autoresearch

分类: 调研与分析 | 上传者: uditgoenka | 下载: 0 | 版本: v1.0（最新）
自主目标导向迭代。将 Karpathy 的自动研究原则应用于任何任务。自主循环——修改、验证、保留/丢弃、重复。通过 Iterations:N 内联配置支持有界迭代。
更新日志: Source: GitHub https://github.com/uditgoenka/autoresearch
目录结构

当前层级: tree/master/.claude/skills/autoresearch/
📁 references/
- 📄 autonomous-loop-protocol.md 28.0 KB
- 📄 core-principles.md 7.8 KB
- 📄 debug-workflow.md 21.0 KB
- 📄 fix-workflow.md 25.8 KB
- 📄 learn-workflow.md 22.2 KB
- 📄 plan-workflow.md 9.5 KB
- 📄 predict-workflow.md 30.0 KB
- 📄 reason-workflow.md 24.5 KB
- 📄 results-logging.md 6.0 KB
- 📄 scenario-workflow.md 18.1 KB
- 📄 security-workflow.md 36.0 KB
- 📄 ship-workflow.md 14.6 KB
📄 SKILL.md 34.5 KB
SKILL.md

---
name: autoresearch
description: Autonomous Goal-directed Iteration. Apply Karpathy's autoresearch principles to ANY task. Loops autonomously — modify, verify, keep/discard, repeat. Supports bounded iteration via Iterations: N inline config.
version: 1.9.0
---

# Claude Autoresearch — Autonomous Goal-directed Iteration

Inspired by [Karpathy's autoresearch](https://github.com/karpathy/autoresearch). Applies constraint-driven autonomous iteration to ANY work — not just ML research.

**Core idea:** You are an autonomous agent. Modify → Verify → Keep/Discard → Repeat.

## MANDATORY: Interactive Setup Gate

**CRITICAL — READ THIS FIRST BEFORE ANY ACTION:**

For ALL commands (`/autoresearch`, `/autoresearch:plan`, `/autoresearch:debug`, `/autoresearch:fix`, `/autoresearch:security`, `/autoresearch:ship`, `/autoresearch:scenario`, `/autoresearch:predict`, `/autoresearch:learn`, `/autoresearch:reason`):

1. **Check if the user provided ALL required context inline** (Goal, Scope, Metric, flags, etc.)
2. **If ANY required context is missing → you MUST use `AskUserQuestion` to collect it BEFORE proceeding to any execution phase.** DO NOT skip this step. DO NOT proceed without user input.
3. Each subcommand's reference file has an "Interactive Setup" section — follow it exactly when context is missing.

| Command | Required Context | If Missing → Ask |
|---------|-----------------|-----------------|
| `/autoresearch` | Goal, Scope, Metric, Direction, Verify | Batch 1 (4 questions) + Batch 2 (3 questions) from Setup Phase below |
| `/autoresearch:plan` | Goal | Ask via `AskUserQuestion` per `references/plan-workflow.md` |
| `/autoresearch:debug` | Issue/Symptom, Scope | 4 batched questions per `references/debug-workflow.md` |
| `/autoresearch:fix` | Target, Scope | 4 batched questions per `references/fix-workflow.md` |
| `/autoresearch:security` | Scope, Depth | 3 batched questions per `references/security-workflow.md` |
| `/autoresearch:ship` | What/Type, Mode | 3 batched questions per `references/ship-workflow.md` |
| `/autoresearch:scenario` | Scenario, Domain | 4-8 adaptive questions per `references/scenario-workflow.md` |
| `/autoresearch:predict` | Scope, Goal | 3-4 batched questions per `references/predict-workflow.md` |
| `/autoresearch:learn` | Mode, Scope | 4 batched questions per `references/learn-workflow.md` |
| `/autoresearch:reason` | Task, Domain | 3-5 adaptive questions per `references/reason-workflow.md` |

**YOU MUST NOT start any loop, phase, or execution without completing interactive setup when context is missing. This is a BLOCKING prerequisite.**

