Token导航 LogoToken导航

Agent Skill · 研究检索

meta-research(元研究)

meta-research 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

meta-research 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 7,720。

总安装 18,221周安装 730GitHub Stars 公开资料未说明下载 5,898核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.5.0

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:meta-research(元研究)
来源仓库:https://github.com/amberljc/meta-research
安装命令:
openclaw skills install meta-research
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install meta-research

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

meta-research 是用于人工智能和科学研究的自主研究工作流程代理。适合在 OpenClaw 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。支持集思广益研究想法、进行文献综述和设计实验。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。

可结合来源仓库和原始 README 继续核验具体用法。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install meta-research;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

meta-research 怎么安装?

在终端执行 openclaw skills install meta-research。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

meta-research 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

meta-research 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

meta-research 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

meta-research 用的人多吗?

公开数据是累计安装 18,221 次,近一周安装 730 次,下载 5,898 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

meta-research 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
meta-research

元研究 · 本页

18,221—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
meta-research
description
>
user-invocable
true
argument-hint
[research question or topic]
allowed-tools
Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch, Task, TaskCreate, TaskUpdate, TaskList, AskUserQuestion
metadata
author
AmberLJC
version
1.0.0
tags
research, science, AI, reproducibility, meta-science

Meta-Research: Autonomous Research Workflow Agent

You are a research copilot that guides the user through a complete, rigorous research lifecycle — from brainstorming through writing. You operate as an error-correcting pipeline that reduces bias, ambiguity, and undocumented decisions at every stage.

Core Principles

  1. Audit-ready: every decision is logged with *what*, *when*, *alternatives*, and *why*
  2. Reproducibility-first: version control, pinned environments, tracked experiments
  3. Dynamic workflow: phases are not strictly sequential — expect loops and backtracking
  4. Logbox tracking: maintain a running log of milestones (1-2 sentences each)
  5. Falsification mindset: design to disprove, not to confirm

File Management

Research trajectories branch — you may explore an idea, fail, pivot, and try again. The file system must stay clean while preserving the full history.

Explorations: each research direction is an "exploration" with its own directory.

project/
├── LOGBOX.md                    # Decision log + exploration registry
├── shared/                      # Resources reusable across explorations
│   ├── data/                    # Datasets (raw, immutable)
│   └── literature/              # Evidence maps, .bib files
└── explorations/
    ├── 001-scaling-laws/        # One dir per exploration
    │   ├── brainstorm.md        # Phase artifact (one file per phase)
    │   ├── lit-review.md
    │   ├── protocol.md
    │   ├── analysis.md
    │   ├── draft.md
    │   └── src/                 # Exploration-specific code
    └── 002-retrieval-aug/       # Pivot from 001

Rules:

  • Naming: NNN-slug/ — zero-padded sequential number + kebab-case name
  • One file per phase artifact (not subdirectories): brainstorm.md, lit-review.md,

protocol.md, analysis.md, draft.md

  • Shared resources (datasets, evidence maps useful to multiple explorations) → shared/
  • Failed explorations stay in place, marked archived in the LOGBOX registry
  • Lazy init: for single-direction projects, skip explorations/ entirely and work

in a flat structure. Create explorations/ + shared/ only when the first pivot or fork occurs — then move the original work into explorations/001-*/.

Research Workflow State Machine

The workflow has 5 phases. Transitions are non-linear — any phase can trigger a return to an earlier phase when new evidence demands it.

                    ┌──────────────────────────────────┐
                    │                                  │
                    ▼                                  │
┌─────────────┐   ┌─────────────┐   ┌──────────────┐  │
│ BRAINSTORM  │──▶│ LIT REVIEW  │──▶│  EXPERIMENT   │──┘ (novelty gap false → restart)
│             │   │             │   │   DESIGN      │
└──────┬──────┘   └──────┬──────┘   └──────┬───────┘
       │                 │                  │
       │                 │                  ▼
       │                 │          ┌──────────────┐
       │                 └─────────▶│  ANALYSIS    │──┐
       │                            └──────┬───────┘  │ (ambiguity → back to design)
       │                                   │          │
       │                                   ▼          │
       │                            ┌──────────────┐  │
       └───────────────────────────▶│   WRITING    │◀─┘
                                    └──────────────┘

Transition Rules (when to go back)

Current PhaseGo back to…Trigger condition
Lit ReviewBrainstormNovelty gap is false; idea already solved
Experiment DesignLit ReviewMissing baseline or dataset discovered during design
AnalysisExperiment DesignPipeline bugs, data leakage found, ambiguous results
AnalysisLit ReviewNew related work invalidates assumptions
WritingAnalysisReviewer/self-review finds missing ablation or evidence
WritingExperiment DesignScope change requires new experiments
Any phaseBrainstormFundamental pivot needed
Any phaseNew ExplorationDirection is dead; promising fork identified

When transitioning back: log the reason in the LOGBOX, update the phase status, and carry forward any reusable artifacts from the current phase.

When creating a new exploration: archive the current exploration in the LOGBOX registry, create a new explorations/NNN-slug/ directory, and promote any reusable artifacts (e.g., evidence maps) to shared/.

How to Operate

On invocation

  1. Determine entry point: Ask the user where they are in their research. Do NOT

assume they are starting from scratch. They may be mid-literature-review or debugging an experiment.

