# Fable Advisor > Consult Claude Fable for a compact strategic second opinion from Codex or Claude Code. Use before costly-to-revert architecture, schema, API, migration, or unattended-automation decisions; after two distinct failed fix attempts; for consequential plan or completion review; or when the user explicitly asks for Fable advice. Codex invokes claude -p with an explicit Fable model; Claude Code uses its native advisor agent. Covers compact evidence packets, three-interaction budgets, and weighing the verdict. Skip routine reversible edits and questions merely about Fable. - Skill: `czlonkowski/fable-advisor` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add czlonkowski/fable-advisor` - Raw SKILL.md: https://api.skillmd.com/api/skills/czlonkowski/fable-advisor/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: czlonkowski (https://skillmd.com/u/czlonkowski) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/czlonkowski/fable-advisor --- # Fable Advisor — the expensive-consultant protocol You are a Codex or Claude Code orchestrator consulting Claude Fable. Keep the consult small: Fable supplies a verdict; the parent gathers evidence, builds, and verifies. Do not assume the parent is Opus or that a fixed price ratio applies to its model or subscription. The Codex CLI route pins `claude-fable-5-1`; the native Claude agent uses the host's `fable` alias. Use `xhigh` effort for consultations on both routes; preserve an explicit user model or effort choice. 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", skip an automatic consult. Honor an explicit request for a Fable second opinion even if the parent already runs on a premium model. ## 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). ## When NOT to consult - Easily reversed work: renames, refactors within a file, adding a flag, styling, copy. - Decisions already made — by the user, by repo convention, by AGENTS.md / CLAUDE.md, or by memory. - Routine implementation where the path is clear, however long it is. - Generation of any kind: code, documents, configs, workflows. If you're tempted to ask Fable to "write" something, that's your job. - A first failed attempt. Try a genuinely different approach before escalating. - A low-stakes, easily-cancelled loop: read-only polling ("check deploy status every 5 min") needs no review — cancelling a loop is free. Consult only when the routine acts on external systems unattended, or cost per iteration × frequency is material. - To offload thinking you haven't done yet. Fable amplifies a well-framed question and wastes money on a vague one. ## Budget rules (hard) - **Default one consult per task. Hard cap: three executed Fable interactions per task** (native spawns, CLI invocations, and follow-ups combined; attempts rejected before model execution do not count). Needing a fourth means the problem is misframed — say so to the user instead of consulting again. - **Announce every consult** in one line before invoking, so the user sees the spend: `Consulting Fable on (trigger: , consult 1/3).` - **Batch questions.** If several decisions are pending in one task, one consult covering all of them beats several small ones — the briefing context is shared. - **Cap the exchange.** One spawn plus at most one reconcile follow-up per question. Fable is a consultant, not a pair-programming partner. - **Respect user overrides.** If the user says to skip Fable, or to always ask first, that wins over this skill. - Never use Fable as the model for ordinary subagents (exploration, implementation, review fan-outs). This advisory protocol does not restrict separately authorized Fable work. ## How to consult Route by the parent host: - **Codex:** use `claude -p`, not a Codex native sub-agent with a Claude model name. Read [the Codex CLI route](references/codex-cli.md) for the exact command, tool isolation, result checks, and fresh-invocation follow-ups. No Claude plugin install is required: the skill folder contains everything needed to construct the brief. - **Claude Code:** use the native agent below. The `fable` alias is host-configurable; record the actual model when available and honor explicit version requests. ### Claude Code native agent Preferred — the dedicated advisor agent (read-only tools, verdict-format discipline built into its system prompt, `effort: xhigh` in its definition): ``` Agent( subagent_type: "fable-advisor", # plugin-installed form: "fable-advisor:fable-advisor" name: "fable-consult-1", # so you can SendMessage a follow-up description: "Fable consult: ", prompt: , run_in_background: false # the verdict blocks your next step ) ``` If the spawn is rejected because of the `name` parameter (this happens when you are yourself a subagent — named spawns are top-level-only), retry the same call without `name`; follow-ups then need a fresh spawn carrying a one-paragraph recap instead of SendMessage. A rejected spawn that never executed does not count against the budget. Fallback — if neither `fable-advisor` nor `fable-advisor:fable-advisor` is in the available agent list, spawn `general-purpose` with `model: "fable"` and an explicit host-supported `xhigh` effort setting. If the spawn schema cannot set effort, use a custom read-only agent definition with `effort: xhigh`; do not assume the parent effort or a prompt request changes the runtime setting. Prepend this preamble to the briefing: > You are a one-shot strategic advisor to a capable orchestrator that does all the > building itself. Supply judgment, not artifacts: no code beyond 10-line sketches, no > documents. Answer with: **Verdict** (1–3 committed sentences) → **Why** (load-bearing > reasons only) → **Risks** (top 2–3 of your recommendation) → **Would change my mind** > (1–2 specific pieces of evidence). Stay under 300 words. Only read the named files, > and narrowly, if the briefing is insufficient. Your final message is the deliverable. Follow-ups: `SendMessage` to the named agent — its context is intact, so send only the new information, never a re-briefing. ## The briefing packet Curate the briefing rather than forwarding the parent transcript. Aim for 800 words, with a 1,200-word ceiling including excerpts. Native advisors can read named files narrowly. The default Codex CLI route has no tools: include decisive excerpts and label paths as evidence citations, not requests to open files. Runtime system context may still be added; a fresh process is not a zero-overhead prompt. ``` DECISION: CONTEXT: <2–4 sentences: what's being built, for whom, where it runs> OPTIONS: A.