## Subcommands

| Subcommand | Purpose |
|------------|---------|
| `/autoresearch` | Run the autonomous loop (default) |
| `/autoresearch:plan` | Interactive wizard to build Scope, Metric, Direction & Verify from a Goal |
| `/autoresearch:security` | Autonomous security audit: STRIDE threat model + OWASP Top 10 + red-team (4 adversarial personas) |
| `/autoresearch:ship` | Universal shipping workflow: ship code, content, marketing, sales, research, or anything |
| `/autoresearch:debug` | Autonomous bug-hunting loop: scientific method + iterative investigation until codebase is clean |
| `/autoresearch:fix` | Autonomous fix loop: iteratively repair errors (tests, types, lint, build) until zero remain |
| `/autoresearch:scenario` | Scenario-driven use case generator: explore situations, edge cases, and derivative scenarios |
| `/autoresearch:predict` | Multi-persona swarm prediction: pre-analyze code from multiple expert perspectives before acting |
| `/autoresearch:learn` | Autonomous codebase documentation engine: scout, learn, generate/update docs with validation-fix loop |
| `/autoresearch:reason` | Adversarial refinement for subjective domains: isolated multi-agent generate→critique→synthesize→blind judge loop until convergence |

### /autoresearch:security — Autonomous Security Audit

Runs a comprehensive security audit using the autoresearch loop pattern. Generates a full STRIDE threat model, maps attack surfaces, then iteratively tests each vulnerability vector — logging findings with severity, OWASP category, and code evidence.

Load: `references/security-workflow.md` for full protocol.

**What it does:**

1. **Codebase Reconnaissance** — scans tech stack, dependencies, configs, API routes
2. **Asset Identification** — catalogs data stores, auth systems, external services, user inputs
3. **Trust Boundary Mapping** — browser↔server, public↔authenticated, user↔admin, CI/CD↔prod
4. **STRIDE Threat Model** — Spoofing, Tampering, Repudiation, Info Disclosure, DoS, Elevation of Privilege
5. **Attack Surface Map** — entry points, data flows, abuse paths
6. **Autonomous Loop** — iteratively tests each vector, validates with code evidence, logs findings
7. **Final Report** — severity-ranked findings with mitigations, coverage matrix, iteration log

**Key behaviors:**
- Follows red-team adversarial mindset (Security Adversary, Supply Chain, Insider Threat, Infra Attacker)
- Every finding requires **code evidence** (file:line + attack scenario) — no theoretical fluff
- Tracks OWASP Top 10 + STRIDE coverage, prints coverage summary every 5 iterations
- Composite metric: `(owasp_tested/10)*50 + (stride_tested/6)*30 + min(findings, 20)` — higher is better
- Creates `security/{YYMMDD}-{HHMM}-{audit-slug}/` folder with structured reports:
  `overview.md`, `threat-model.md`, `attack-surface-map.md`, `findings.md`, `owasp-coverage.md`, `dependency-audit.md`, `recommendations.md`, `security-audit-results.tsv`

**Flags:**

| Flag | Purpose |
|------|---------|
| `--diff` | Delta mode — only audit files changed since last audit |
| `--fix` | After audit, auto-fix confirmed Critical/High findings using autoresearch loop |
| `--fail-on {severity}` | Exit non-zero if findings meet threshold (for CI/CD gating) |

**Usage:**
```
# Unlimited — keep finding vulnerabilities until interrupted
/autoresearch:security

# Bounded — exactly 10 security sweep iterations
/autoresearch:security
Iterations: 10

# With focused scope
/autoresearch:security
Scope: src/api/**/*.ts, src/middleware/**/*.ts
Focus: authentication and authorization flows

# Delta mode — only audit changed files since last audit
/autoresearch:security --diff

# Auto-fix confirmed Critical/High findings after audit
/autoresearch:security --fix
Iterations: 15

# CI/CD gate — fail pipeline if any Critical findings
/autoresearch:security --fail-on critical
Iterations: 10

# Combined — delta audit + fix + gate
/autoresearch:security --diff --fix --fail-on critical
Iterations: 15
```

**Inspired by:**
- [Strix](https://github.com/usestrix/strix) — AI-powered security testing with proof-of-concept validation
- `/plan red-team` — adversarial review with hostile reviewer personas
- OWASP Top 10 (2021) — industry-standard vulnerability taxonomy
- STRIDE — Microsoft's threat modeling framework

### /autoresearch:ship — Universal Shipping Workflow

Ship anything — code, content, marketing, sales, research, or design — through a structured 8-phase workflow that applies autoresearch loop principles to the last mile.

Load: `references/ship-workflow.md` for full protocol.

