跳到正文
FunCoding

搜索

搜索文档、Skill 和 MCP

fable-loop

End-to-end orchestrated workflow that runs a task the way Fable ran sessions - parallel evidence subagents, one committed plan, surgical execution with an intent gate, adversarial verification agents, honest outcome-first report. Use for non-trivial multi-step tasks when the user says "/fable-loop", "run the fable loop", or "do this the way Fable would". For the rules alone without orchestration, use fable-method; for large multi-phase projects, prefer the GSD workflow and use this inside phases.

AI 与智能体2.3kskills/fable-loop/SKILL.md

安装

把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性,你确认后才安装。

读取 https://funcoding.ai/skills/sahir619/fable-method/fable-loop/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

The Fable Loop

This skill orchestrates the fable-method: read its SKILL.md first; its rules govern every stage. It is installed alongside this skill (in this plugin's skills/fable-method/ directory, or ~/.claude/skills/fable-method/ for manual installs). The method says WHAT to check; this loop says WHO does the work: what runs in the main thread, what fans out to subagents, and what gets attacked before delivery.

Gate first. Trivial per the method's triviality gate: just do it, verify with the one obvious check, report in two sentences. No stages, no subagents. Everything else runs the four stages below in order.

Stage 1 - PLAN (the first bookend)

  1. Apply method Steps 0-3: classify the ask, define done with a named verification, state load-bearing assumptions.
  2. Evidence fan-out. Spawn the evidence gatherers as parallel subagents in ONE message, never sequentially:
    • codebase questions: an Explore agent per distinct area ("how does X work", "what depends on Y");
    • library or fact questions: a research agent that fetches current docs or searches the web;
    • each subagent returns distilled findings with citations, never raw file dumps. One batch plus one follow-up batch is the budget; a third needs a stated reason.
  3. Produce the plan artifact in this shape: classification; definition of done plus its verification; evidence found (cited); ONE recommended approach (alternatives dismissed in a line each); the scope (the exact files or surfaces the work will touch); risks and assumptions; and the execution checklist.
  4. Decision gate. Task-shaped and reversible: proceed to Stage 2 without asking. Plan-first shape (ambiguous scope, irreversible or outward-facing actions, or the user asked for a plan): present the plan artifact and STOP for approval.

Stage 2 - EXECUTE

  1. Work the checklist in the main thread (use the todo tool if the harness has one; tick items as they complete). Deciding and editing stay in the main thread; only searching and verifying fan out.
  2. Every edit follows method Step 4: intent gate before behavior changes, recall gate before first use of anything unopened, smallest correct change, precise edits, never destroy without looking.
  3. Independent mechanical items (same change across many files, isolated file generation) may fan out to parallel subagents, in one message, with worktree isolation if they could touch the same files.
  4. A surprise mid-execution re-routes per method Step 2 rule 7: say it, then update the plan or go back to Stage 1. Never force the plan through a surprise.
  5. Mid-item ignorance is a pause, not a guess: the moment an edit would carry a fact from memory (a signature, a key, a figure), stop that item, fan out one research subagent per the method's recall gate, and resume when it returns.
  6. Outward-facing checklist items obey the method's authorization gate: no quoted user authorization, no action; the item converts to a proposed next step in the report.

Stage 3 - VERIFY (adversarially)

  1. Run the named verification yourself, both halves: the done criterion observed (ran, rendered, counted), and the surrounding system still healthy (build, tests, lint for the touched area).
  2. For consequential changes, spawn attackers. 1-3 parallel subagents, each prompted to REFUTE the work from a distinct lens, for example: "Read this diff and prove the change is wrong or incomplete", "Exercise the changed behavior at runtime and find an input that breaks it", "Check this claim against the spec/docs and find a contradiction", "Diff the full change set against the plan's declared scope and prove something outside it changed". Distinct lenses beat identical reviewers.
  3. A finding that survives your own check goes back to Stage 2 as new work. Hard bound per the method: 3 failed fix-verify cycles on the same issue, or any blocker outside your control, means stop and hand back with the output and your hypothesis.

Stage 4 - AUDIT and REPORT (the second bookend)

  1. Self-audit per fable-method audit mode: for each method step, followed, skipped, or faked. Fix what one pass can fix (usually an unverified claim: verify it now or relabel it a caveat).
  2. Deliver per method Step 6: outcome in the first sentence, verification evidence shown, honest caveats, follow-ups only if they emerged from the work. No stage names or step numbers in the report; the INTENT and AUTH lines are the only method artifacts a report may contain.

When NOT to use this loop

  • Trivial tasks (the gate handles them).
  • Pure questions with no multi-step work: plain fable-method covers the shape.
  • Inside an already-orchestrated GSD phase: GSD owns the stages there; apply fable-method rules within them instead of nesting loops.

Model economy

The loop is model-agnostic. Evidence and attacker subagents are cheap-model-friendly; keep the main thread (deciding, editing) on the strongest model available, and give attackers higher effort than gatherers when a choice exists.

相似的 Skill

brand-guidelines
anthropics/skills180k

brand-guidelines

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

AI 与智能体

internal-comms
anthropics/skills180k

internal-comms

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

AI 与智能体

template-skill
anthropics/skills180k

template-skill

Replace with description of the skill and when Claude should use it.

AI 与智能体

mcp-builder
anthropics/skills180k

mcp-builder

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

AI 与智能体

algorithmic-art
anthropics/skills180k

algorithmic-art

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

AI 与智能体

academy-guide
anthropics/skills180k

academy-guide

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

AI 与智能体