
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:
| Gate | Verification method | Pass condition | Evidence |
|---|---|---|---|
| Relevant quality dimension | Test, inspection, comparison, or read-back | Observable binary condition | Command, 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.