**What it does:**

1. **Identify** — auto-detect what you're shipping (code PR, deployment, blog post, email campaign, sales deck, research paper, design assets)
2. **Inventory** — assess current state and readiness gaps
3. **Checklist** — generate domain-specific pre-ship gates (all mechanically verifiable)
4. **Prepare** — autoresearch loop to fix failing checklist items until 100% pass
5. **Dry-run** — simulate the ship action without side effects
6. **Ship** — execute the actual delivery (merge, deploy, publish, send)
7. **Verify** — post-ship health check confirms it landed
8. **Log** — record shipment to `ship-log.tsv` for traceability

**Supported shipment types:**

| Type | Example Ship Actions |
|------|---------------------|
| `code-pr` | `gh pr create` with full description |
| `code-release` | Git tag + GitHub release |
| `deployment` | CI/CD trigger, `kubectl apply`, push to deploy branch |
| `content` | Publish via CMS, commit to content branch |
| `marketing-email` | Send via ESP (SendGrid, Mailchimp) |
| `marketing-campaign` | Activate ads, launch landing page |
| `sales` | Send proposal, share deck |
| `research` | Upload to repository, submit paper |
| `design` | Export assets, share with stakeholders |

**Flags:**

| Flag | Purpose |
|------|---------|
| `--dry-run` | Validate everything but don't actually ship (stop at Phase 5) |
| `--auto` | Auto-approve dry-run gate if no errors |
| `--force` | Skip non-critical checklist items (blockers still enforced) |
| `--rollback` | Undo the last ship action (if reversible) |
| `--monitor N` | Post-ship monitoring for N minutes |
| `--type <type>` | Override auto-detection with explicit shipment type |
| `--checklist-only` | Only generate and evaluate checklist (stop at Phase 3) |

**Usage:**
```
# Auto-detect and ship (interactive)
/autoresearch:ship

# Ship code PR with auto-approve
/autoresearch:ship --auto

# Dry-run a deployment before going live
/autoresearch:ship --type deployment --dry-run

# Ship with post-deployment monitoring
/autoresearch:ship --monitor 10

# Prepare iteratively then ship
/autoresearch:ship
Iterations: 5

# Just check if something is ready to ship
/autoresearch:ship --checklist-only

# Ship a blog post
/autoresearch:ship
Target: content/blog/my-new-post.md
Type: content

# Ship a sales deck
/autoresearch:ship --type sales
Target: decks/q1-proposal.pdf

# Rollback a bad deployment
/autoresearch:ship --rollback
```

**Composite metric (for bounded loops):**
```
ship_score = (checklist_passing / checklist_total) * 80
           + (dry_run_passed ? 15 : 0)
           + (no_blockers ? 5 : 0)
```
Score of 100 = fully ready. Below 80 = not shippable.

**Output directory:** Creates `ship/{YYMMDD}-{HHMM}-{ship-slug}/` with `checklist.md`, `ship-log.tsv`, `summary.md`.

### /autoresearch:scenario — Scenario-Driven Use Case Generator

Autonomous scenario exploration engine that generates, expands, and stress-tests use cases from a seed scenario. Discovers edge cases, failure modes, and derivative scenarios that manual analysis misses.

Load: `references/scenario-workflow.md` for full protocol.

**What it does:**

1. **Seed Analysis** — parse scenario, identify actors, goals, preconditions, components
2. **Decomposition** — break into 12 exploration dimensions (happy path, error, edge case, abuse, scale, concurrent, temporal, data variation, permission, integration, recovery, state transition)
3. **Situation Generation** — create one concrete situation per iteration from unexplored dimensions
4. **Classification** — deduplicate (new/variant/duplicate/out-of-scope/low-value)
5. **Expansion** — derive edge cases, what-ifs, failure modes from each kept situation
6. **Logging** — record to scenario-results.tsv with dimension, severity, classification
7. **Repeat** — pick next unexplored dimension/combination, iterate

**Key behaviors:**
- Adaptive interactive setup: 4-8 questions based on how much context the user provides
- 12 exploration dimensions ensure comprehensive coverage
- Domain-specific templates (software, product, business, security, marketing)
- Every situation requires concrete trigger, flow, and expected outcome — no vague "something goes wrong"
- Composite metric: `scenarios_generated*10 + edge_cases_found*15 + (dimensions_covered/12)*30 + unique_actors*5`
- Creates `scenario/{YYMMDD}-{HHMM}-{slug}/` with: `scenarios.md`, `use-cases.md`, `edge-cases.md`, `scenario-results.tsv`, `summary.md`

**Flags:**

| Flag | Purpose |
|------|---------|
| `--domain <type>` | Set domain (software, product, business, security, marketing) |
| `--depth <level>` | Exploration depth: shallow (10), standard (25), deep (50+) |
| `--scope <glob>` | Limit to specific files/features |
| `--format <type>` | Output: use-cases, user-stories, test-scenarios, threat-scenarios, mixed |
| `--focus <area>` | Prioritize dimension: edge-cases, failures, security, scale |

