autoresearch

name	autoresearch
description	Autonomous Goal-directed Iteration. Apply Karpathy's autoresearch principles to ANY task. Loops autonomously — modify, verify, keep/discard, repeat. Supports optional loop count via Claude Code's /loop command.
version	1.3.3

Claude Autoresearch — Autonomous Goal-directed Iteration

Inspired by Karpathy's 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.

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:security — Autonomous Security Audit (v1.0.3)

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
/loop 10 /autoresearch:security

# 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
/loop 15 /autoresearch:security --fix

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

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

Inspired by:

• 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 (v1.1.0)

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
/loop 5 /autoresearch:ship

# 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: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 or /ug: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 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

Optional: Controlled Loop Count

By default, autoresearch loops forever until manually interrupted. However, users can optionally specify a loop count to limit iterations using Claude Code's built-in /loop command.

Requires: Claude Code v1.0.32+ (the /loop command was introduced in this version)

Usage

Unlimited (default):

/autoresearch
Goal: Increase test coverage to 90%

Bounded (N iterations):

/loop 25 /autoresearch
Goal: Increase test coverage to 90%

This chains /autoresearch with /loop 25, running exactly 25 iteration cycles. After 25 iterations, Claude stops and prints a final summary.

When to Use Bounded Loops

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

Behavior with Loop Count

When a loop count is specified:

• Claude runs exactly N iterations through the autoresearch loop
• After iteration N, Claude 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
• All other rules (atomic changes, mechanical verification, auto-rollback) still apply

Setup Phase (Do Once)

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

If any critical field is missing → use AskUserQuestion to collect them interactively:

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 /loop N", "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: Always batch questions — never ask one at a time. Users should see all config choices together for full context.

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 kept change committed. Agent reads history to learn patterns
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

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