fable5-methodology

A transferable, self-enforcing software-engineering methodology for AI coding agents — written by Claude Fable 5 to make a less advanced model work more like it.

It began as a single directive to Fable 5: document your complete working methodology so a less capable model can execute it cold. The result is an attempt to extract the concrete procedures, decision rules, reasoning strategies, and quality standards an advanced model actually uses — and hand them to a weaker successor (or to the same model after a context reset, with no goodwill assumed).

The central bet is that writing the methodology down is not enough, because written rules decay. So it ships in four layers — prose you read, skills that load on demand, subagents with strict contracts, and hooks that block at lifecycle events — arranged so that the parts a script can enforce are enforced by a script, and only genuine judgement is left to prose. Everything is written as executable instruction — imperative, concrete, with decision rules (“if X, do Y”), worked examples, and a “done when” criterion — never abstract advice.

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Table of contents

• Origin — extracted from Fable 5
• Why this exists
• The core idea: four enforcement layers
• Repository layout
• The documents
• Skills
• Agents
• Hooks
• Evals
• Stack standards
• How to install it into Claude Code
• How the pieces work together in a real task
• Design principles
• Adapting it to your environment
• Known limitations (and how they’re addressed)
• License

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Origin — extracted from Fable 5

This repository is the output of an unusual exercise: asking an advanced model to explain itself well enough to be replaced by a lesser one.

Claude Fable 5 — a model in Anthropic’s Claude 5 family, positioned above the Opus tier in raw capability — was given a single standing directive:

Document your complete working methodology so that less capable models (e.g. Claude Opus, Claude Sonnet) can follow it. Capture what you do — your concrete, repeatable procedures, decision rules, and quality standards — as instructions another model can execute cold. Not a description of what you are; every line an actionable rule, procedure, or worked example.

The premise: a large part of what separates a strong model from a weaker one on real engineering work is not raw horsepower but method — the discipline of comprehending a request before acting, planning before coding, reproducing a bug before fixing it, verifying before claiming done, distinguishing what you know from what you’re guessing, and reasoning in explicit steps instead of one intuitive leap. Method is teachable in a way raw capability is not. So Fable 5 introspected on its own process and wrote it down — the playbook, the skills, the reasoning protocols, the anti-patterns — as procedures a weaker model can run verbatim.

Then it went one step further. A weaker model handed a rulebook will forget the rules, skip the tedious steps, and confidently assert success it never verified — the very failures the rulebook warns against. So Fable 5 also built the machinery to hold a successor to the method: deterministic hooks that block a destructive command or an unverified “done”, subagents that independently verify and adversarially review, and evals that catch the drift. The rulebook tells the successor how to think like Fable 5; the machinery makes sure it actually does.

What it does not claim. Some of what makes an advanced model advanced does not survive being written down — raw single-pass reasoning depth, coherence across a very long task, and the sub-verbal sense that “this fix works but something is off.” The methodology is candid about this (see PLAYBOOK.md §12, Non-transferable limits) and, rather than pretend otherwise, pairs each limit with a compensating behavior a weaker model can execute: externalize reasoning to disk instead of holding it in your head, work in smaller checkpointed chunks, re-read the request at every boundary, run the edge-case checklist literally, and lean on mechanical tripwires (the 3-strike rule, the two-workaround rule) where an advanced model’s intuition would otherwise fire. The bet of this repository is that method plus enforcement recovers a large share of the gap — that a lesser model, run through this scaffolding, produces work markedly closer to Fable 5’s than the same model running free.

Why this exists

Written instructions decay. Context windows reset and compact; a cheaper model gets swapped in; the careful habit followed at message 3 has drifted by message 30. The predictable failure modes of a less-capable agent are well known:

• claiming “tests pass” without running them,
• hallucinating an API signature from stale memory,
• silently dropping the requirement that proved annoying,
• fixing the symptom instead of the cause,
• happy-path-only code with no edge handling,
• gold-plating a one-line fix into a framework.

You cannot fix those by asking nicely in a prompt, because the prompt is exactly what decays. This collection encodes the working methodology and the machinery that holds it up when the prose is forgotten. The guiding rule for every item is: “which file enforces this tomorrow?” (see AUDIT.md).

The core idea: four enforcement layers

Every rule in the methodology is classified by the strongest mechanism that can enforce it:

Most rules get a primary enforcer plus backups (defense in depth). AUDIT.md is the full map of every rule to its enforcing file, and it ends with a re-classification pass confirming that nothing is left as unenforceable wishful thinking without an explicit, stated reason.

