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Grok Bot+18 agentsAI-Loop — Independent Verification for Claude Code
A gated multi-agent workflow for Claude Code with adversarial review, independent verification, and evidence before human acceptance.
$8
AI-Loop — Independent Verification for Claude Code
Example session with this skill installed
Use AI-Loop to let a tournament organizer replace a team with a team from another club. Test the feature in the web app using Chrome.
- Read your context and instructions
- Compiled the ai-loop
External-team replacement — implementation and verification report
Translated and anonymized summary of an actual Claude Code session. This is an edited case study, not a verbatim transcript or a new execution.
The feature was implemented across the backend, web, and mobile repositories. An organizer can select a replacement from other clubs; the incoming team is invited, and its matches remain blocked until it accepts.
Review caught issues before handoff
The final cross-repository review found that failed replacements did not display an error in the web app and that an error message was missing. Both issues were corrected, and the follow-up review returned clean.
Verification evidence
| Check | Recorded result |
|---|---|
| Eight implementation tasks | Passed their reviews |
| Backend acceptance test run | 67 PASS, 0 FAIL, 0 UNVERIFIED |
| Baseline comparison | The pre-feature binary failed the expected new replacement and consent checks; perimeter checks remained unchanged |
| Backend regression suite | Matched the baseline package by package |
| Authorization checks | Passed with zero authorization orphans |
| Affected web test directories | Passed after the final corrections |
End-to-end flow exercised in Chrome
- Open the replacement dialog and select a team from another club.
- Confirm that the UI warns that the incoming team requires acceptance.
- Complete the replacement and confirm that the incoming team appears as invited in the registration inbox.
- Attempt to start its match before acceptance: the app blocks the action and names the team.
- After acceptance, start the match successfully.
The local browser trace contains navigation, clicks, and screenshots for the flow. Acceptance in the E2E scenario used a synthetic session; this does not establish a complete two-user acceptance UI walkthrough.
Declared limits and handoff
- The complete web suite was not rerun after the last corrections; affected directories were rerun.
- The new blocking message was covered by a mobile widget test but was not inspected on a physical phone.
- The match-start E2E used a prepared tournament fixture and did not cover the creation wizard.
- Branches were ready for owner review. Integration with newer changes, deployment checks, and final acceptance remained separate steps.
The handoff delivered the implementation, verification results, browser-tested behavior, and remaining limitations for the human owner to review.
Connects securely to your tools. The creator never sees your data.
About this skill
Your AI agent says “done.” What proves it?
When the same agent plans, implements, and checks a change, its blind spots can survive the review. AI-Loop gives Claude Code a structured multi-agent protocol: separate roles, explicit quality gates, and evidence for every acceptance criterion.
The implementer does not verify its own work. Passing tests does not automatically mean acceptance. The human owner decides when the work is done.
What it does
- Separates implementation from verification. Distinct agents handle adversarial review, implementation, and independent verification.
- Challenges the specification. An adversarial gate tests assumptions and technical claims before implementation proceeds.
- Requires meaningful test evidence. The implementation protocol calls for failing assertions, baseline checks, and mutation checks where applicable.
- Verifies independently. A separate verifier uses its own probes on a different model. The coder’s report is an input, never proof.
- Makes uncertainty explicit. PASS requires evidence for every acceptance criterion. FAIL, BLOCKED, and UNKNOWN have distinct routes; UNKNOWN never counts as PASS.
- Structures handoffs. Written handoffs and fresh sessions at phase boundaries help manage context and preserve progress.
How it works
- Versioned specification: Define scope, acceptance criteria, and measured technical claims.
- Adversarial gate: Challenge the plan using independent checks and isolated worktree probes.
- Implementation: Execute the approved specification and report changes, checks, and unresolved limitations.
- Independent verification: Re-measure acceptance criteria and review the complete diff.
- E2E checks: Verify the resulting behavior through the relevant end-to-end checks.
- Human acceptance: Present the evidence and remaining risks for the owner’s decision.
Best suited to
- Root-cause fixes that need more than a plausible patch.
- Features involving schemas, permissions, data, or explicit behavioral contracts.
- Substantial refactoring where existing behavior must be preserved.
- Changes that benefit from adversarial review and clear approval boundaries.
What you get
The complete SKILL.md, a quick-start installation guide, and the original MIT license. Install it as a personal or project skill in Claude Code.
Requirements and limitations
Requires Claude Code with skills and subagents, access to a different model for independent verification, and a Git repository with the relevant build, test, and E2E tools. Human participation is required for approvals and session handoffs. Model usage costs are separate.
AI-Loop provides agent instructions, not deterministic enforcement. Compliance and bug-free results are not guaranteed. The workflow adds time and token overhead and is not suitable for trivial renames or formatting changes.
No model access, executable engine, automated test fixtures, or personalized consulting is included. Compatibility with other agents has not been validated.
Open source, packaged for convenience
AI-Loop is available free under MIT on GitHub. Purchase this ready-to-install package through Agensi to support the author.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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