deep-verify

name	deep-verify
description	Multi-angle release quality verification using parallel expert review teams
scope	core
version	1.1.0
user-invocable	true
effort	high

/deep-verify — Multi-Angle Release Quality Verification

Purpose

Performs deep cross-iterative verification of code changes before release, using multiple independent review perspectives to catch issues that single-pass review misses.

Usage

/deep-verify [branch|PR]

If no argument, verifies current branch against its base (usually develop).

Workflow

Round 1: Baseline Assessment

• Gather the full diff (git diff develop...HEAD)
• Run test suite, lint, and type check
• Collect results as baseline

Round 2: Parallel Expert Review (6 agents)

Spawn 6 parallel review agents, each with a different focus:

1. Correctness Reviewer — Logic errors, edge cases, off-by-one, null handling
2. Security Reviewer — Injection, auth bypass, data exposure, OWASP top 10
3. Performance Reviewer — O(n^2) loops, unbounded queries, memory leaks, missing indexes
4. Integration Reviewer — API contract breaks, migration safety, cross-module side effects
5. Philosophy Reviewer — Project concept/metaphor alignment, separation of concerns (R006), orchestrator rules (R010), advisory-first enforcement (R021), compilation metaphor integrity
6. Regression & Performance Reviewer — Feature regression risk, API contract preservation, query performance impact, index effectiveness, algorithm complexity at realistic scale

Each agent receives the full diff and returns findings as structured JSON:

{
  "severity": "HIGH|MEDIUM|LOW",
  "file": "path/to/file",
  "line": 42,
  "finding": "description",
  "suggestion": "fix suggestion"
}

Round 3: Cross-Verification

• Merge all findings from Round 2
• Deduplicate (same file+line+similar finding = 1 entry)
• For each HIGH finding: spawn a verification agent to confirm or dismiss as FALSE POSITIVE
• Evidence-based: each confirmation must include proof (e.g., toQuery() output, test result)

Round 4: FALSE POSITIVE Filter

• Remove confirmed false positives with evidence
• Remaining findings are CONFIRMED issues

Round 5: Fix Application

• For each CONFIRMED HIGH/MEDIUM finding: spawn fix agent
• Run tests after fixes
• If tests fail: revert fix, report as "needs manual review"

Round 6: Final Verification

• Re-run full test suite
• Re-run lint and type check
• Generate summary report

Round 7: Philosophy & Regression Gate

• Verify all changes align with project's compilation metaphor (Skills=source, Agents=artifacts, Rules=spec)
• Check separation of concerns: no agents containing skill logic, no skills with agent definitions
• Verify orchestrator rules: no new file writes from orchestrator context
• Check advisory-first: no new hard-blocking hooks introduced
• Confirm no feature regressions: existing APIs preserved, test coverage maintained
• Performance sanity: no O(n^2) on large datasets, no missing indexes for new queries
• If any CONCERN or VIOLATION found: report for manual review before release

Output Format

╔══════════════════════════════════════════════════════╗
║  Deep Verification Report                            ║
╠══════════════════════════════════════════════════════╣
║  Branch: {branch}                                    ║
║  Commits: {count}                                    ║
║  Files changed: {count}                              ║
╠══════════════════════════════════════════════════════╣
║  Findings:                                           ║
║    HIGH:   {n} ({confirmed} confirmed, {fp} FP)      ║
║    MEDIUM: {n} ({confirmed} confirmed, {fp} FP)      ║
║    LOW:    {n}                                       ║
╠══════════════════════════════════════════════════════╣
║  Fixes Applied: {n}                                  ║
║  Tests: {pass}/{total} passing                       ║
║  Verdict: READY / NEEDS REVIEW / BLOCKED             ║
║  Philosophy: ALIGNED / {n} CONCERNS                  ║
║  Regression: CLEAN / {n} RISKS                       ║
╚══════════════════════════════════════════════════════╝

Notes

• Round 2 agents use model: sonnet for cost efficiency
• Round 3 verification agents use model: opus for reasoning depth
• FALSE POSITIVE filtering is critical — previous releases showed 80%+ FP rate on automated review
• This skill replaces ad-hoc cross-verification with a repeatable process
• Round 7 philosophy check references CLAUDE.md architecture section and R006/R010/R021 rules
• Regression check compares function signatures, export lists, and test counts against develop baseline