Codex Autoresearch

Skill by ara.so — Daily 2026 Skills collection.

Codex Autoresearch is a Codex skill that runs an autonomous modify→verify→keep/revert loop on your codebase. You describe a measurable goal in one sentence; Codex confirms the plan, then iterates unattended — every improvement stacks in git, every failure reverts automatically — until interrupted or a cap is reached. Inspired by Karpathy's autoresearch concept, generalized beyond ML training to any software metric.

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Installation

Option A — manual copy into your project:

git clone https://github.com/leo-lilinxiao/codex-autoresearch.git
cp -r codex-autoresearch your-project/.agents/skills/codex-autoresearch

Option B — Codex skill installer:

$skill-installer install https://github.com/leo-lilinxiao/codex-autoresearch

The skill lives at .agents/skills/codex-autoresearch/ inside your project. No config file is required before first use.

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How to Activate

Open Codex in your project directory and prefix your goal with $codex-autoresearch:

$codex-autoresearch
I want to get rid of all `any` types in my TypeScript code

Codex will:

1. Scan the repo and infer scope, metric, verify command, and guard command.
2. Present a confirmation summary — reply go (or correct anything).
3. Run the loop unattended until you interrupt it or the goal is met.

You never write config. Codex infers everything.

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Confirmation Flow

Before the loop starts Codex always shows what it found and asks you to confirm. Example exchange:

Codex: I found 47 `any` occurrences across src/**/*.ts.

       Confirmed:
       - Target: eliminate `any` types in src/**/*.ts
       - Metric: `any` count (current: 47), direction: lower
       - Verify: grep + tsc --noEmit as guard

       Need to confirm:
       - Run until all gone, or cap at N iterations?

       Reply "go" to start, or tell me what to change.

You:   Go, run overnight.

Codex: Starting — baseline: 47. Iterating until interrupted.

Up to five confirmation rounds are possible. After that, Codex proceeds.

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The Loop (internals)

PHASE 0: Probe environment (CPU/GPU/RAM/toolchains), check for session resume
PHASE 1: Read context + lessons file from prior run (if any)

LOOP (forever or N times):
  1. Review current state, git history, results log, lessons
  2. Pick ONE hypothesis (apply perspectives, filter by environment)
     -- or N hypotheses if parallel mode is active
  3. Make ONE atomic change
  4. git commit (before verification)
  5. Run verify command  →  did the target metric improve?
     Run guard command   →  did anything else break?
  6. Improved → keep (extract lesson)
     Worse    → approved rollback strategy (git revert)
     Crashed  → fix or skip
  7. Log the result to results log
  8. Health check (disk, git, verify health)
  9. If 3+ discards → REFINE; 5+ → PIVOT; 2 PIVOTs → web search
 10. Repeat. Never stop. Never ask.

The loop runs unbounded unless you say Iterations: N during confirmation.

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Dual-Gate Verification

Two commands serve distinct purposes:

Gate	Purpose	Fails means
Verify	Did the target metric improve?	Change discarded, reverted
Guard	Did anything else break?	Change reworked (up to 2 attempts), then reverted

Guard files are never modified by the loop.

Example verify + guard pair for a Python coverage run:

Verify: pytest --cov=src --cov-report=term 2>&1 | grep TOTAL | awk '{print $NF}'
Guard:  python -m mypy src --ignore-missing-imports

Example for TypeScript type cleanup:

Verify: grep -r "any" src --include="*.ts" | wc -l
Guard:  npx tsc --noEmit

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Modes

Codex maps your sentence to one of seven modes automatically — you never pick a mode explicitly.

loop — iterate toward a measurable target (default)

$codex-autoresearch
Improve test coverage in src/ to at least 80%

$codex-autoresearch
Reduce bundle size — it's currently 2.3 MB, get it under 1 MB

plan — turn a vague goal into a validated loop config

$codex-autoresearch
I want to make our API faster but I don't know where to start

Codex will interview you (p95 latency vs throughput? which endpoint?) and produce a ready-to-run loop config.

fix — repair errors until count reaches zero

$codex-autoresearch
pytest is failing, 12 tests broken after the refactor — fix them all

debug — evidence-driven root-cause hunting

$codex-autoresearch
Our API returns 503 randomly under load, no idea why

Each iteration tests one falsifiable hypothesis. Codex presents evidence, not guesses.

security — read-only STRIDE + OWASP audit

$codex-autoresearch
Is this code secure?

ship — readiness verification and release gating

$codex-autoresearch
Ship it

exec — one-shot execution with no loop

$codex-autoresearch
Run the benchmark suite and summarize results

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Inline Configuration (optional)

You can override defaults inline during the confirmation step — no file edits needed:

Phrase	Effect
Iterations: 20	Cap the loop at 20 iterations
Parallel: 3	Test 3 hypotheses concurrently per round
Guard: npm test	Override the inferred guard command
Verify: <command>	Override the inferred verify command
Scope: src/api/	Restrict changes to a subdirectory

Example during confirmation:

You:   Go. Iterations: 30, Guard: npm test, Scope: src/api/

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Cross-Run Learning

At the end of each iteration Codex writes a structured lesson to .agents/skills/codex-autoresearch/lessons.md:

Iteration 7 — KEPT
Hypothesis: replace explicit `any` with inferred generic in src/utils/mapper.ts
Change: added <T extends Record<string, unknown>> to mapKeys()
Result: any count 31 → 29
Lesson: Generic constraints on utility functions eliminate clusters of `any` downstream.

