vibe

Vibe Skill

Purpose: Is this code ready to ship?

Loop position

Per-slice quality gate within move 6 (close the bead by proving acceptance) of the [operating loop](../../docs/architecture/operating-loop.md). Consumes a slice's changes; produces PASS/WARN/FAIL on complexity, architecture, security, intent fit. Vibe answers "is this slice ready to be counted against the [slice-validation roll-up](../../docs/templates/slice-validation.md)?" — it is not a substitute for the slice's first failing test (the test proves behavior; vibe judges the code that gets there).

Three steps:

1. Complexity analysis — Find hotspots (radon, gocyclo)
2. Bug hunt audit — Systematic sweep for concrete bugs
3. Council validation — Multi-model judgment

Quick Start

/vibe                                    # validates recent changes
/vibe recent                             # same as above
/vibe src/auth/                          # validates specific path
/vibe --quick recent                     # fast inline check, no agent spawning
/vibe --structured recent                # 6-phase verification report (build→types→lint→tests→security→diff)
/vibe --deep recent                      # 3 judges instead of 2
/vibe --sweep recent                     # deep audit: per-file explorers + council
/vibe --mixed recent                     # cross-vendor (Claude + Codex)
/vibe --preset=security-audit src/auth/  # security-focused review
/vibe --explorers=2 recent               # judges with explorer sub-agents
/vibe --debate recent                    # two-round adversarial review
/vibe --tier=quality recent              # use quality tier for council calls

Execution Steps

Step 0: Load Prior Review Context

Before reviewing, pull relevant learnings from prior code reviews and known patterns:

if command -v ao &>/dev/null; then
    ao lookup --query "<target-scope> code review patterns" --limit 3 2>/dev/null || true
fi

Apply retrieved knowledge (mandatory when results returned):

If learnings or patterns are returned, do NOT just load them as passive context. For each returned item:

1. Check: does this learning apply to the code under review? (answer yes/no)
2. If yes: include it as a known_risk in your review — state the pattern, what to look for, and whether the code exhibits it
3. Cite the learning by filename in your review output when it influences a finding

After applying, record the citation:

ao metrics cite "<learning-path>" --type applied 2>/dev/null || true

Skip silently if ao is unavailable or returns no results.

Project reviewer config: If .agents/reviewer-config.md exists, its full config (reviewers, plan_reviewers, skip_reviewers) is passed to council for judge selection. See skills/council/SKILL.md Step 1b.

Crank Checkpoint Detection

Before scanning for changed files via git diff, check if a crank checkpoint exists:

if [ -f .agents/vibe-context/latest-crank-wave.json ]; then
    echo "Crank checkpoint found — using files_changed from checkpoint"
    FILES_CHANGED=$(jq -r '.files_changed[]' .agents/vibe-context/latest-crank-wave.json 2>/dev/null)
    WAVE_COUNT=$(jq -r '.wave' .agents/vibe-context/latest-crank-wave.json 2>/dev/null)
    echo "Wave $WAVE_COUNT checkpoint: $(echo "$FILES_CHANGED" | wc -l | tr -d ' ') files changed"
fi

When a crank checkpoint is available, use its files_changed list instead of re-detecting via git diff. This ensures vibe validates exactly the files that crank modified.

Step 1: Determine Target

If target provided: Use it directly.

If no target or "recent": Auto-detect from git:

# Check recent commits
git diff --name-only HEAD~3 2>/dev/null | head -20

If nothing found, ask user.

Pre-flight: If no files found: Return immediately with: "PASS (no changes to review) — no modified files detected." Do NOT spawn agents for empty file lists.

Step 1.5a: Structured Verification Path (--structured mode)

If --structured flag is set, run a 6-phase mechanical verification pipeline instead of the council flow. This produces a machine-readable verification report suitable for PR gates and CI integration.

Phases: Build → Types → Lint → Tests → Security → Diff Review.

Read references/verification-report.md for the full report template and per-phase commands. Each phase is fail-fast — if Build fails, skip remaining phases and report NOT READY.

After all phases complete, write the structured report to .agents/council/YYYY-MM-DD-verification-<target>.md and output the summary table to the user.

When to use: Pre-PR gate, CI integration, when you need a mechanical pass/fail rather than judgment-based review.

Step 1.5: Fast Path (--quick mode)

If --quick flag is set, skip Steps 2a through 2e as heavy pre-processing, plus 2.5 and 2f, and jump to Step 4 with inline council after Steps 2.3, 2.4, 2g, and Step 3. Domain checklists, compiled-prevention loading, test-pyramid inventory, and inline product context are cheap and high-value, so they still run in quick mode. Complexity analysis (Step 2) still runs — it's cheap and informative.

Why: Steps 2.5 and 2a–2f add 30–90 seconds of pre-processing that mainly feed multi-judge council packets. In --quick mode (single inline agent), those inputs are not worth the cost, but test-pyramid and product-context checks still shape the inline review meaningfully.

Step 2: Run Complexity Analysis

Read [references/complexity-analysis.md](references/complexity-analysis.md) when you need the language-detection preflight, per-language analyzer commands (radon/gocyclo), and the score interpretation table. Filter by language present in the diff before running any analyzer.

Step 2.3: Load Domain-Specific Checklists

Detect code patterns in the target files and load matching domain-specific checklists from standards/references/:

| Trigger | Checklist | Detection | |---------|-----------|-----------| | SQL/ORM code | sql-safety-checklist.md | Files contain SQL queries, ORM imports (database/sql, sqlalchemy, prisma, activerecord, gorm, knex), or migration files in changeset | | LLM/AI code | llm-trust-boundary-checklist.md | Files import anthropic, openai, google.generativeai, or match llm, prompt, completion patterns | | Concurrent code | race-condition-checklist.md | Files use goroutines, threading, asyncio, multiprocessing, sync.Mutex, concurrent.futures, or shared file I/O patterns | | Codex skills | codex-skill.md | Files under skills-codex/, or files matching *codex*SKILL.md, convert.sh, skills-codex-overrides/, or converter scripts |

For each matched checklist, load it via the Read tool and include relevant items in the council packet as context.domain_checklists. Multiple checklists can be loaded simultaneously.

Skip silently if no patterns match. This step runs in both --quick and full modes (domain checklists are cheap to load and high-value).

Steps 2.4-2f, 2h, 3-3.6 (Deep Checks & Pre-Council Prep): Read references/deep-checks.md for compiled prevention, prior findings, pre-council deep analysis checks, product context, spec loading, suppressions, pre-mortem correlation, and model cost tiers. Loaded automatically unless --quick mode is set. In --quick mode, skip directly to Step 2g.

Compiled prevention inputs: Load .agents/pre-mortem-checks/ and .agents/planning-rules/ when available. These compiled artifacts contain known_risks from prior findings that inform the review — carry matched finding IDs into council context so judges can assess whether the flywheel prevented rediscovery.

Step 2a: Prior Findings Check

Skip if --quick. Load prior findings from .agents/findings/registry.jsonl.

Step 2b: Constraint Tests

Skip if --quick. Run compiled constraint tests from .agents/constraints/.

Step 2c: Metadata Ch