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mrtooher

fable-mode

A Claude skill that activates Fable-style agentic behavior: explicit multi-stage planning, sub-agent delegation, and self-verification.

AI EngineeringPrompt & ScaffoldPython
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创建于 2026-06-13 · 更新于 2026-10-14 · 今日第 12982 名
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mrtooherLagunaswift

mrtooher/fable-mode 排名第 12,982,有 876 个星标。

同类别比较

mrtooher/fable-mode 排名第 12,982,此处有 8 个同类条目;oxylabs/agent-skills 排名第 12,983。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别AI Engineering
子类别Prompt & Scaffold
关键词Planning, Prompt engineering, System prompts, Tool use
星标数876
分叉数95
贡献者3
创建日期2026-06-13
更新日期2026-10-14
1 天星标数0
7 天星标数4
排名12,982
上次排名6,271
排名变化-6,711
7 天星标排名6,307
上次 7 天星标排名6,311
7 天星标排名变化4
总星标排名12,365
上次总星标排名12,337
总星标排名变化-28
README 摘录

Ladder: Haiku → Sonnet → Opus → the user. Sections below that describe them are kept as history. Execution discipline for Claude on large tasks: a written stage map, delegation to named agents where the runtime allows, a verification check at every stage that can actually **fail**, a skeptical self-review, and a mandatory cold double-check before anything is delivered. It is a checklist, not a capability transplant. Benchmarked three times so the README can say what it does and does not

do (see Evidence). Every model, every task, whether or not the loop runs. A panel of fresh checker agents (Haiku for mechanical checks, Sonnet for requirements, Opus to adjudicate) attacks the *finished* artifact. Every fable runner calls it before handing anything over. ** Numbered stages, expected output each, one verifiable artifact per stage. ** Reasoning stages to a Sonnet worker, mechanical stages to a Haiku worker, cold checks to a read-only verifier. ** A test that runs, a file in the expected

shape, a source actually fetched, an output diffed against spec. "I reviewed it and it looks right" is not a check. Name the exact command or mark the stage unverified. ** Fix or flag a real weakness; a clean pass stated plainly beats a manufactured caveat. ** Fresh eyes, ternary verdicts (PASS / FAIL / UNVERIFIABLE), fix and re-check before delivery. v3: delegation is structural Field feedback on v2 (r/claudeskills): telling a model *in prose* to spawn a worker almost always runs

the task inline. It cannot produce artifacts; every one must come from a named worker. Opus by default; runs the same definition with (per-invocation override, documented for Claude Code and confirmed in Cowork). Evidence Three benchmark rounds, deterministic graders, tasks built with **unstated** traps (duplicate rows, grep-invisible , errata chains superseded by meeting notes, a real federal data file to fetch or fabricate). On Opus it buys real sources over plausible ones. ai**: Settings → Capabilities → Skills → add each (root

is ). The agents are not loadable there; the skills detect that and fall back to inline mode, saying so. **Do not install anything from as a skill. ** Those files are agent definitions: second-person system prompts whose frontmatter carries and . Folder name must equal the field in each SKILL. Files When NOT to use it One obvious approach that fits in a single pass → do it directly. Staging a trivial task buries the answer under ceremony.

常见问题

记录了多少星标和分叉?

mrtooher/fable-mode 有 876 个星标和 95 个分叉。

仓库排名是多少?

mrtooher/fable-mode 排名第 12,982。

记录的语言和类别是什么?

mrtooher/fable-mode 有 876 个星标,语言为 Python,类别为 AI Engineering。

哪个仓库排名最接近?

mrtooher/fable-mode 与 oxylabs/agent-skills 比较,后者排名第 12,983,有 876 个星标。

README

fable-mode

Current: v6 (2026-10-10). Live skills: fable-mode (root SKILL.md), fable-opus, fable-sonnet, double-check, execution-guardrails. Ladder: Haiku → Sonnet → Opus → the user. fable-fable, fable-haiku and the v3 agents/ routing are retired — see SUPERSEDED.md. Sections below that describe them are kept as history.

Execution discipline for Claude on large tasks: a written stage map, delegation to named agents where the runtime allows, a verification check at every stage that can actually fail, a skeptical self-review, and a mandatory cold double-check before anything is delivered.

It is a checklist, not a capability transplant. Benchmarked three times so the README can say what it does and does not do (see Evidence).

The ladder

Haiku  →  Sonnet  →  Opus  →  Fable 5.1  →  the user

One skill per rung, same core loop, tuned to that tier's known failure mode:

Skill Model Use it when Known gap it guards
fable-mode whatever is running default; adapts intensity to the model —
fable-fable Claude Fable 5.1 the deliverable needs an audit trail (worklog, reproducible checks, flagged judgment calls) or spans sessions none measured — a provenance runner, not an accuracy runner
fable-opus Claude Opus peak synthesis short of Fable; orchestrates Sonnet/Haiku workers trusts its own introspection; scope growth
fable-sonnet Claude Sonnet balanced default for thorough work skips the failable check, substitutes "looks right"
fable-haiku Claude Haiku bulk, cheap, parallel mechanical work skips verification under time pressure

Two always-on companions:

  • execution-guardrails — verify-before-flag, warning batching, word-boundary find-and-replace. Every model, every task, whether or not the loop runs.
  • double-check — the delivery gate. A panel of fresh checker agents (Haiku for mechanical checks, Sonnet for requirements, Opus to adjudicate) attacks the finished artifact. Every fable runner calls it before handing anything over.