**Usage:**
```
# Unlimited — keep exploring until interrupted
/autoresearch:scenario

# Bounded with context
/autoresearch:scenario
Scenario: User attempts checkout with multiple payment methods
Domain: software
Depth: standard
Iterations: 25

# Quick edge case scan
/autoresearch:scenario --depth shallow --focus edge-cases
Scenario: File upload feature for profile pictures

# Security-focused
/autoresearch:scenario --domain security
Scenario: OAuth2 login flow with third-party providers
Iterations: 30

# Generate test scenarios
/autoresearch:scenario --format test-scenarios --domain software
Scenario: REST API pagination with filtering and sorting
```

### /autoresearch:predict — Multi-Persona Swarm Prediction

Multi-perspective code analysis using swarm intelligence principles. Simulates 3-5 expert personas (Architect, Security Analyst, Performance Engineer, Reliability Engineer, Devil's Advocate) that independently analyze code, debate findings, and reach consensus — all within Claude's native context. Zero external dependencies.

Load: `references/predict-workflow.md` for full protocol.

**What it does:**

1. **Codebase Reconnaissance** — scan files, extract entities, map dependencies into knowledge .md files
2. **Persona Generation** — create 3-5 expert personas from codebase context
3. **Independent Analysis** — each persona analyzes code from their unique perspective
4. **Structured Debate** — 1-2 rounds of cross-examination with mandatory Devil's Advocate dissent
5. **Consensus** — synthesizer aggregates findings with confidence scores + anti-herd check
6. **Knowledge Output** — write predict/ folder with codebase-analysis.md, dependency-map.md, component-clusters.md
7. **Report** — generate findings.md, hypothesis-queue.md, overview.md
8. **Handoff** — write handoff.json for optional --chain to debug/security/fix/ship/scenario

**Key behaviors:**
- File-based knowledge representation: .md files ARE the knowledge graph, zero external deps
- Git-hash stamping: every output embeds commit SHA for staleness detection
- Incremental updates: only re-analyzes files changed since last run
- Anti-herd mechanism: Devil's Advocate mandatory, groupthink detection via flip rate + entropy
- Empirical evidence always trumps swarm prediction when chained with autoresearch loop
- Composite metric: `findings_confirmed*15 + findings_probable*8 + minority_preserved*3 + (personas/total)*20 + (rounds/planned)*10 + anti_herd_passed*5`
- Creates `predict/{YYMMDD}-{HHMM}-{slug}/` folder with: `overview.md`, `codebase-analysis.md`, `dependency-map.md`, `component-clusters.md`, `persona-debates.md`, `hypothesis-queue.md`, `findings.md`, `predict-results.tsv`, `handoff.json`

**Flags:**

| Flag | Purpose |
|------|---------|
| `--chain <targets>` | Chain to tools. Single: `--chain debug`. Multi: `--chain scenario,debug,fix` (sequential) |
| `--personas N` | Number of personas (default: 5, range: 3-8) |
| `--rounds N` | Debate rounds (default: 2, range: 1-3) |
| `--depth <level>` | Depth preset: shallow (3 personas, 1 round), standard (5, 2), deep (8, 3) |
| `--adversarial` | Use adversarial persona set (Red Team, Blue Team, Insider, Supply Chain, Judge) |
| `--budget <N>` | Max total findings across all personas (default: 40) |
| `--fail-on <severity>` | Exit non-zero if findings at or above severity (for CI/CD) |
| `--scope <glob>` | Limit analysis to specific files |

**Usage:**
```
# Standard analysis
/autoresearch:predict
Scope: src/**/*.ts
Goal: Find reliability issues

# Quick security scan
/autoresearch:predict --depth shallow --chain security
Scope: src/api/**

# Deep analysis with adversarial debate
/autoresearch:predict --depth deep --adversarial
Goal: Pre-deployment quality audit

# CI/CD gate
/autoresearch:predict --fail-on critical --budget 20
Scope: src/**
Iterations: 1

# Chain to debug for hypothesis-driven investigation
/autoresearch:predict --chain debug
Scope: src/auth/**
Goal: Investigate intermittent 500 errors

# Multi-chain: predict → scenario → debug → fix (sequential pipeline)
/autoresearch:predict --chain scenario,debug,fix
Scope: src/**
Goal: Full quality pipeline for new feature
```

### /autoresearch:learn — Autonomous Codebase Documentation Engine

Scouts codebase structure, learns patterns and architecture, generates/updates comprehensive documentation — then validates and iteratively improves until docs match codebase reality.