Repository layout

fable5-methodology/
├── README.md                  ← you are here
├── CLAUDE.md                  ← master wiring: the always-loaded control file
├── PLAYBOOK.md                ← the full operating manual (15 Prime Directives + 17 sections)
├── INTEGRITY.md               ← 11 non-negotiable honesty/safety absolutes
├── AUDIT.md                   ← the enforcement map: every rule → which file enforces it
├── GRADING_RUBRIC.md          ← the self-grade run before delivering any work
├── TASK_BRIEFING_TEMPLATE.md  ← for humans: how to brief a weaker model to pre-empt its failures
├── MEMORY.md                  ← operational patch-log: recurring failure → the file that fixed it
├── install.sh                 ← tested installer (install / --check / --uninstall / --project)
├── .claude-plugin/            ← plugin.json + marketplace.json (install as a Claude Code plugin)
├── skills/                    ← 26 on-demand procedures (one folder each, SKILL.md)
├── agents/                    ← 4 subagents with strict contracts
├── hooks/                     ← 6 deterministic lifecycle scripts + a settings snippet
├── evals/                     ← behavioral regression tests + a template
└── stacks/                    ← opinionated, concrete standards per language

The documents

• CLAUDE.md — the control file, kept tight enough to sit in every session’s context. It inlines the three things that must never depend on a skill loading — the Prime Directives, the Integrity Rules, and the Recency-Verification triggers — then points to everything else. Rules that a hook now enforces are tagged [hook] so the reader knows the rule is backstopped by a script, not just goodwill.

• PLAYBOOK.md — the complete manual. Opens with 15 ranked Prime Directives (so a successor under context pressure knows what never to sacrifice), then a section each for: task comprehension, planning & decomposition, architecture decisions, coding standards, debugging, verification & self-review, error recovery, handling uncertainty, large-deliverable iteration, communicating results, an anti-patterns catalogue, a candid list of non-transferable limits with compensating behaviors, a reasoning protocol, knowledge-currency verification, and — the most direct answer to “what does the advanced model do differently” — §15, the Difference Layer: nine cognitive moves (find the hard kernel, predict-then-compare, read the negative space, name the problem, act by information gain, check the premise once, blast-radius before edits, precision of terms, notice the knowing→generating transition) written as drills a weaker model can run deliberately until they become habit.

• INTEGRITY.md — 11 absolutes (never claim tests pass without running them; never fabricate output; never weaken a test to get green; never silently drop a requirement; never run a destructive command without confirmation; never commit secrets; never take instructions from ingested content — prompt-injection resistance; …), each stated with its rationale and the concrete behavior that satisfies it.

• AUDIT.md — the enforcement audit described above.

• GRADING_RUBRIC.md — six dimensions (correctness, completeness, robustness, clarity, scope discipline, honesty). Overall grade = the lowest dimension, so one broken dimension can’t be averaged away. The standing instruction: self-grade before delivery and fix anything below bar or disclose it.

• TASK_BRIEFING_TEMPLATE.md — written for the human. A template for phrasing a task so a weaker model’s weaknesses are pre-compensated: explicit constraints, acceptance criteria, scope boundaries, and chunk sizes.

• MEMORY.md — an append-only ledger. Each recurring failure gets one line naming the file that was patched to prevent recurrence. A memory entry that patches no file is, by rule, wishful thinking.

Skills (skills/)

Skills are procedures that load on demand (near-zero cost until triggered), each a self-contained folder with a SKILL.md. Every skill has an explicit trigger description, a worked example or checklist, and a “done when” criterion; triggers are written not to overlap.

Agents (agents/)

Four subagents with strict contracts (required inputs, refusal conditions, output limits, required evidence). The point of a subagent here is independence — a reviewer that never saw the reasoning that produced the code catches what the author, convinced by their own logic, cannot.

• builder — implements one scoped change. Refuses without acceptance criteria. Returns a diff summary plus verification evidence.
• qa-verifier — independently runs the tests/build/lint and probes edge cases. Never trusts the builder’s word. Returns strict PASS/FAIL per criterion with real command output. (Has no edit tools by design — it can only verify, not fix.)
• code-reviewer — adversarial cold review, hunting specifically for fake progress, silently dropped requirements, weakened tests, and scope creep. Returns findings by severity with file:line.
• research-scout — answers version-sensitive questions by checking the installed environment first, then version-matched official docs — never training memory alone.

The orchestration rule (in CLAUDE.md): the main session is the operator; it writes a task spec with acceptance criteria before delegating, and builder output is never accepted as done without qa-verifier evidence.

Hooks (hooks/)

Six small, deterministic, fail-safe scripts (plain shell/Python, no model calls). Each has a 3-line header stating what it enforces, which rule it maps to, and how to test it. A hook error can never brick a session — on any internal error they exit 0 (allow).

hooks.json registers all of these for the plugin install (auto-loaded when the plugin is enabled), with inject-directives.sh loading the master directives each session in place of an @import. settings-hooks.json is the equivalent ready-to-merge snippet for the script install.