Core loop

  1. Stage map first. Numbered stages, expected output each, one verifiable artifact per stage. Living document; at most two full replans per run — a third means the requirements are ambiguous, go back to the user.
  2. Delegate by name. Reasoning stages to a Sonnet worker, mechanical stages to a Haiku worker, cold checks to a read-only verifier. Workers don't spawn workers.
  3. Verify with a check that can fail. A test that runs, a file in the expected shape, a source actually fetched, an output diffed against spec. "I reviewed it and it looks right" is not a check. Name the exact command or mark the stage unverified.
  4. Self-critique. Fix or flag a real weakness; a clean pass stated plainly beats a manufactured caveat.
  5. Double-check. Fresh eyes, ternary verdicts (PASS / FAIL / UNVERIFIABLE), fix and re-check before delivery.

v3: delegation is structural

Field feedback on v2 (r/claudeskills): telling a model in prose to spawn a worker almost always runs the task inline. So v3 moves the discipline into real agent definitions under agents/:

  • fable-orchestrator — no Write/Edit tool. It cannot produce artifacts; every one must come from a named worker. Opus by default; fable-fable runs the same definition with model: "fable" (per-invocation override, documented for Claude Code and confirmed in Cowork).
  • fable-worker-sonnet / fable-worker-haiku — one bounded assignment, exact output path, named pass condition, evidence in the report.
  • fable-verifier — read-only; gets the spec and the artifact, never the producer's reasoning.

The skills shrink to routers. Full history in V3-CHANGES.md.

Evidence

Three benchmark rounds, deterministic graders, tasks built with unstated traps (duplicate rows, grep-invisible getattr("calc" + "_total"), errata chains superseded by meeting notes, a real federal data file to fetch or fabricate).

Tier Effect of the skill Source
Fable 5.1 0 delta — 100/100 in all 16 paired runs (n=2/cell, 4 tasks incl. open-ended real-data fetch); ~1.1× tokens, ~2× wall BENCHMARK-2026-09-02.md
Opus / Sonnet 0 delta on closed graded tasks; real on open-ended research — Opus+skill fetched and parsed the NIAAA state dataset, Opus baseline shipped synthetic trend lines July 2026 rounds
Haiku real but noisy: +25 / −17 across rounds; raises the floor on mechanical diligence, cannot supply synthesis July 2026 rounds

Read: the loop's value inverts with model strength. On Fable 5.1 it buys provenance. On Opus it buys real sources over plausible ones. On Haiku it buys verification the model would otherwise skip — sometimes. The harness is in bench/; rerun it.

Install

Claude Code (user scope — skills to ~/.claude/skills/):

git clone https://github.com/mrtooher/fable-mode.git && cd fable-mode && ./install.sh

Cowork / claude.ai: Settings → Capabilities → Skills → add each /SKILL.md (root SKILL.md is fable-mode). The agents are not loadable there; the skills detect that and fall back to inline mode, saying so.

Do not install anything from agents/ as a skill. Those files are agent definitions: second-person system prompts whose frontmatter carries tools: and model:. Loaded as a skill they land in the main thread and assert things about it that are false there — fable-orchestrator in particular tells its reader it has no Write tool and must delegate to worker agents that do not exist on a skill-only surface. The result is a stall, or an inline run narrating a delegation that never happened — the exact failure v3 exists to prevent. Agents go in agents/, skills go in skills.

Folder name must equal the name: field in each SKILL.md or the skill will not trigger.

Files

SKILL.md                    fable-mode (default, adaptive)
fable-fable/SKILL.md        Fable 5.1 runner
fable-opus/SKILL.md         Opus runner (orchestrator route)
fable-sonnet/SKILL.md       Sonnet runner
fable-haiku/SKILL.md        Haiku runner
execution-guardrails/       always-on rules
double-check/               delivery gate
agents/                     orchestrator, sonnet worker, haiku worker, verifier
bench/                      fixtures generator, graders, prompts, results.json
BENCHMARK-2026-09-02.md     Fable 5.1 results
EXAMPLE.md                  worked before/after: the failable check catching a bug
V3-CHANGES.md               why delegation became structural
install.sh                  Claude Code installer

When NOT to use it

One obvious approach that fits in a single pass → do it directly. Staging a trivial task buries the answer under ceremony. The loop earns its cost only when a one-shot attempt would plausibly miss something — or when you need to show your work.

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