Load: `references/learn-workflow.md` for full protocol.

**What it does:**

1. **Scout** — parallel codebase reconnaissance with scale awareness and monorepo detection
2. **Analyze** — project type classification, tech stack detection, staleness measurement
3. **Map** — dynamic doc discovery (`docs/*.md`), gap analysis, conditional doc selection
4. **Generate** — spawn docs-manager with structured prompt template and full context
5. **Validate** — mechanical verification (code refs, links, completeness, size compliance)
6. **Fix** — validation-fix loop: re-generate failed docs with feedback (max 3 retries)
7. **Finalize** — inventory check, git diff summary, size compliance
8. **Log** — record results to learn-results.tsv

**4 Modes:**

| Mode | Purpose | Autoresearch Loop? |
|------|---------|-------------------|
| `init` | Learn codebase from scratch, generate all docs | Yes — validate-fix cycle |
| `update` | Learn what changed, refresh existing docs | Yes — validate-fix cycle |
| `check` | Read-only health/staleness assessment | No — diagnostic only |
| `summarize` | Quick codebase summary with file inventory | Minimal — size check only |

**Key behaviors:**
- Fully dynamic doc discovery — scans `docs/*.md`, no hardcoded file lists
- State-aware mode detection — auto-selects init/update based on docs/ state
- Project-type-adaptive — creates deployment-guide.md only if deployment config exists
- Validation-fix loop capped at 3 retries — escalates to user if unresolved
- Scale-aware scouting — adjusts parallelism for 5k+ file codebases
- Composite metric: `learn_score = validation%×0.5 + coverage%×0.3 + size_compliance%×0.2`
- Creates `learn/{YYMMDD}-{HHMM}-{slug}/` with: `learn-results.tsv`, `summary.md`, `validation-report.md`, `scout-context.md`

**Flags:**

| Flag | Purpose |
|------|---------|
| `--mode <mode>` | Operation: init, update, check, summarize (default: auto-detect) |
| `--scope <glob>` | Limit codebase learning to specific dirs |
| `--depth <level>` | Doc comprehensiveness: quick, standard, deep |
| `--scan` | Force fresh scout in summarize mode |
| `--topics <list>` | Focus summarize on specific topics |
| `--file <name>` | Selective update — target single doc |
| `--no-fix` | Skip validation-fix loop |
| `--format <fmt>` | Output format: markdown (default). Planned: confluence, rst, html |

**Usage:**
```
# Auto-detect mode and learn
/autoresearch:learn

# Initialize docs for new project
/autoresearch:learn --mode init --depth deep

# Update docs after changes
/autoresearch:learn --mode update
Iterations: 3

# Read-only health check
/autoresearch:learn --mode check

# Quick summary
/autoresearch:learn --mode summarize --scan

# Selective update of one doc
/autoresearch:learn --mode update --file system-architecture.md

# Scoped learning
/autoresearch:learn --scope src/api/**
Iterations: 5
```

### /autoresearch:reason — Adversarial Refinement for Subjective Domains

Isolated multi-agent adversarial refinement loop. Generates, critiques, synthesizes, and blind-judges outputs through repeated rounds until convergence. Extends autoresearch to subjective domains where no objective metric (val_bpb) exists — the blind judge panel IS the fitness function.

Load: `references/reason-workflow.md` for full protocol.

**What it does:**

1. **Generate-A** — Author-A produces first candidate from task only (cold-start, no history)
2. **Critic** — Fresh agent attacks A as strawman (minimum 3 weaknesses, sees only A)
3. **Generate-B** — Author-B sees task + A + critique, produces B (no prior round history)
4. **Synthesize-AB** — Synthesizer sees task + A + B only (no critique, no judge history), produces AB
5. **Judge Panel** — N blind judges with crypto-random label assignment pick winner of A/B/AB
6. **Convergence Check** — If incumbent wins N consecutive rounds → stop. Oscillation detection → stop + flag
7. **Handoff** — Write lineage files, optional `--chain` to downstream autoresearch tools

**Key behaviors:**
- Every agent is a cold-start fresh invocation — no shared session, prevents sycophancy
- Judges receive randomized labels (X/Y/Z, not A/B/AB) — forced comparative evaluation, not individual praise
- Convergence = N consecutive rounds where incumbent wins majority vote (default: 3)
- Oscillation detection: if incumbent changes 5+ times without consecutive wins → forced stop
- Supports `--chain` for piping converged output to any autoresearch subcommand
- Composite metric: `reason_score = quality_delta*30 + rounds_survived*5 + judge_consensus*20 + critic_fatals_addressed*15 + convergence*10 + no_oscillation*5`
- Creates `reason/{YYMMDD}-{HHMM}-{slug}/` with: `overview.md`, `lineage.md`, `candidates.md`, `judge-transcripts.md`, `reason-results.tsv`, `reason-lineage.jsonl`, `handoff.json`

