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ai-review-evidence-reconciler

Reconcile AI-generated review evidence against expected verification criteria in read-only mode.

Updated Sep 16, 2026
Search Tags:#code review#verification#audit#compliance#read-only#test logs#evidence reconciliation
One-click install
npx skills add https://github.com/Military-Veteran-Team-LPT-Realty/mvt-manus-public-skills --skill ai-review-evidence-reconciler-military-veteran-team-lpt-realty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-review-evidence-reconciler
Source: https://github.com/Military-Veteran-Team-LPT-Realty/mvt-manus-public-skills/tree/main/skills/ai-review-evidence-reconciler
Command: npx skills add https://github.com/Military-Veteran-Team-LPT-Realty/mvt-manus-public-skills --skill ai-review-evidence-reconciler-military-veteran-team-lpt-realty

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI-generated code reviews and test summaries can contain claims that contradict or lack support from actual execution logs. This Skill provides a systematic, fail-closed workflow to cross-check those claims against raw artifacts before any approval decision is made. ## Core Features & Use Cases - Evidence Reconciliation: Compare AI review claims line-by-line against test logs, build outputs, and baseline criteria, classifying each finding as Match, Discrepancy, or Unverified. - Fail-Closed Verification: Missing logs, ambiguous evidence, or unverified assertions are flagged as verification failures by default. - Standardized Reporting: Generate a reconciliation report from the included template with confidence ratings, risk levels, and an explicit human approval gate. - Use Case: Before merging a release candidate, run this Skill to audit the AI-generated review summary against the actual CI test logs and produce a signed-off reconciliation report for reviewers. ## Quick Start Reconcile the AI review summary for PR #123 against the test execution logs and generate a reconciliation report using the evidence template.

Frequently Asked Questions about ai-review-evidence-reconciler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I verify AI-generated code review claims against test logs?▼

Gather the AI review summary, test execution logs, and build artifacts into a review scope, then compare each claim line-by-line against the raw logs. Classify findings as Match, Discrepancy, or Unverified and aggregate them into a confidence rating.

What is fail-closed verification in evidence reconciliation?▼

Fail-closed verification means any discrepancy, missing log, unverified assertion, or ambiguous evidence is flagged as a verification failure by default. Claims are only accepted when directly supported by underlying logs and criteria.

Can this Skill modify code or approve deployments automatically?▼

No. All analysis operates strictly in read-only mode and never modifies source code, test artifacts, or production systems. Reports may recommend actions, but execution requires explicit human authorization and separate sign-off.

What artifacts are needed to run an evidence reconciliation audit?▼

You need the AI review summaries, test execution logs, build outputs, compliance checklists, and the canonical expected criteria or acceptance thresholds. File integrity and read-only access should be confirmed before comparison begins.

Does the reconciliation report include secrets or credentials?▼

No. The workflow enforces a zero-secret policy, so API keys, passwords, authentication tokens, and personal identifiers are never included in review evidence summaries or logs.

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