codex-autoresearch-loop
aradotso/trending-skills
Autonomous modify→verify→iterate loop for Codex that continuously improves measurable code metrics until goals are met.
What is codex-autoresearch-loop?
Codex Autoresearch is a self-directed skill that runs an unattended modify→verify→keep/revert loop on your codebase. You describe a measurable goal in one sentence; Codex infers the scope, metric, and verification commands, then iterates automatically—stacking improvements in git and reverting failures—until interrupted or a cap is reached. Useful for eliminating type errors, improving test coverage, reducing bundle size, or fixing broken tests without manual intervention.
- Runs autonomous modify→verify→keep/revert loops on code metrics without user intervention
- Automatically infers scope, metric, verify command, and guard command from your goal description
- Commits each change to git before verification and auto-reverts failures
- Learns from prior runs via a lessons file that carries insights across sessions
- Escalates strategy automatically (refine, pivot, web search) if the loop stalls after consecutive failures
- Supports parallel hypothesis testing (run N experiments concurrently, keep the best)
How to install codex-autoresearch-loop
npx skills add https://github.com/aradotso/trending-skills --skill codex-autoresearch-loop- Codex installed and active in your project directory
- Git repository initialized in your project
- A measurable goal expressible in one sentence (e.g., 'eliminate all `any` types', 'reach 80% test coverage')
- Verify and guard commands (Codex infers these, but custom commands can override)
How to use codex-autoresearch-loop
- 1.Install the skill via `npx skills add https://github.com/aradotso/trending-skills --skill codex-autoresearch-loop` or manually copy to `.agents/skills/codex-autoresearch/`
- 2.Open Codex in your project directory and prefix your goal with `$codex-autoresearch` followed by a one-sentence measurable goal
- 3.Review the confirmation summary Codex presents (scope, metric, verify/guard commands, iteration cap)
- 4.Optionally override defaults inline (e.g., `Iterations: 30, Parallel: 3, Guard: npm test`)
- 5.Reply 'go' to start the loop; Codex iterates unattended, committing improvements and reverting failures until interrupted or goal is met
- 6.Monitor progress via the results log and lessons file at `.agents/skills/codex-autoresearch/`; resume an interrupted run with `$codex-autoresearch Resume`
Use cases
- Eliminate all `any` types in a TypeScript codebase overnight without manual intervention
- Improve test coverage from 60% to 80% by iteratively adding tests and refactoring
- Reduce bundle size from 2.3 MB to under 1 MB by testing optimization hypotheses in parallel
- Fix all broken tests after a large refactor by iterating on failing assertions
- Debug intermittent 503 errors under load by testing falsifiable hypotheses with evidence
- TypeScript/JavaScript developers automating code quality improvements
- Teams running overnight optimization tasks without manual oversight
- QA and test engineers fixing large batches of broken tests
- Performance engineers reducing bundle size or latency metrics
- Developers debugging production issues via hypothesis-driven investigation
codex-autoresearch-loop FAQ
Codex automatically reverts the change via git and logs the failure. If the guard command fails, Codex attempts up to 2 fixes before reverting. The loop continues with the next hypothesis.
Yes, interrupt Codex at any time. To resume later, run `$codex-autoresearch Resume` and Codex will re-read the lessons file, re-establish the baseline, and continue from where it left off.
Codex infers the scope, metric, and verification commands from your one-sentence goal and the codebase structure. You can override these during confirmation with inline phrases like `Verify: <command>` or `Scope: src/api/`.
Codex escalates automatically: after 3 consecutive discards it refines (smaller changes), after 5 it pivots (new strategy), and after 2 pivots it performs a web search for external references to unstick itself.
Yes, request parallel mode during confirmation (e.g., `Go, parallel 4`) to test up to 4 hypotheses concurrently. Codex keeps the best result and discards the rest.
Full instructions (SKILL.md)
Source of truth, from aradotso/trending-skills.
name: codex-autoresearch-loop description: Self-directed iterative research skill for Codex that continuously cycles through modify, verify, retain or discard, and repeat until a measurable goal is reached. triggers:
- run autoresearch on my codebase
- iterate autonomously until tests pass
- improve my code overnight without interruption
- set up a modify verify loop
- keep improving coverage until I say stop
- eliminate all type errors automatically
- run codex autoresearch skill
- continuously optimize this metric
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.
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.
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:
- Scan the repo and infer scope, metric, verify command, and guard command.
- Present a confirmation summary — reply
go(or correct anything). - Run the loop unattended until you interrupt it or the goal is met.
You never write config. Codex infers everything.
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.
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.
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
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
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/
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.
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.
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.
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: lowerorDirection: higherduring 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.jsonto 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> || trueif 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
planmode first to produce a richer hypothesis list before switching toloop.
Quick Reference
# Start a loop
$codex-autoresearch
<your goal in one sentence>
# Resume interrupted run
$codex-autoresearch
Resume
# Bounded run
$codex-autoresearch
<goal> — Iterations: 25
# Parallel hypotheses
$codex-autoresearch
<goal> — Parallel: 4
# Force a mode
$codex-autoresearch fix
pytest has 8 failures, repair them
# Read-only audit
$codex-autoresearch security
Audit src/api/ for injection vulnerabilities
Related skills
More from aradotso/trending-skills and the wider catalog.

codex-plugin-cc
Use OpenAI Codex from inside Claude Code for code reviews and delegated background tasks.

creationix-rx-data-store
Expert skill for using RX, an embedded data store for JSON-shaped data with random-access reads, no-parse lookups, and a text-safe binary encoding format.

dingtalk-workspace-cli
Use DingTalk Workspace CLI (dws) to manage DingTalk contacts, calendar, todos, attendance, approvals, and more from the command line or AI agent workflows.

emdash-cms
AI coding agent skill for building with EmDash, the full-stack TypeScript CMS built on Astro and Cloudflare

fabro-workflow-factory
Define AI agent pipelines as Graphviz DOT graphs with human gates, multi-model routing, and cloud sandboxes.

filmkit-fujifilm-camera
Browser-based preset manager and RAW converter for Fujifilm X-series cameras using WebUSB and PTP protocol