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Local AI Agent Loop Audit

AI Agent Loop Readiness Doctor

Audit an existing loop or build one from a checklist. Confirm every detected rule, see task-specific blockers, and export a corrected workflow without uploading your prompt.

Use this before running a Claude Code, Codex, Cursor, GitHub Actions, Generic Agent, or Ralph-style loop. Learn why weak validators invite metric gaming or turn a ready task into a copyable workflow with the loop goal generator.

Audit an existing loop or build from a checklist

Select the task and target tool first. The same confirmed rules receive different weights when the risk profile changes.

Your text is processed locally in your browser and is never sent to analytics.
0 / 20,000 characters
Deterministic rules only. No AI API, repository access, or server upload.

Ready for a local audit.

Privacy boundaryThe audit runs entirely in this page. Analytics receive only fixed task, tool, score range, issue category, and action names after consent—never your pasted workflow.

How the audit works

1. Choose context

Select a task type and target Agent tool so the audit can apply the correct risk weights.

2. Analyze locally

Paste a workflow or answer the checklist. Deterministic rules inspect only the text in your browser.

3. Confirm candidates

Review the exact matched text and correct every Yes, Partial, or No result before scoring.

4. Export a correction

Compare before and after, then copy or download a target-specific workflow and audit report.

Task-weighted readiness criteria

The same eleven dimensions always total 100 points, but their weights change by task. Every result remains visible and editable.

Hypothesis

A testable hypothesis turns the first run into evidence instead of activity.

Smallest useful run

A small first run exposes bad assumptions before they become expensive.

Goal clarity

Agents need an outcome they can finish, not an open-ended instruction to keep working.

Validation

Validation separates completed work from plausible-looking output.

Independent checker

The worker should not be the only judge of its own output.

Boundaries

Explicit boundaries prevent the loop from satisfying a metric through unsafe shortcuts.

Stop condition

A loop without a hard stop can repeat the same unproductive action indefinitely.

Budget and iteration cap

A budget makes the worst case visible before the loop starts.

Rollback or fallback

A fallback preserves evidence and gives a human a safe decision point.

Sandbox isolation

Isolation contains mistakes and makes cleanup or comparison easier.

Human approval gate

Human approval belongs immediately before high-impact actions, not after they happen.

Privacy and interpretation limits

Pasted workflows are processed locally in your browser. They are not sent to analytics, logs, URLs, error monitoring, a repository, or an AI provider. Detection is based on explainable patterns and can miss implied rules or misunderstand ambiguous language. This tool is not a safety certification, security review, or production guarantee.

Loop Readiness Doctor FAQ

No. The MVP uses deterministic browser-side rules and shows every matched text candidate for you to confirm. It does not call an external AI API.