fable-loop

Orchestrates multi-stage task execution with parallel evidence workers and adversarial verification.

Updated Jun 25, 2026
One-click install
npx skills add https://github.com/ismaelsoilet/ismaelsoilet-landing --skill fable-loop-ismaelsoilet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fable-loop
Source: https://github.com/ismaelsoilet/ismaelsoilet-landing/tree/main/.agents/skills/fable-loop
Command: npx skills add https://github.com/ismaelsoilet/ismaelsoilet-landing --skill fable-loop-ismaelsoilet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Complex tasks often fail because agents skip planning, guess at facts mid-execution, or ship unverified changes. This Skill enforces a disciplined four-stage loop—plan, execute, verify, audit—so multi-step work is evidence-driven and adversarially checked before delivery. ## Core Features & Use Cases - Parallel Evidence Fan-Out: Spawns concurrent research and codebase-exploration workers that return distilled, cited findings before any plan is committed. - Adversarial Verification: Launches 1-3 attacker workers that attempt to refute the change from distinct lenses, catching defects the main thread missed. - Gated Execution: Applies intent, recall, and authorization gates so edits stay minimal, facts are never recalled from memory, and outward-facing actions require explicit user approval. - Use Case: When asked to refactor an authentication flow across a codebase, the loop gathers evidence on all call sites in parallel, commits to one plan, executes surgically, then has attacker agents try to break the change before reporting the verified outcome. ## Quick Start Ask the agent to run the fable loop on your next non-trivial task, for example by saying run the fable loop to migrate the settings page to the new API.

Frequently Asked Questions about fable-loop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run an orchestrated multi-stage task workflow with an AI agent?▼

Invoke the fable-loop on any non-trivial task. It runs four stages in order: plan with parallel evidence gathering, execute in the main thread, verify adversarially with attacker workers, then audit and report the outcome with verification evidence.

What is adversarial verification for AI-generated code changes?▼

Adversarial verification spawns 1-3 parallel workers prompted to refute the work from distinct lenses, such as proving the diff is wrong, finding inputs that break it, or detecting scope creep beyond the plan. Findings that survive review go back to execution as new work.

Does this workflow work without parallel subagent support?▼

Yes. If the framework lacks a parallel worker tool, evidence gathering and verification checks run sequentially with independent reads batched in one message. The limitation is noted in the final report.

When should I not use an orchestrated task loop?▼

Skip the loop for trivial tasks handled by a single obvious check, pure questions with no multi-step work, and tasks already inside an orchestrated GSD phase where the outer framework owns the stages.

How does the loop handle unknown facts during execution?▼

Mid-execution ignorance triggers a pause, not a guess. When an edit would rely on a memorized fact like a signature or key, the item stops, a research worker fetches the fact per the recall gate, and work resumes when it returns.