Skills

Iterate Until Verified

Apply a prompt-agnostic execution and verification loop to any substantial task while...

Install

npx skills add MengTo/Skills --skill iterate-until-verified -g -a claude-code -y
Repository
MengTo/Skills7.2k
Category
AI and agents
Updated
3d ago
License
MIT
Context cost
Mediumabout 1,755 tokens
Runs scripts
No, instructions only

What it does

Apply a prompt-agnostic execution and verification loop to any substantial task while preserving the original request. Use when the user asks to fan out work, use subagents or independent reviewers, loop until done, benchmark against references, apply a harsh critic, compare candidates blind, improve an existing prompt with verification, or continue until explicit quality gates pass.

Iterate Until Verified

Preserve the task. Strengthen the process around it.

Choose the mode

  • Execute: Complete the original task with the workflow below. Use this mode by default.
  • Compose: When the user asks for an improved prompt rather than the finished work, return a reusable prompt wrapper. Keep the original task authoritative and unchanged inside the wrapper.

Do not silently switch from composing a prompt to executing it.

1. Lock the original task

Extract:

  • outcome and deliverables
  • audience and use case
  • supplied inputs and references
  • constraints, tools, formats, and exclusions
  • authorized actions and protected boundaries
  • explicit definition of done

Treat these as the task contract. Do not replace the subject, invent requirements, relax constraints, expand permissions, or let the verification method become the deliverable.

Ask a question only when a missing answer would materially change the work and cannot be discovered safely. Otherwise, state a reasonable assumption and proceed.

2. Convert ambition into gates

Translate words such as perfect, best, professional, production-ready, or AAA into observable checks. Select only the dimensions relevant to the task:

  • correctness and factual accuracy
  • completeness against the request
  • craft, clarity, and audience fit
  • usability and accessibility
  • robustness, edge cases, and regression safety
  • performance, security, or compliance
  • visual, editorial, or technical fidelity to a supplied benchmark

Create a compact acceptance matrix:

GateVerification methodPass conditionEvidence
Relevant quality dimensionTest, inspection, comparison, or read-backObservable binary conditionCommand, source, screenshot, output, or artifact

Prefer pass/fail conditions over vague scores. A strong reaction such as “wow” may be a useful signal, but it is never the only gate.

3. Decompose and assign

Read the whole SKILL.md on GitHub

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