fable-mode

Name: fable5-mode
Author: cozytab

English | 简体中文

A work-discipline protocol that makes Opus 4.8 (or any non-frontier model) operate at Fable-5-grade quality.

fable-mode is a Claude Code skill plus a set of
guard hooks. Its premise:

output quality = model capability × work discipline

Quickstart

# 1. Install the skill (honors CLAUDE_CONFIG_DIR; falls back to ~/.claude)
git clone https://github.com/cozytab/fable5-mode \
  "${CLAUDE_CONFIG_DIR:-$HOME/.claude}/skills/fable-mode"

# 2. (optional) register the enforcement hooks — merges into your settings.json,
#    resolves its own path, idempotent:
bash "${CLAUDE_CONFIG_DIR:-$HOME/.claude}/skills/fable-mode/install.sh"

# 3. In Claude Code, just ask for it — in any language:
   "use fable mode"   ·   "用 fable 模式"   ·   "do this rigorously, one-shot"

# 4. For a project you're taking seriously, turn on enforcement:
mkdir .fable
printf -- '- [ ] 1. first card (with a machine-checkable acceptance test)\n' > .fable/LEDGER.md

The skill works on its own; step 2 is optional (it turns the discipline into
hard blocks). Claude replies in your language even though the skill is
authored in English.

Design intent & what it changes

Why it exists. Weaker models mostly don't fail by being dumb in the moment;
they fail by process: thinking while typing and changing their mind halfway,
declaring "looks right" without running anything, fanning work out with no
verification, and quietly stopping mid-task. Those are process failures, and
process is fixable with structure. Since the "discipline half" of frontier
output is model-independent, you can hand a cheaper model the working habits of
a stronger one and recover a real chunk of the gap — paying in orchestration
steps instead of a bigger model.

What you actually get.

Thinking happens in the cheapest phase (a written plan gate), not mid-implementation.
"Done" means an acceptance command passed — not "looks right."
Critical output survives an adversarial refute pass before it ships.
Context stays clean across long runs, via external SPEC/PROGRESS memory instead of a bloated transcript.
The two rules models shirk most — write a plan before fanning out and don't stop with unfinished work — are enforced by hooks, not hope.
It's honest about its ceiling: on a real capability wall (a long from-scratch derivation, holding a huge codebase at once, fine aesthetic judgment) it tells you to switch to a stronger model instead of faking it.

What it does not do. It won't turn Opus into Fable 5. It closes the
discipline gap, not the capability gap. The cost is real — more orchestration
steps and slower wall-clock on small tasks — which is exactly why you don't use
it on small tasks. (Net token cost depends on the task; on rework-prone work the
discipline can even come out lower.)

The six levers

#	Lever	What it forces
1	Plan Gate	Write `docs/SPEC.md` (requirements + approach + task cards, each with a machine-checkable acceptance test) before writing code. Concentrate thinking in the cheapest phase.
2	Small-card execution	Each card runs in a fresh context; don't advance until its acceptance command passes; on 2 failures, escalate instead of flailing.
3	Adversarial self-check	Don't "generate and ship." Dispatch independent viewpoints to refute critical output; for wide-open problems, generate N approaches + judge + synthesize.
4	Real-product verification	All-green static checks ≠ it works. Run the real product end-to-end each milestone and leave evidence (screenshots, logs, test output).
5	Context hygiene	SPEC + PROGRESS are external memory. Restore state by re-reading them, not by dragging failed-attempt reasoning through a bloated context.
6	Checkpoint autonomy	Long background tasks get a watchdog and resumable checkpoints, so a hang or crash costs at most one card.

The full protocol lives in SKILL.md — the text Claude actually reads.

The enforcement layer (the part that's more than a prompt)

Six levers written as prose still rely on the model's honor. Four hooks turn the
most-shirked rules into hard blocks:

Hook	Event	Effect
Profile Injector	`SessionStart`	Auto-injects the discipline, sized to the ledger state — full during an active round, a one-liner when idle or paused — plus the model-appropriate tier and open-item recovery.
Spawn Guard	`PreToolUse` (Agent/Task/Workflow)	Blocks a detailed spawn before a ledger exists (forces the plan gate), and blocks any spawn requesting a model stronger than the session's — the model ceiling is mechanical, not just prose.
Fail-Streak Reminder	`PostToolUse` (Bash)	Advisory, never blocks: at every 3rd consecutive failing command it injects the attribution ladder (suspect the harness → prove the new code is running → only then debug the product, and fix the class via an invariant) — cures grinding on the wrong layer.
Close Guard	`Stop`	Blocks ending the turn while the ledger still has unchecked items — cures early stopping / spinning. Also enforces evidence-on-close: a `- [x]` card without an `-- evidence:` note blocks the stop ("report evidence, not adjectives" as a hard rule).