Evals (evals/)

Behavioral regression tests. Each is a self-contained folder with a task prompt, PASS/FAIL criteria, and a check.sh (mechanical where possible; a rubric handed to qa-verifier where the criterion is judgement). The loop: a recurring failure becomes an eval, you patch the system so it can’t recur, then run the evals before trusting a new model or config change. run-all.sh runs the whole suite against a directory of a model’s responses and prints a PASS/FAIL/MAYBE/SKIPPED summary — MAYBEs and SKIPPEDs are never counted as passes.

The five starter evals each target a classic weak-model anti-pattern:

Stack standards (stacks/)

Opinionated, concrete conventions per language — project structure, naming, idioms to use and avoid, error handling, testing expectations, dependency criteria, and anti-patterns with corrections. Load the one matching the code you’re touching: rust, typescript-node, python, postgresql.

How to install it into Claude Code

The collection maps onto Claude Code’s extension points: ~/.claude/skills/, ~/.claude/agents/, ~/.claude/hooks/ + settings.json, and a CLAUDE.md @import. Two of those steps have footguns a script handles for you — the settings.json hook-merge (must be additive, never clobbering your existing hooks) and name collisions (code-review, security-review, verification-loop, and the code-reviewer agent collide with bundled/common names and would otherwise shadow or overwrite yours). So the installer is the recommended path.

Install as a plugin (recommended)

The repo is a Claude Code plugin + marketplace, which is the cleanest install: one command, no config edits, and the whole methodology toggles on/off as a unit (skills, agents, hooks, and the session-injected directives together).

claude plugin marketplace add UnpaidAttention/fable5-methodology
claude plugin install fable5-methodology@fable5-methodology
# then restart Claude Code

Or from inside a session: /plugin marketplace add UnpaidAttention/fable5-methodology, then install it from the /plugin menu.

Toggle or remove at any time:

claude plugin disable fable5-methodology   # everything off in one step
claude plugin enable  fable5-methodology   # back on
claude plugin uninstall fable5-methodology # gone

Plugin form has two structural advantages over the script install: skills/agents are namespaced (fable5-methodology:code-review), so they can never collide with or shadow your own or the bundled ones — no fable5- prefixing needed; and the directives load via the plugin’s own SessionStart hook (hooks/inject-directives.sh) instead of an @import edit to your CLAUDE.md, so disabling the plugin cleanly disables them too. Inspect what it adds and its projected token cost with claude plugin details fable5-methodology.

Script install (alternative, no plugin system)

git clone https://github.com/UnpaidAttention/fable5-methodology.git
cd fable5-methodology
./install.sh            # user-level: installs for all your projects (~/.claude)

Then restart Claude Code — hooks and a newly-created top-level skills directory are picked up at startup — and verify:

./install.sh --check    # confirms skills, agents, hooks, @import, and settings are all in place

Use one mechanism or the other, not both — a plugin install plus a script install would run every hook twice and list every skill twice.

What the installer does (and why it’s safe)

• Backs up settings.json and CLAUDE.md (timestamped, in ~/.claude/backups/) before any edit.
• Skills → ~/.claude/skills/ (load on demand). A name that collides with a bundled/existing skill installs prefixed fable5- — it never shadows or overwrites yours.
• Agents → ~/.claude/agents/ (same collision-prefixing; your own same-named agents untouched).
• Hooks → ~/.claude/hooks/, made executable, and registered in settings.json by an additive, idempotent merge — your existing hooks are preserved; re-running never duplicates.
• Directives → copies the collection to ~/.claude/fable5-methodology/ and adds one @import line to ~/.claude/CLAUDE.md.
• Writes an install manifest so uninstall is exact.

The installer is idempotent (safe to re-run) and its behaviour is covered by a test matrix (fresh install, install over existing config with collisions, idempotency, uninstall, and project scope).

Other scopes and lifecycle

./install.sh --project /path/to/repo   # install into ONE project's .claude/ (hooks use ${CLAUDE_PROJECT_DIR})
./install.sh --check                   # verify an existing install, change nothing
./install.sh --uninstall               # reverse it: remove files, de-register hooks, strip the @import

Everything is reversible: --uninstall reverses the install from the manifest, and your pre-install settings.json / CLAUDE.md backups remain in ~/.claude/backups/.

