Iterate Until Verified
Meng ToCommunityCreated by Meng To
- Skill
- Meta (authoring, orchestration, planning, memory)
- Agent tooling
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Iterate Until Verified provides a structured methodology for managing substantial tasks by separating execution from verification. It forces the translation of subjective quality goals into observable acceptance gates, ensures independent review of work, and mandates evidence-based progression. The skill supports two modes: direct execution of a task or the composition of a reusable prompt wrapper that enforces this rigorous workflow for future use.
What it does
- Extracts a task contract from user requests to define clear deliverables and constraints.
- Converts subjective quality requirements into an observable acceptance matrix.
- Decomposes tasks into independent workstreams for efficient execution.
- Enforces a separation between implementer and verifier roles.
- Requires evidence-based verification for every quality gate.
- Generates reusable prompt wrappers for iterative task management.
When to use it
- When a task requires high precision and multiple quality checks.
- When you need to ensure work is reviewed by an independent party.
- When you want to improve an existing prompt by adding a verification loop.
- When a task involves complex requirements that benefit from a structured, gate-based approach.
Advantages
- Prevents the implementer from being the sole judge of their own work.
- Replaces vague quality goals with concrete, observable pass conditions.
- Ensures the original task remains authoritative throughout the process.
- Provides a clear, evidence-based audit trail for final deliverables.
Usage examples
- “Write a technical report on cloud migration and iterate until verified.”
- The agent will define a contract for the report, establish acceptance gates for technical accuracy and clarity, draft the content, and perform an independent verification pass before finalizing.
- “Compose a prompt wrapper for my data analysis task using the iterate-until-verified workflow.”
- The agent will return a structured prompt template that includes the authoritative task, the iterative process steps, and the final response requirements for your specific analysis.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/mengto/iterate-until-verified/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip iterate-until-verified.zip -d .agents/skills
Documentation by Meng To
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.
3. Decompose and assign
Split the task into the smallest meaningful workstreams with clear ownership, inputs, outputs, and integration boundaries.
Fan out only workstreams that are genuinely independent.
Keep coupled edits with one owner to avoid racing changes.
Give each worker the original task contract and only the context it needs.
Require every worker to return an artifact or evidence, not a confidence claim.
Keep one integrator responsible for cross-workstream consistency and regressions.
Use subagents or delegated workers when they are available, permitted, and useful. Otherwise, perform the workstreams sequentially while preserving the same ownership boundaries.
4. Separate making from judging
Do not let an implementer be the sole approver of its own work.
Give the verifier:
the original task contract
the acceptance matrix
the candidate artifact
the relevant benchmark or source material
Withhold the implementer’s rationale and self-assessment unless the verifier needs them to reproduce a check. Instruct the verifier to find failures first, cite evidence, reject unsupported claims, and return a gate-by-gate verdict.
For blind comparison:
anonymize and randomize candidates when practical
compare like with like using the same conditions
keep the evaluator blind to author or candidate identity, not to the task or rubric
do not call a comparison blind when obvious identity cues remain
5. Match proof to the work
Use the strongest verification surface available:
Code: focused tests, typechecks, builds, linters, security checks, runtime behavior, and regression tests.
Visual work: rendered output at relevant sizes, interaction checks, accessibility checks, and side-by-side comparison with an accessible reference.
Research or analysis: primary sources, reproducible calculations, citation checks, and contradiction searches.
Writing: factual checks, brief coverage, audience fit, structure, and an editorial pass against representative references.
Plans or decisions: constraint coverage, dependency checks, failure scenarios, feasibility, and explicit tradeoffs.
External actions: exact target resolution followed by post-action read-back.
Never substitute a self-rating for evidence. Never invent a benchmark, source, test result, screenshot, or blind verdict.
6. Run the loop
Repeat:
Produce or improve the candidate.
Run every applicable gate.
Record
pass,fail, orblockedwith evidence.Route each failure to the responsible workstream.
Make the smallest revision that addresses the evidence.
Re-run the failed gate and any affected regression gates.
Integrate only verified work.
Continue while required gates fail and a safe, in-scope action can make meaningful progress. Do not churn on the same approach after repeated failure; change the approach or report the blocker.
7. Stop honestly
Finish only when:
every required gate passes
the integrated result still satisfies the original task
regressions relevant to the changed work have been checked
evidence supports the final claims
remaining unknowns are disclosed
Stop as blocked when a required gate depends on missing access, unavailable inputs, new authority, or an infeasible constraint. Name the exact blocker and the minimum next action. Do not weaken a gate merely to declare success.
Compose mode template
When returning an enhanced prompt, use this shape:
Use an iterative execution-and-verification workflow around the authoritative task below.
AUTHORITATIVE TASK
<preserve the user's original task here without changing its subject, deliverables, or constraints>
PROCESS
1. Extract the task contract and convert subjective quality language into observable acceptance gates.
2. Decompose independent workstreams and fan them out when delegation is useful and permitted.
3. Keep one integrator responsible for consistency.
4. Assign an independent verifier that sees the task, rubric, candidate, and references—but not the implementer's self-assessment.
5. Verify with task-appropriate evidence. Use anonymized side-by-side comparison when a real comparable benchmark exists.
6. Route failed gates back to the responsible workstream, revise, and re-check affected regressions.
7. Do not finish until every required gate passes or a concrete blocker is proven.
FINAL RESPONSE
Return the deliverable, a concise gate-by-gate evidence summary, and anything still unverified. Do not claim checks that were not run.Adapt the process to the task. Do not copy domain-specific tools, benchmarks, or quality claims from another prompt unless they apply here.
Completion checks
The original task remains authoritative.
Subjective ambition became observable gates.
Independent work was separated without creating racing edits.
Making and judging were assigned to different roles.
Benchmarks were real, comparable, and honestly labeled.
Failed gates drove revisions.
The final claims match the collected evidence.
Files
- ATTRIBUTION.mdAdded by AutoGineer861 B
- LICENSELicence file added by AutoGineer1.0 KiB
- REFERENCES.md55 B
- SKILL.md7.3 KiB
- agents/openai.yaml231 B
ATTRIBUTION.md
Attribution
This folder is an unmodified copy of the Agent Skill "iterate-until-verified", distributed by AutoGineer (https://autogineer.com/agent-skills).
Commit: 5ea1661a0e05d82b054553daaa6baf6d0233c33c
Created by: Meng To (https://github.com/MengTo)
Publisher: Meng To (https://github.com/MengTo)
Licence: MIT License (https://github.com/MengTo/Skills/blob/5ea1661a0e05d82b054553daaa6baf6d0233c33c/LICENSE)
Licence evidence: repository licence LICENSE
Retrieved: 2026-11-07
The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.
Files added by AutoGineer
ATTRIBUTION.md: this file, written by AutoGineer
LICENSE: copied from LICENSE
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Meng To
- Publisher
- Meng To (Community)
- Source
- MengTo/Skills · agent-skills/codex/iterate-until-verified · commit 5ea1661
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Meng To.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download iterate-until-verified.zip