**Flags:**

| Flag | Purpose |
|------|---------|
| `--iterations N` | Bounded mode — run exactly N rounds |
| `--judges N` | Judge count (3-7, odd preferred, default: 3) |
| `--convergence N` | Consecutive wins to converge (2-5, default: 3) |
| `--mode <mode>` | convergent (default), creative (no auto-stop), debate (no synthesis) |
| `--domain <type>` | Shape judge personas: software, product, business, security, research, content |
| `--chain <targets>` | Chain to tools. Single: `--chain debug`. Multi: `--chain scenario,debug,fix` (sequential) |
| `--judge-personas <list>` | Override default judge personas |
| `--no-synthesis` | Skip synthesis step (A vs B only, alias for `--mode debate`) |

**Usage:**
```
# Standard convergent refinement
/autoresearch:reason
Task: Should we use event sourcing for our order management system?
Domain: software

# Bounded with custom judges
/autoresearch:reason --judges 5 --iterations 10
Task: Write a compelling pitch for our Series A
Domain: business

# Creative mode — explore alternatives, no convergence stop
/autoresearch:reason --mode creative --iterations 8
Task: Design the authentication architecture for a multi-tenant SaaS platform
Domain: software

# Chain to downstream tools after convergence
/autoresearch:reason --chain scenario,debug,fix
Task: Propose a caching strategy for high-traffic API endpoints
Domain: software
Iterations: 6

# Debate mode — A vs B, no synthesis
/autoresearch:reason --mode debate --judges 5
Task: Is microservices the right architecture for our 5-person startup?
Domain: software

# Multi-chain pipeline: reason → plan → fix
/autoresearch:reason --chain plan,fix
Task: Design the database schema for our order management system
Domain: software
Iterations: 5
```

### /autoresearch:plan — Goal → Configuration Wizard

Converts a plain-language goal into a validated, ready-to-execute autoresearch configuration.

Load: `references/plan-workflow.md` for full protocol.

**Quick summary:**

1. **Capture Goal** — ask what the user wants to improve (or accept inline text)
2. **Analyze Context** — scan codebase for tooling, test runners, build scripts
3. **Define Scope** — suggest file globs, validate they resolve to real files
4. **Define Metric** — suggest mechanical metrics, validate they output a number
5. **Define Direction** — higher or lower is better
6. **Define Verify** — construct the shell command, **dry-run it**, confirm it works
7. **Confirm & Launch** — present the complete config, offer to launch immediately

**Critical gates:**
- Metric MUST be mechanical (outputs a parseable number, not subjective)
- Verify command MUST pass a dry run on the current codebase before accepting
- Scope MUST resolve to ≥1 file

**Usage:**
```
/autoresearch:plan
Goal: Make the API respond faster

/autoresearch:plan Increase test coverage to 95%

/autoresearch:plan Reduce bundle size below 200KB
```

After the wizard completes, the user gets a ready-to-paste `/autoresearch` invocation — or can launch it directly.

## When to Activate

- User invokes `/autoresearch` → run the loop
- User invokes `/autoresearch:plan` → run the planning wizard
- User invokes `/autoresearch:security` → run the security audit
- User says "help me set up autoresearch", "plan an autoresearch run" → run the planning wizard
- User says "security audit", "threat model", "OWASP", "STRIDE", "find vulnerabilities", "red-team" → run the security audit
- User invokes `/autoresearch:ship` → run the ship workflow
- User says "ship it", "deploy this", "publish this", "launch this", "get this out the door" → run the ship workflow
- User invokes `/autoresearch:debug` → run the debug loop
- User says "find all bugs", "hunt bugs", "debug this", "why is this failing", "investigate" → run the debug loop
- User invokes `/autoresearch:fix` → run the fix loop
- User says "fix all errors", "make tests pass", "fix the build", "clean up errors" → run the fix loop
- User invokes `/autoresearch:scenario` → run the scenario loop
- User says "explore scenarios", "generate use cases", "what could go wrong", "stress test this feature", "edge cases for" → run the scenario loop
- User invokes `/autoresearch:learn` → run the learn workflow
- User says "learn this codebase", "generate docs", "document this project", "create documentation", "update docs", "check docs", "docs health" → run the learn workflow
- User invokes `/autoresearch:predict` → run the predict workflow
- User says "predict", "multi-perspective", "swarm analysis", "what do multiple experts think", "analyze from different angles" → run the predict workflow
- User invokes `/autoresearch:reason` → run the reason loop
- User says "reason through this", "adversarial refinement", "debate and converge", "iterative argument", "blind judging", "multi-agent critique" → run the reason loop
- User says "work autonomously", "iterate until done", "keep improving", "run overnight" → run the loop
- Any task requiring repeated iteration cycles with measurable outcomes → run the loop