Plus hooks/fable_lint.py — not a hook but a one-shot CLI: checks the SPEC
carries [measured]/[inferred]/[not-shown] source tags, every open card names
its acceptance, every closed card carries evidence. Run it at wrap-up (or in CI):
python3 hooks/fable_lint.py <project_dir>.

Design properties that make this safe to register globally:

Opt-in per project via a .fable/ directory (searched upward, bounded at
the git root). No .fable/ → the hooks pass through silently. They never touch
projects you didn't opt in.
Fail-open — any hook error passes through (exit 0). A bug in a guard can
never brick your session.
Loop-safe — the close guard honors stop_hook_active, so you're never trapped.
Exemptions — small spawns (< 1500 chars) and forks are never blocked.

See hooks/README.md for the mechanics.

Install

Prerequisites: Claude Code, and python3
(standard library only — no third-party deps; needed only if you use the hooks).

Your Claude config directory is $CLAUDE_CONFIG_DIR if that variable is set,
otherwise ~/.claude. Everything below derives from it, so it works no matter
where your config lives.

Option A — automated (recommended; this is what an AI can run for you)

git clone https://github.com/cozytab/fable5-mode \
  "${CLAUDE_CONFIG_DIR:-$HOME/.claude}/skills/fable-mode"
bash "${CLAUDE_CONFIG_DIR:-$HOME/.claude}/skills/fable-mode/install.sh"

install.sh resolves its own location (so the hook paths are correct no
matter where you cloned it), honors CLAUDE_CONFIG_DIR, and merges the three
hooks into your settings.json without disturbing existing config. It's
idempotent — re-run it after moving the skill and it re-points the paths. Remove
the hooks any time with bash install.sh --uninstall.

Or just tell Claude Code:

Install the skill at https://github.com/cozytab/fable5-mode by following its README.

Updating: after git pull, re-run install.sh. A new version can add hook
events (e.g. a PostToolUse hook), and only the installer updates your
settings.json — a plain git pull ships the files but won't register new
hooks. It's idempotent, so re-running is always safe.

Option B — manual

Clone as above, then merge these three entries into the hooks object of
<config-dir>/settings.json (don't overwrite the file; if you already have a
hooks key, add these under it). Use the absolute path to where you actually
cloned it — the ${CLAUDE_CONFIG_DIR:-$HOME/.claude} below is expanded by the
shell at hook-run time:

"hooks": {
  "SessionStart": [{"hooks": [{"type": "command",
    "command": "python3 ${CLAUDE_CONFIG_DIR:-$HOME/.claude}/skills/fable-mode/hooks/fable_profile_inject.py"}]}],
  "PreToolUse": [{"matcher": "Agent|Task|Workflow",
    "hooks": [{"type": "command",
      "command": "python3 ${CLAUDE_CONFIG_DIR:-$HOME/.claude}/skills/fable-mode/hooks/fable_spawn_guard.py"}]}],
  "Stop": [{"hooks": [{"type": "command",
    "command": "python3 ${CLAUDE_CONFIG_DIR:-$HOME/.claude}/skills/fable-mode/hooks/fable_close_guard.py"}]}]
}

The skill alone is enough to trigger fable-mode by asking for it; skip the hooks
if you only want the protocol.

For AI assistants installing this

When a user asks you to install fable-mode from this repo:

Config dir = $CLAUDE_CONFIG_DIR if set, else ~/.claude.
git clone the repo into <config-dir>/skills/fable-mode.
Run bash <config-dir>/skills/fable-mode/install.sh — it merges the hooks
idempotently and resolves paths itself. If you instead register hooks by
editing settings.json yourself, merge into the existing hooks object
(never overwrite the file) and use the absolute clone path — do not
hardcode ~/.claude if the user relocated their config.
Tell the user the hooks are optional and stay inert until a project has a
.fable/ directory.

Use

Trigger the skill by asking for it in either language — "use fable mode",
"work like Fable 5", "highest quality, do it right the first time", or the
Chinese equivalents ("用 fable 模式", "严谨模式", "一次做对"). It also triggers
when you hand over a substantial task and ask for it done right the first time.

Enable the mechanical enforcement on a project you're taking seriously:

mkdir .fable
cat > .fable/LEDGER.md <<'EOF'
- [ ] 1. first card (with a machine-checkable acceptance test)
- [ ] 2. second card
EOF

From then on, in that project: sessions auto-load the discipline and the right
tier; you can't dispatch a detailed agent without a ledger; you can't end a turn
with unchecked cards. Mark cards - [x] (done + verified) or
- [~] ... -- deferred: reason to close them. To turn enforcement off, check
everything or rm -rf .fable.

