fable-advisor

A decision process for asking Claude Fable 5, a premium AI model, to advise on expensive-to-reverse technical choices.

In plain words
What is it for?
Use it before choosing system architecture, database structures, API contracts, automation workflows, or other designs where a wrong decision could cause substantial rework.
Why use it?
It helps decide when a separate expert opinion is worth the added model cost and keeps the consultation focused on judgment rather than producing code.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/czlonkowski/fables/fable-advisor
Any agent
npx skills add czlonkowski/fables --skill fable-advisor
Clone the repo
git clone --depth 1 https://github.com/czlonkowski/fables

Made for: Claude Code, Codex.

Per session 318 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,635 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash d278c3006740, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

plugins/fable-advisor/skills/fable-advisor/SKILL.md · 186 lines

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

  1. 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.
  2. 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.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Unattended-automation design. Before starting a loop, schedule, or routine that runs without a human watching — /loop, /schedule, /goal with 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).

Read the full file on GitHub · 186 lines

Changes

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.

  1. 2d ago First seen · 186 lines · 318 tokens per session scan A d278c3006740

Subscribe to this mod's changes

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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