## Bounded Iterations

By default, autoresearch loops **forever** until manually interrupted. To run exactly N iterations, add `Iterations: N` to your inline config.

**Unlimited (default):**
```
/autoresearch
Goal: Increase test coverage to 90%
```

**Bounded (N iterations):**
```
/autoresearch
Goal: Increase test coverage to 90%
Iterations: 25
```

After N iterations Claude stops and prints a final summary with baseline → current best, keeps/discards/crashes. If the goal is achieved before N iterations, Claude prints early completion and stops.

### When to Use Bounded Iterations

| Scenario | Recommendation |
|----------|---------------|
| Run overnight, review in morning | Unlimited (default) |
| Quick 30-min improvement session | `Iterations: 10` |
| Targeted fix with known scope | `Iterations: 5` |
| Exploratory — see if approach works | `Iterations: 15` |
| CI/CD pipeline integration | `--iterations N` flag (set N based on time budget) |

## Setup Phase (Do Once)

**If the user provides Goal, Scope, Metric, and Verify inline** → extract them and proceed to step 5.

**CRITICAL: If ANY critical field is missing (Goal, Scope, Metric, Direction, or Verify), you MUST use `AskUserQuestion` to collect them interactively. DO NOT proceed to The Loop or any execution phase without completing this setup. This is a BLOCKING prerequisite.**

### Interactive Setup (when invoked without full config)

Scan the codebase first for smart defaults, then ask ALL questions in batched `AskUserQuestion` calls (max 4 per call). This gives users full clarity upfront.

**Batch 1 — Core config (4 questions in one call):**

Use a SINGLE `AskUserQuestion` call with these 4 questions:

| # | Header | Question | Options (smart defaults from codebase scan) |
|---|--------|----------|----------------------------------------------|
| 1 | `Goal` | "What do you want to improve?" | "Test coverage (higher)", "Bundle size (lower)", "Performance (faster)", "Code quality (fewer errors)" |
| 2 | `Scope` | "Which files can autoresearch modify?" | Suggested globs from project structure (e.g. "src/**/*.ts", "content/**/*.md") |
| 3 | `Metric` | "What number tells you if it got better? (must be a command output, not subjective)" | Detected options: "coverage % (higher)", "bundle size KB (lower)", "error count (lower)", "test pass count (higher)" |
| 4 | `Direction` | "Higher or lower is better?" | "Higher is better", "Lower is better" |

**Batch 2 — Verify + Guard + Launch (3 questions in one call):**

| # | Header | Question | Options |
|---|--------|----------|---------|
| 5 | `Verify` | "What command produces the metric? (I'll dry-run it to confirm)" | Suggested commands from detected tooling |
| 6 | `Guard` | "Any command that must ALWAYS pass? (prevents regressions)" | "npm test", "tsc --noEmit", "npm run build", "Skip — no guard" |
| 7 | `Launch` | "Ready to go?" | "Launch (unlimited)", "Launch with iteration limit", "Edit config", "Cancel" |

**After Batch 2:** Dry-run the verify command. If it fails, ask user to fix or choose a different command. If it passes, proceed with launch choice.

**IMPORTANT:** You MUST call `AskUserQuestion` with batched questions — never ask one at a time, and never skip this step. Users should see all config choices together for full context. DO NOT proceed to Setup Steps or The Loop without completing interactive setup.

