/predict

name: autoresearch:predict
description: "5 expert personas debate proposed changes before implementation"
argument-hint: "[Scope: <glob>] [Goal: <text>] [--depth shallow|standard|deep] [--adversarial] [--chain <targets>]"
disable-model-invocation: true

EXECUTE IMMEDIATELY.

Parse Arguments

Extract from $ARGUMENTS:

• `Scope:` or `--scope` — file globs to analyze
• `Goal:` or `--goal` — focus area for analysis
• `Depth:` or `--depth` — shallow (3 personas, 1 round), standard (5, 2), deep (8, 3)
• `--personas N` — override persona count (3-8)
• `--rounds N` — override debate rounds (1-3)
• `--adversarial` — use hostile reviewer personas instead of default
• `--budget N` — max findings across all personas (default 40)
• `--fail-on <severity>` — CI gate: exit non-zero if findings at/above threshold
• `--incremental` — reuse existing knowledge files, update only changed files
• `--chain`, `--<subcommand>`

Remaining text not matching flags = goal description.

Setup (if Scope or Goal missing)

AskUserQuestion (single batch): Q1 (Scope): "Which files to analyze?" — suggested globs + entire codebase Q2 (Goal): "What should personas focus on?" — code quality, security, performance, architecture, all Q3 (Depth): "How deep?" — shallow (3 personas, 1 round), standard (5, 2 — recommended), deep (8, 3) Q4 (Chain): "After analysis, chain to?" — debug, security, fix, ship, scenario, no chain If all provided → skip.

Phase 1: Reconnaissance

Scan all in-scope files. Build structured knowledge:

• File inventory with purpose annotations
• Dependency graph (imports/exports)
• API surface (routes, handlers, types)
• Data flow (inputs → processing → outputs → storage)
• Existing test coverage map

Phase 2: Persona Generation

Load `references/predict-personas.md` for persona definitions.

**Default set (5):** Architect, Security Analyst, Performance Engineer, Reliability Engineer, Devil's Advocate. **Adversarial set (--adversarial):** Breaker, Cheater, Scaler, Newbie, Malicious Insider.

Each persona receives: task description + codebase knowledge + their specific evaluation criteria. Personas are isolated — no shared context between them.

Phase 3: Independent Analysis

Each persona analyzes the codebase independently:

• Read relevant code through their lens
• Produce findings with: title, severity, confidence (0-100%), file:line, recommendation
• Max findings per persona: budget / persona_count

Phase 4: Debate (per round)

For each debate round: 1. Present all personas' findings to each other 2. Each persona can: challenge findings, raise new issues, change confidence 3. Cross-examination: personas must respond to challenges with evidence 4. No persona can dismiss without counter-evidence

Phase 5: Consensus

Synthesizer aggregates all findings: 1. Deduplicate (same file:line + same issue = merge, keep highest severity) 2. Resolve conflicts (if personas disagree, note dissent) 3. **Anti-herd check:** if all personas agree on everything, synthesizer MUST find at least 1 counter-argument 4. Rank by: severity × average confidence × persona agreement count

Phase 6: Report

Create output directory: `autoresearch/predict-{YYMMDD}-{HHMM}/`

Write:

• `summary.md` — top findings, consensus view, risk assessment
• `debate.md` — full persona analysis + debate transcript
• Per-persona sections with individual findings

Print to console: top 10 findings ranked by severity × confidence.

Phase 7: CI Gate

If `--fail-on` set: check findings against threshold. Exit non-zero if exceeded.

Chain Handoff

Write handoff.json: version "2.1.0", source "predict", timestamp, status (COMPLETE|ERROR), findings = consensus findings with severity + confidence + file:line, config{scope, goal, depth}. Invoke next target in --chain order via the `autoresearch` skill's Subcommand routing.