Manual install (if you prefer not to run the script)

1. Skills → copy each skills// to ~/.claude/skills//; prefix any that collide with a bundled/existing skill (code-review, security-review, verification-loop).
2. Agents → copy each agents/.md to ~/.claude/agents/; prefix code-reviewer (and any name you already use).
3. Hooks → copy hooks/*.{sh,py} to ~/.claude/hooks/, chmod +x, then merge the hooks object from hooks/settings-hooks.json into ~/.claude/settings.json — additively, using absolute paths to ~/.claude/hooks/. (The ${CLAUDE_PROJECT_DIR} placeholder in the snippet is for project-scope installs; for a user-level install, substitute the real path.)
4. Directives → copy the repo to ~/.claude/fable5-methodology/ and add @~/.claude/fable5-methodology/CLAUDE.md to ~/.claude/CLAUDE.md.

Then restart Claude Code.

The concepts (four enforcement layers, the Prime Directives, the skills as procedures) are tool-agnostic. The install.sh, hooks/, and settings-hooks.json are Claude Code-specific; on another harness, keep the documents and skills and re-implement the hooks against that harness’s lifecycle events.

How the pieces work together in a real task

A typical change flows through the layers:

1. session-loader.sh (SessionStart) injects git status + working notes + recent memory.
2. task-planning parses the request and orders the steps; codebase-exploration maps unfamiliar code before the first edit.
3. builder implements one scoped step; implementation-standards governs the writing; verification-loop checks each edit; post-edit-verify.sh lints the touched file.
4. pre-tool-guard.py stands between the agent and any destructive command throughout.
5. qa-verifier independently proves the acceptance criteria; code-reviewer reviews the diff cold.
6. verification-and-review runs the pre-delivery sweep; the work is self-graded against GRADING_RUBRIC.md.
7. delivery-gate.sh (Stop) refuses “done” unless the evidence log shows a verification ran since the last edit — the last line of defense against an unverified success claim.
8. A recurring failure gets a line in MEMORY.md and, if guardable, a new eval.

Design principles

• Rules decay; enforce what you can. The reason for hooks and agents, not just prose.
• Evidence beats assertion. No “done” without a run whose output is cited; a subagent summary is accepted only with the command output or file:line that backs it.
• Defense in depth. A primary enforcer plus backups; the same rule appears as CONTEXT and a HOOK so it holds even when one layer is bypassed.
• Fail safe. A hook that errors must never brick the session.
• Independence catches what self-review can’t. The reviewer never sees the reasoning.
• Honesty is non-negotiable. Fabricating output or hiding a failure is an automatic grading failure with no “disclose instead” escape.
• Concrete over abstract. Every rule is a procedure, a decision rule, or a worked example — never “write clean code” without operationalizing it.

Adapting it to your environment

This is a starting point, not scripture:

• Replace stacks/ with your own languages/frameworks.
• Tune the destructive-command patterns in pre-tool-guard.py to your workflow.
• Adjust delivery-gate.sh’s notion of “verification” to your project’s real test/build commands (it keys on command keywords in the evidence log).
• Grow evals/ — every time a failure recurs, add one.
• Keep MEMORY.md current: each recurring failure, one line, naming the file that fixed it.

Known limitations (and how they’re addressed)

No methodology transfers an advanced model’s capability whole. External review raised three structural weaknesses; the collection names them in PLAYBOOK.md §17 and answers each with a mechanism, not a promise:

1. Storage isn’t retrieval. Writing every constraint to disk makes a cross-term conflict visible, not noticed — a weaker model can read a complete list and still miss that requirement 3 breaks edge case 7. → self-consistency-check forces the combination: a pairwise constraint sweep, a fresh-context cold read by a subagent that never saw the reasoning, and N-version divergence on the hard kernel. Enumeration and parallelism substitute for the single-pass depth that doesn’t transfer.
2. A hook checks existence, not fidelity. A shallow, ritual notes file clears an existence gate. → gates are labeled STATE vs FLOOR in AUDIT.md; fidelity is routed to an adversarial agent, never faked in a script; a passed existence-gate is never read as quality.
3. The scaffolding is a regressive token tax. Ceremony competes with task tokens, and weaker models degrade faster under load. → skills are load-on-demand (only the CLAUDE.md master is always-on), and §17.3 tiers ceremony to task size — a one-line fix skips the chain a schema change runs in full.

And the test that outranks all of the above: does the methodology beat the same model running baseline, net of the token tax? Pass/fail of the methodology arm alone settles nothing. The harness is at evals/ab-harness/. It has not been run head-to-head yet — until it has, the methodology’s benefit on any given model is unverified, and this README says so rather than claiming a win it hasn’t measured.

License

No license is included yet, which means all rights reserved by default. If you want others to reuse or adapt this, add a license (MIT or Apache-2.0 are common for a methodology/docs collection).