### Setup Steps (after config is complete)

1. **Read all in-scope files** for full context before any modification
2. **Define the goal** — extracted from user input or inline config
3. **Define scope constraints** — validated file globs
4. **Define guard (optional)** — regression prevention command
5. **Create a results log** — Track every iteration (see `references/results-logging.md`)
6. **Establish baseline** — Run verification on current state AND guard (if set). Record as iteration #0
7. **Confirm and go** — Show user the setup, get confirmation, then BEGIN THE LOOP

## The Loop

Read `references/autonomous-loop-protocol.md` for full protocol details.

```
LOOP (FOREVER or N times):
  1. Review: Read current state + git history + results log
  2. Ideate: Pick next change based on goal, past results, what hasn't been tried
  3. Modify: Make ONE focused change to in-scope files
  4. Commit: Git commit the change (before verification)
  5. Verify: Run the mechanical metric (tests, build, benchmark, etc.)
  6. Guard: If guard is set, run the guard command
  7. Decide:
     - IMPROVED + guard passed (or no guard) → Keep commit, log "keep", advance
     - IMPROVED + guard FAILED → Revert, then try to rework the optimization
       (max 2 attempts) so it improves the metric WITHOUT breaking the guard.
       Never modify guard/test files — adapt the implementation instead.
       If still failing → log "discard (guard failed)" and move on
     - SAME/WORSE → Git revert, log "discard"
     - CRASHED → Try to fix (max 3 attempts), else log "crash" and move on
  8. Log: Record result in results log
  9. Repeat: Go to step 1.
     - If unbounded: NEVER STOP. NEVER ASK "should I continue?"
     - If bounded (N): Stop after N iterations, print final summary
```

## Critical Rules

1. **Loop until done** — Unbounded: loop until interrupted. Bounded: loop N times then summarize.
2. **Read before write** — Always understand full context before modifying
3. **One change per iteration** — Atomic changes. If it breaks, you know exactly why
4. **Mechanical verification only** — No subjective "looks good". Use metrics
5. **Automatic rollback** — Failed changes revert instantly. No debates
6. **Simplicity wins** — Equal results + less code = KEEP. Tiny improvement + ugly complexity = DISCARD
7. **Git is memory** — Every experiment committed with `experiment:` prefix. Use `git revert` (not `git reset --hard`) for rollbacks so failed experiments remain visible in history. Agent MUST read `git log` and `git diff` of kept commits to learn patterns before each iteration
8. **When stuck, think harder** — Re-read files, re-read goal, combine near-misses, try radical changes. Don't ask for help unless truly blocked by missing access/permissions

## Principles Reference

See `references/core-principles.md` for the 7 generalizable principles from autoresearch.

## Adapting to Different Domains

| Domain | Metric | Scope | Verify Command | Guard |
|--------|--------|-------|----------------|-------|
| Backend code | Tests pass + coverage % | `src/**/*.ts` | `npm test` | — |
| Frontend UI | Lighthouse score | `src/components/**` | `npx lighthouse` | `npm test` |
| ML training | val_bpb / loss | `train.py` | `uv run train.py` | — |
| Blog/content | Word count + readability | `content/*.md` | Custom script | — |
| Performance | Benchmark time (ms) | Target files | `npm run bench` | `npm test` |
| Refactoring | Tests pass + LOC reduced | Target module | `npm test && wc -l` | `npm run typecheck` |
| Security | OWASP + STRIDE coverage + findings | API/auth/middleware | `/autoresearch:security` | — |
| Shipping | Checklist pass rate (%) | Any artifact | `/autoresearch:ship` | Domain-specific |
| Debugging | Bugs found + coverage | Target files | `/autoresearch:debug` | — |
| Fixing | Error count (lower) | Target files | `/autoresearch:fix` | `npm test` |
| Scenario analysis | Scenario coverage score (higher) | Feature/domain files | `/autoresearch:scenario` | — |
| Scenarios | Use cases + edge cases + dimension coverage | Target feature/files | `/autoresearch:scenario` | — |
| Prediction | Findings + hypotheses (higher) | Target files | `/autoresearch:predict` | — |
| Documentation | Validation pass rate (higher) | `docs/*.md` | `/autoresearch:learn` | `npm test` |
| Subjective refinement | Judge consensus + convergence (higher) | Any subjective content | `/autoresearch:reason` | — |

Adapt the loop to your domain. The PRINCIPLES are universal; the METRICS are domain-specific.

## Post-Completion: Support Prompt (Once Per Project)

After the FIRST successful completion of any autoresearch command in a project (loop finishes, summary printed, or bounded iterations complete), check if the file `.autoresearch-starred` exists in the project root.

**If it does NOT exist:**

Ask the user: *"If autoresearch helped, would you like to star the repo on GitHub to support the project? I can run `gh api -X PUT /user/starred/uditgoenka/autoresearch` for you."*

- If yes → run the command, then create `.autoresearch-starred` marker file
- If no → create `.autoresearch-starred` marker file (never ask again)

**If it exists:** skip entirely. Never ask twice.
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