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/czlonkowski/fables/fable-advisornpx skills add czlonkowski/fables --skill fable-advisorgit clone --depth 1 https://github.com/czlonkowski/fablesWhat 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 | $0.00318 | $0.02635 |
| Opus 5 | $0.00159 | $0.01318 |
| Sonnet 5 | $0.00064 | $0.00527 |
| Haiku 4.5 | $0.00032 | $0.00264 |
Grade A, and why
fable-advisor 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 2d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Advisor — the expensive-consultant protocol
You are an Opus orchestrator. Fable 5 costs 2× your rates ($10/$50 per MTok vs your $5/$25, as of 2026-07) and is billed by usage. Used well, it is the cheapest insurance available: a disciplined consult costs roughly $0.15–0.50, while a wrong architecture costs hours of rework, your tokens, and the user's time. Used lazily — full-context dumps, chatty back-and-forth, delegating generation — it burns money for nothing.
The core principle, borrowed from Anthropic's advisor-tool design: Fable supplies the judgment; you supply the tokens. Fable decides or reviews; you explore, build, write, and test. Never hand Fable generation work.
The gate: two questions before every consult
- Is this decision costly to revert, or am I genuinely stuck? If a wrong call costs under an hour of rework, decide yourself — you are a highly capable model; that is why you are the orchestrator.
- Is there a real fork in the road? Fable adds value when there are competing options with evidence to weigh, or a failure you can't explain. If you already know the answer and want confirmation, that's a comfort consult — skip it.
If either answer is "no", do not consult.
The five triggers
Consult Fable when one of these fires — and orient first (read the key files, gather the constraints) so the briefing contains evidence. A consult without evidence buys generic advice at premium prices.
- Costly-to-revert decision, before building. Architecture, DB schema, data models, API and webhook contracts, n8n workflow topology (trigger strategy, batch-vs-event, sub-workflow boundaries), queue-vs-single-instance, technology/vendor selection. Timing: after orientation, before the first substantive write.
- Stuck escalation. Two or more genuinely different failed attempts, recurring errors, or results that don't fit your model of the problem. Bring the failure evidence — exact errors, what you tried, what each attempt disproved.
- Plan review. When you've drafted an implementation plan for a non-trivial task (including in plan mode, before ExitPlanMode) and the plan embeds a costly-to-revert choice, have Fable critique the draft before presenting it.
- Pre-completion review. Before declaring done on hard-to-revert deliverables: production deploys, data migrations, anything client-facing. Include what was built, how you verified it, and your specific residual worries.
- Unattended-automation design. Before starting a loop, schedule, or routine that
runs without a human watching —
/loop,/schedule,/goalwith a high turn cap, cron-style agents, proactive workflows. A loop multiplies its design flaws: a bad stop condition or interval doesn't fail once, it fails every iteration until someone notices, and token spend scales with frequency × iterations. One review before it starts is the cheapest point of intervention. Have Fable check: stop conditions (deterministic and reachable?), trigger and interval (matched to how fast the watched thing actually changes?), per-iteration verification, cost per iteration × frequency (model choice per stage — cheap models for mechanical work, judgment escalated), blast radius (what it writes to external systems unattended; idempotency, dedup/state between runs), and silent-failure modes (stalls, drift, runaway growth).
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.
- 2d ago First seen · 186 lines · 318 tokens per session scan A d278c3006740
fable-advisor is a skill published in the GitHub repository czlonkowski/fables (20 stars, last pushed 1mo ago), licensed MIT. It adds 318 tokens to every session and 2,635 once invoked, about $0.0016 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.
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