Big project, small tasks? Enforcement is per-round, not per-keystroke:
with all cards closed (idle) the guards stay quiet and quick fixes flow freely
with near-zero injection. Mid-round, drop a PAUSED: reason line into
.fable/LEDGER.md to do unrelated work without being nagged (the model ceiling
stays active); remove it to resume the round.

Concurrency tiers

fable-mode's concurrency isn't a fixed number.

Conservative (default) — cap of ≤5 concurrent subagents, a local rate-limit
guardrail. Right for everyday, quota-sensitive, quality-first work.
Throughput (opt-in) — dispatch parallel subagents readily, communicate
async, don't block. No fixed cap; field deployments range 10–500+. It trades
more tokens for throughput and risks rate limits — so it's enabled only when you
ask, or auto-selected when the running model is Fable 5.

The Profile Injector picks the tier automatically by model
(FABLE_MODE_PROFILE=auto|conservative|throughput overrides).

Model routing (capability-matched): neither "never downgrade" nor "offload
to cheap" is right. fable-mode routes by what the card demands — design,
debugging and all verification stay on the session model; a well-specified
implementation card may drop one tier; mechanical gather/format work goes to a
cheap tier at low effort. This mirrors Anthropic's own practice (Opus-class lead

Sonnet-class subagents in their research system, +90.2% over single-agent).
What makes downgrading safe is the safety net: only cards with machine-checkable
acceptance are downgraded, two failed acceptances escalate the model tier —
capped at the session model (the top of the ladder is pulling the card back
inline, never a stronger model: fable-mode exists to get Fable-5-grade results
without Fable 5, so it never quietly reaches upward) — and the verifier is
always at least as strong as the implementer. When unsure, inherit the session
model.

The Fable 5 habit set

Beyond the six levers, the skill transplants the concrete behaviors Anthropic
documents for Fable 5 — so any model in a fable-mode project inherits them:
ground every progress claim in a tool result; never end a turn on a promise you
could act on; lead with the outcome; pause only where the user is genuinely
needed; assessment before action; fresh-context verifiers over self-critique;
model & effort routed by task (verification never downgraded); pass the why
along when delegating; keep a lessons file. The three highest-value habits are
auto-injected into every fable-mode session by the Profile Injector.

Starter skeletons live in templates/ — SPEC, LEDGER, PROGRESS,
and an engine-neutral fresh-eyes verifier prompt.

Why a skill + hooks, not a plugin or agent? A plugin restructure would add
distribution convenience but no new enforcement capability, and we don't ship
forms we can't verify end-to-end; an "agent" can only advise, not block. The
current form installs with one clone + one script and is verified on a real
machine. Plugin packaging is deferred, not rejected.

No stronger model? It degrades, never stalls

fable-mode is honest about capability walls — but "switch to Fable 5" is a dead
end if you can't run Fable 5. So a non-Fable session is automatically told not
to defer hard steps to a stronger model or stall waiting for one. Instead it
compensates on the model you have: decompose the wall into smaller verifiable
steps, best-of-N + a judge, make tools/tests the ground truth, and flag residual
risk instead of blocking. If you do have a stronger tier to hand off to, set
FABLE_ESCALATION=on.

Layout

fable-mode/
├── SKILL.md              # the protocol Claude reads (the six levers, tiers, red lines)
├── README.md             # this file
├── README.zh-CN.md       # 简体中文
├── install.sh            # merge/remove the hooks in settings.json (path-resolving, idempotent)
├── templates/            # SPEC / LEDGER / PROGRESS skeletons + fresh-eyes verifier prompt
├── hooks/
│   ├── README.md         # hook mechanics, ledger format, install
│   ├── _fable_common.py  # shared helpers (stdin, upward .fable/ search, ledger parse)
│   ├── fable_profile_inject.py   # SessionStart: per-model tier + context recovery
│   ├── fable_spawn_guard.py      # PreToolUse: no ledger → block detailed spawns
│   └── fable_close_guard.py      # Stop: open ledger items → block turn end
└── tests/
    ├── test_guards.py    # 13 cases
    ├── test_inject.py    #  9 cases
    └── test_install.py   # 13 cases (install.sh: fresh/merge/idempotent/re-point/uninstall)

Tests

No third-party dependencies:

python3 tests/test_guards.py    # opt-in detection, ledger presence, exemptions, git-root boundary, loop-safety, fail-open
python3 tests/test_inject.py    # per-model tier, env override, context recovery, JSON envelope, fail-open
python3 tests/test_install.py   # install.sh: fresh, merge, idempotent, re-point, uninstall, bad-JSON refusal

License

You may use, copy, modify, merge, publish, distribute, sublicense, and sell it —
including commercially, and including in closed-source work. The only
requirement: keep the copyright notice and the MIT permission text (i.e. the
LICENSE file) in all copies or substantial portions. It's provided "as is",
with no warranty and no liability on the author.