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codex-autoresearch-loopCodex autoresearch loop 搜索

Agent Skill

codex-autoresearch-loop 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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21,763

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889

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:codex-autoresearch-loop(Codex autoresearch loop 搜索)
来源仓库:https://github.com/aradotso/trending-skills
仓库路径:skills/codex-autoresearch-loop
安装命令:
npx skills add https://github.com/aradotso/trending-skills --skill codex-autoresearch-loop
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aradotso/trending-skills --skill codex-autoresearch-loop

简介

codex-autoresearch-loop 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 它基于 ara.so 的每日技能集合,支持自主修改→验证→保留/回滚的代码库迭代流程。
  • 用户描述可衡量的目标后,系统自动迭代改进并提交 git,失败时自动回退。
  • 安装命令为 npx skills add https://github.com/aradotso/trending-skills --skill codex-autoresearch-loop,需确认权限和执行环境。
  • 使用前建议核实仓库维护状态及是否会触发联网或文件操作。

SKILL.md

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:

  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.


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:

GatePurposeFails means
VerifyDid the target metric improve?Change discarded, reverted
GuardDid 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:

PhraseEffect
Iterations: 20Cap the loop at 20 iterations
Parallel: 3Test 3 hypotheses concurrently per round
Guard: npm testOverride 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 discardsAction
3REFINE — narrow hypothesis, try smaller atomic changes
5PIVOT — change strategy entirely
2 PIVOTsWeb 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

FileContents
.agents/skills/codex-autoresearch/lessons.mdStructured lessons from every iteration
.agents/skills/codex-autoresearch/results.logFull per-iteration log (metric value, kept/reverted, elapsed)
.agents/skills/codex-autoresearch/session.jsonCurrent 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: 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
<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

适合场景

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用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.84%
按下载量换算2,523

Claude

27.75%
按下载量换算1,954

Cursor

18.8%
按下载量换算1,324

Gemini CLI

8.5%
按下载量换算598

安全审计

Gen Agent Trust Hub

未通过

Socket

可疑

Snyk

未通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/aradotso/trending-skills --skill codex-autoresearch-loop 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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