On session resume Codex reads this file first. Each new run benefits from prior runs.

To resume an interrupted run:

$codex-autoresearch
Resume

Codex re-reads the lessons file, checks git state, re-establishes the baseline, and continues.

---

Parallel Experiments

Request parallel mode during confirmation or at any time:

You:   Go, parallel 4

Codex runs four hypotheses concurrently, keeps the best result, discards the rest. Useful when hypothesis space is large.

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Pivot Protocol

If the loop stalls, escalation happens automatically:

Consecutive discards	Action
3	REFINE — narrow hypothesis, try smaller atomic changes
5	PIVOT — change strategy entirely
2 PIVOTs	Web search — Codex fetches external references to unstick itself

You are never asked for permission during escalation. The loop continues.

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Real Code Examples

Example 1 — TypeScript any elimination (Python verify script)

If you want a custom verify script instead of a one-liner:

# scripts/count_any.py
import subprocess, sys

result = subprocess.run(
    ["grep", "-r", "--include=*.ts", r"\bany\b", "src/"],
    capture_output=True, text=True
)
count = len(result.stdout.strip().splitlines())
print(count)
sys.exit(0)  # always exit 0; the number is what matters

Tell Codex during confirmation:

Verify: python scripts/count_any.py
Guard:  npx tsc --noEmit

Example 2 — pytest coverage loop (Python)

# scripts/coverage_pct.py
import subprocess, re, sys

out = subprocess.check_output(
    ["pytest", "--cov=src", "--cov-report=term", "-q"],
    stderr=subprocess.STDOUT, text=True
)
match = re.search(r"TOTAL\s+\d+\s+\d+\s+(\d+)%", out)
if match:
    print(int(match.group(1)))
    sys.exit(0)
print(0)
sys.exit(0)

$codex-autoresearch
Improve test coverage — target 85%

Verify: python scripts/coverage_pct.py
Guard:  python -m mypy src
Direction: higher
Target: 85
Iterations: 50

Example 3 — bundle size loop (Node.js project)

# scripts/bundle_size.sh
#!/usr/bin/env bash
npm run build --silent 2>/dev/null
du -k dist/bundle.js | awk '{print $1}'

$codex-autoresearch
Reduce our JS bundle size, currently ~2300 KB, target under 900 KB

Verify: bash scripts/bundle_size.sh
Guard:  npm test
Direction: lower
Target: 900

Example 4 — lint warning count (any language)

# scripts/lint_count.sh
#!/usr/bin/env bash
npx eslint src/ --format json 2>/dev/null \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print(sum(len(f['messages']) for f in d))"

$codex-autoresearch
Get our ESLint warning count to zero

Verify: bash scripts/lint_count.sh
Direction: lower
Target: 0

---

Unattended Runs

For overnight or long runs, ensure Codex CLI approval settings do not interrupt git commit or git revert commands. The simplest option is to run in a disposable or sandboxed repo clone:

git clone . /tmp/autoresearch-sandbox
cd /tmp/autoresearch-sandbox
# launch Codex here with full permissions

Results accumulate in git history. Pull the winning commits back to your main repo when done:

# in your main repo
git fetch /tmp/autoresearch-sandbox main
git cherry-pick <winning-commit-sha>

---

Session Artifacts

File	Contents
.agents/skills/codex-autoresearch/lessons.md	Structured lessons from every iteration
.agents/skills/codex-autoresearch/results.log	Full per-iteration log (metric value, kept/reverted, elapsed)
.agents/skills/codex-autoresearch/session.json	Current session state for resume

These files persist across Codex sessions. Delete them to start fresh.

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Troubleshooting

Loop reverts every change:

• Verify command may be returning a non-numeric value. Test it manually: bash -c "<your verify command>" should print a single number.
• Metric direction may be wrong. Confirm Direction: lower or Direction: higher during setup.

Guard fires on unrelated files:

• Narrow scope: Scope: src/specific-module/
• Or tell Codex explicitly: Do not touch tests/ during confirmation.

Session resume picks up wrong baseline:

• Delete session.json to force a fresh baseline: rm .agents/skills/codex-autoresearch/session.json

Parallel mode produces merge conflicts:

• Codex handles this internally via the pivot protocol, but if it gets stuck, reduce parallelism: Parallel: 2

Codex asks questions mid-loop:

• This means a guard crash produced ambiguous output. Pre-empt it by specifying Guard: <command> || true if guard failures should be non-fatal, or by giving Codex fuller sandbox permissions so it can run git commands freely.

Loop hits PIVOT but makes no progress:

• Supply a seed hypothesis during confirmation: Hint: try tree-shaking unused imports first
• Or run plan mode first to produce a richer hypothesis list before switching to loop.

---

Quick Reference

# Start a loop
$codex-autoresearch
how to use codex-autoresearch-loop
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use codex-autoresearch-loop on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add codex-autoresearch-loop
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/aradotso/trending-skills --skill codex-autoresearch-loopcopy
The skills CLI fetches codex-autoresearch-loop from GitHub repository aradotso/trending-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/codex-autoresearch-loop
Reload or restart Cursor to activate codex-autoresearch-loop. Access the skill through slash commands (e.g., /codex-autoresearch-loop) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?