written by ForgeCat

fablize

A Claude Code harness that keeps multi-step work grounded in evidence, verification, and task-specific discipline packs.

Tags

• harness
• verification
• completion
• workflow
• agentic

Installation

npx forgecat install @forgecat/fivetaku_fablize

Skills / Agents / Commands

• fablize — A harness that makes Opus (or any Claude model) behave like Fable — it enforces seeing a task through to the end, with evidence and verification, as procedure. Use when starting a multi-step task (2+ sequential stories), long autonomous work, debugging or root-cause investigation, building render/executable artifacts (HTML, SVG, games, charts), or when the user says "fablize", "see it through", "verify as you go", "split into goals". skill
• setup — Set up fablize always-on (inject the operating block into CLAUDE.md). On setup it also stars the repo on GitHub via gh — no separate prompt. command
• fablize-user-prompt-router — Inject the smallest matching fablize discipline pack when a submitted prompt signals debugging or render/executable artifact work. hook
• fablize-user-prompt-router-codex — Inject the smallest matching fablize discipline pack using Codex hookSpecificOutput JSON. hook
• fablize-finish-the-work — Detect early termination where the assistant only promises work without doing it, then block once to continue the work. hook

Details

Compatibility

Platforms

Dependencies

• bash for hook and setup scripts
• python3 for the goal ledger and setup helpers
• Optional: gh for setup's GitHub star helper

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written by original source

English

fablize — run Opus like Fable

A Claude Code plugin that makes Opus (or any Claude model) see a task through to the end — with evidence and verification — as procedure, not as luck.

Why

When Fable 5 shipped, I ran a controlled comparison of Fable 5 and Opus 4.8 (an A/B set of 19 runs plus 26 real working sessions, ~1,500 tool calls). The finding:

• On closed, answer-bearing work (code, logic, builds), the two models were effectively tied.
• The gap appeared only on open-ended work, and its nature was "following an implication one step further."
• That depth is model capability — it could not be transferred by instructions or a harness. An injection experiment confirmed it: Opus could not reproduce the defects Fable found on its own.
• But the procedure of good work — actually running what you build, seeing it through, investigating systematically — does transfer.

fablize applies only the procedures whose effect was verified. It does not raise the model's ceiling; it makes the model reach its own ceiling.

What transfers and what doesn't

The non-transferable rows are the model's job (or a human's), not a harness's. When you hit them, fablize tells you to escalate instead of pretending.

What's included (verified only)

• Verification grounding — render/executable artifacts (HTML, SVG, games, charts) are run and observed before completion.
• Multi-story verification gate — goals.py decomposes work and refuses a groundless "done."
• Investigation protocol — reproduce, compete hypotheses, trace the full causal chain.
• Early-stop hook — blocks promising-without-doing.
• Per-task router — injects only the matching verified discipline.

Negligible or unverified ideas (style mimicry, broad reasoning injection, a silent-recovery guard, a review-recall scan) are not shipped. They stay in personal development until a controlled experiment confirms their effect.

Install

/plugin marketplace add fivetaku/fablize
/plugin install fablize

The per-task router (a UserPromptSubmit hook) registers automatically.

For always-on operation (the rules resident in context), run once:

bash ${CLAUDE_PLUGIN_ROOT}/setup/setup.sh    # choose local (recommended) or global

Uninstall: bash ${CLAUDE_PLUGIN_ROOT}/setup/uninstall.sh

If fablize helps, a ⭐ on the repo means a lot — setup asks once and can open the page for you.

How it behaves

• Trigger with /fablize or phrases like "see it through", or automatically when installed always-on.
• 2+ stories → decompose + verification gate. Debugging → investigation protocol. Render artifact → verification grounding. Hard task → adaptive thinking plus an /effort xhigh suggestion.
• At the capability ceiling, escalate to a stronger model or a human.

Honest limits

• It cannot raise model capability. Open-ended creative quality and self-driven discovery are out of reach — that is a model-choice decision, not a harness one.
• The effect numbers come from a small, single-family (Claude) self-measurement (the 19-run A/B set). The direction is solid; the decimals are not asserted.
• The early-stop hook can misfire on a declarative offer ("I'll write the report if you want") — phrasing offers as questions avoids it.

License

MIT