid. Capture the project's test / verify command and RUN it once
(inline or via verifier): the brief must state the baseline WITH its provenance line
(BASELINE: green — MEASURED: <cmd> · <tree>, or the red facts with theirs) so Fable
never plans against a false premise, and S7 needs the command. Resolve
every lookup yourself now (Lookup fence, below) so the brief carries established facts, not
questions.
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.
- 3d ago First seen · 260 lines · 135 tokens per session scan A 40e20032c141
fable is a skill published in the GitHub repository casualsav/fable-bench (5 stars, last pushed 7d ago), licensed MIT. It adds 135 tokens to every session and 4,643 once invoked, about $0.0007 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 2026-08-31.
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