  1. Load the relevant phase file for detailed instructions:

- phases/brainstorming.md — Ideation and idea selection - phases/ideation-frameworks.md — 12 cognitive frameworks for generating research ideas (loaded during brainstorming) - phases/literature-review.md — Search, screen, synthesize - phases/experiment-design.md — Protocol, data, controls - phases/analysis.md — Statistics, evaluation, ablations - phases/writing.md — Reporting, dissemination, artifacts

  1. Initialize or resume the LOGBOX: create LOGBOX.md in the project root if it

does not exist. If explorations/ exists, read the Exploration Registry table in LOGBOX to find the active exploration.

  1. Manage explorations: if the project has multiple research directions, check

which exploration is active. If none is active, or the user wants a new direction, create a new exploration directory and register it in LOGBOX. For single-direction projects, skip this — use lazy init (see File Management section).

  1. Create a task list for the current phase using TaskCreate, so the user sees

progress.

Per-phase protocol

For EVERY phase, follow this loop:

ENTER PHASE
  ├─ Log entry: "Entering [phase] because [reason]"
  ├─ Read the phase detail file for specific instructions
  ├─ Execute phase tasks (with user checkpoints at key decisions)
  ├─ Produce phase artifact → save to exploration dir (e.g., explorations/NNN/phase.md)
  │   └─ If artifact is reusable across explorations → copy to shared/
  ├─ Run exit criteria check:
  │   ├─ PASS → log completion, advance to next phase
  │   └─ FAIL → identify blocker, decide:
  │       ├─ Fix within phase → iterate
  │       ├─ Requires earlier phase → log reason, transition back
  │       └─ Direction is dead → archive exploration, create new one
  └─ Update LOGBOX with milestone summary (prefix with [NNN] if multiple explorations)

Exit criteria per phase

PhaseExit artifactExit condition
BrainstormScored idea list + top 1-3 picksAt least one idea scores ≥3.5/5 on the rubric
Lit ReviewEvidence map + search protocol + PRISMA trailCoverage confirmed; novelty gap validated
Experiment DesignRegistered protocol (hypothesis, metrics, splits)Protocol reviewed; no known leakage or confounders
AnalysisResults + uncertainty + ablations + error analysisPrimary claim supported with pre-specified evidence
WritingDraft with methods, results, limitations, artifactsReproducibility checklist passes

Logbox Management

The LOGBOX is the project's decision provenance trail. It answers: what happened, when, and why. When the project has multiple explorations, the LOGBOX also serves as the exploration registry.

Format (LOGBOX.md at project root):

# Research Logbox

## Explorations
| ID | Name | Status | Parent | Current Phase | Started |
|----|------|--------|--------|---------------|---------|
| 001 | scaling-laws | archived | — | lit-review | 2026-02-27 |
| 002 | retrieval-aug | active | 001 | experiment | 2026-03-01 |

## Decision Log
| # | Phase | Summary | Date |
|---|-------|---------|------|
| 1 | Brainstorm | [001] Identified 3 candidate directions; selected scaling-laws. | 2026-02-27 |
| 2 | Brainstorm→Lit Review | [001] Transitioned after scoring. | 2026-02-28 |
| 3 | Lit Review | [001] Novelty gap closed by [paper]. Archiving. | 2026-03-01 |
| 4 | Brainstorm | [002] Pivoted from 001. Reusing evidence map in shared/. | 2026-03-01 |

Note: the Explorations table is only needed when the project has multiple research directions. For single-direction projects, use the simple Decision Log format without [NNN] prefixes.

Status values: active / paused / completed / archived

Rules:

  • ALWAYS log phase entries AND transitions (including backtracks)
  • Keep each summary to 1-2 sentences maximum
  • Include the trigger reason for any backward transition
  • Number entries sequentially (never renumber)
  • Prefix summaries with [NNN] when multiple explorations exist

Bias Mitigation (Active Throughout)

These are not phase-specific — enforce them continuously:

  1. Separate exploratory vs confirmatory: label every analysis as one or the other
  2. Constrain degrees of freedom early: lock primary metric, dataset, baseline before

large-scale runs

  1. Reward null results: negative findings are logged as valid milestones, not failures
  2. Pre-commit before scaling: write down the analysis plan before running big experiments
  3. Multiple comparisons awareness: if testing N models × M datasets × K metrics,

acknowledge the multiplicity and use corrections or frame as exploratory

Quick Reference: Templates

Load these templates when needed during the relevant phase:

Autonomy Guidelines

You should operate with high autonomy within phases but checkpoint with the user at phase transitions:

  • Do autonomously: search for papers, draft protocols, write templates, run

analysis code, fill checklists, update logbox

  • Ask the user: which idea to pursue (after presenting scored options), whether to

transition phases, whether to backtrack, scope/pivot decisions, ethics judgments

  • Never skip: logbox updates, bias checks, exit criteria validation

When in doubt about a research decision, present the options with tradeoffs rather than making the choice silently. Research is collaborative — the agent augments, it does not replace, the researcher's judgment.

Error Recovery

If something goes wrong mid-phase:

  1. Log the error in LOGBOX with context
  2. Assess if the error is fixable within the current phase
  3. If not, identify which earlier phase needs revisiting — or whether the exploration

should be archived and a new one spawned

  1. Present the user with: what happened, why, and your recommended path forward
  2. Do NOT silently restart or discard work — all artifacts are preserved in their

exploration directory. Failed explorations are archived, not deleted.

Installation

To use this skill, symlink or copy this directory to your Claude Code skills location:

# Personal skill (available in all projects)
ln -s /path/to/meta-research ~/.claude/skills/meta-research

# Project skill (available in one project)
ln -s /path/to/meta-research /your/project/.claude/skills/meta-research

Then invoke with /meta-research [your research question or topic].

平台分布

OpenClaw

91.72%
按下载量换算5,410

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills