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Agent Skill · 研究检索

related-works-report-from-paper-mds(纸质 MDS 的相关工作报告)

related-works-report-from-paper-mds 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。

related-works-report-from-paper-mds 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 27,574。

总安装 3,369周安装 139GitHub Stars 公开资料未说明下载 1,101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
1.0.0

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:related-works-report-from-paper-mds(纸质 MDS 的相关工作报告)
来源仓库:https://github.com/red0orange/related-works-report-from-paper-mds
安装命令:
openclaw skills install related-works-report-from-paper-mds
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 related-works-report-from-paper-mds

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。

related-works-report-from-paper-mds 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词快速定位候选结果时使用。从纸质 Markdown 文件中提取引用论文,构建去重后的相关工作报告。

通过 clawhub 安装,命令为 openclaw skills install related-works-report-from-paper-mds。建议确认权限范围、维护状态及是否触发文件读写操作。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

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 related-works-report-from-paper-mds;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

    它需要调用外部服务,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

related-works-report-from-paper-mds 怎么安装?

在终端执行 openclaw skills install related-works-report-from-paper-mds。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

related-works-report-from-paper-mds 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。

related-works-report-from-paper-mds 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

related-works-report-from-paper-mds 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。

related-works-report-from-paper-mds 用的人多吗?

公开数据是累计安装 3,369 次,近一周安装 139 次,下载 1,101 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。

related-works-report-from-paper-mds 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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
related-works-report-from-paper-mds

纸质 MDS 的相关工作报告 · 本页

3,369—调用外部服务MIT-0通过

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
related-works-report-from-paper-mds
description
Build a related-works report from one or more paper markdown files by extracting Related Works sections, resolving cited papers, deduplicating references, retrieving arXiv abstracts via Tavily plus local helpers, and assembling a final report inside a user-specified work folder. Use when the user points at paper_mds, wants a related-works survey report, or asks to turn several paper markdown files into a final_related_works_report.md.
argument-hint
[paper-md-paths --workdir path]
allowed-tools
Bash(*), Read, Write, Edit, Grep, Glob, Agent, Skill, call_mcp_tool, WebFetch

Related Works Report Pipeline

Orchestrate a full related-works reporting workflow for: $ARGUMENTS

Overview

This workflow turns a small set of paper markdown files into a reproducible report:

paper md files
-> step1 extract Related Works + cited papers
-> step2 deduplicate cited papers
-> sequential Tavily abstract lookup + local arXiv fetch
-> normalized citation companion text
-> final_related_works_report.md

The output is not just the final report. Every intermediate artifact is written into the user-selected work folder so the process can be resumed, audited, or partially rerun.

Required Inputs

Before running, collect or infer:

  • PAPER_MD_PATHS: one or more paper markdown paths
  • WORKDIR: a user-specified working folder

If WORKDIR is missing, ask the user for it before doing substantial work.

All process outputs and the final report must be written under WORKDIR. Do not write process artifacts elsewhere.

Constants

  • TAVILY_ONLY = true — Tavily is the only allowed search mechanism for title-to-arXiv matching
  • TAVILY_BATCH_MODE = sequential — finish batch N before batch N+1
  • NO_SEARCH_FALLBACK = true — if Tavily fails, record the failure; do not switch to arXiv search, arXiv API search, or another provider
  • LOW_CONTEXT_ABSTRACT_MODE = true — write one JSON line per title, then render markdown from JSONL
  • CITATION_NORMALIZATION_REQUIRED = true — the final workflow must consider a normalized-citation companion section so Part 1 can be aligned with dedup ids like P001
  • FINAL_REPORT_NAME = final_related_works_report.md

Work Folder Layout

Create and maintain these artifacts under WORKDIR:

ArtifactPurpose
step1_extracted_related_works_and_citations.mdPer-source Related Works verbatim + citation tables
step1_normalized_related_works.mdCitation-normalized companion text using dedup ids like P001 when replacement is unambiguous
step2_deduplicated_paper_list.mdDeduplicated paper list with source occurrences
title_batches/batch_XX.mdAbstract lookup batches
abstract_batches/batch_XX_fetches.jsonlOne JSON line per title after Tavily + local fetch
abstract_batches/batch_XX_results.mdRendered markdown per abstract batch
final_related_works_report.mdFinal assembled report

Execution Rule

Run phases in order. Do not stop after a checkpoint unless:

  • the user explicitly says to stop, or
  • an input is missing and must be confirmed, or
  • Tavily failures are severe enough that the user should decide whether to continue with partial results

Parallelism rules:

  • Phase 1 extraction may use one clean-context sub agent per source paper.
  • Phase 3 abstract lookup must run batches sequentially, not in parallel.

Phase 0: Initialize the Work Folder

  1. Validate PAPER_MD_PATHS.
  2. Create WORKDIR, WORKDIR/title_batches, and WORKDIR/abstract_batches.
  3. Record the chosen source files in the first process artifact.

Phase 1: Extract Related Works and cited papers

Use one clean-context sub agent per source markdown file.

Each sub agent must return:

  • the verbatim Related Works section
  • the papers cited inside that section
  • enough citation metadata to later support normalization and deduplication:

- citation token in the text ([12], Guo et al., 2017, etc.) - year - title - authors - raw reference text

Merge all outputs into WORKDIR/step1_extracted_related_works_and_citations.md.

Phase 2: Deduplicate cited papers

Build WORKDIR/step2_deduplicated_paper_list.md.

Rules:

  • deduplicate conservatively by normalized title
  • merge only when the works are clearly the same paper
  • preserve source occurrences so every original citation can be traced back

Phase 2B: Citation normalization companion text

Before final assembly, produce WORKDIR/step1_normalized_related_works.md.

Goal:

  • keep the original Related Works text untouched in step1_extracted_related_works_and_citations.md
  • create a companion version where in-text citations are rewritten to dedup ids like P001

Preferred format:

  • numeric citations: [12] -> [P052]
  • author-year citations: (Guo et al., 2017) -> [P095]
  • grouped citations: rewrite each cited work individually when the mapping is unambiguous

Rules:

  • only replace citations when the mapping from source citation token to dedup id is unambiguous
  • if a citation is ambiguous, keep the original token and add a short note below that section
  • do not overwrite the verbatim source text

This step exists because the final report should be easy to align with the deduplicated bibliography.

Phase 3: arXiv abstracts via Tavily + local helper

Use the helper scripts stored inside this skill:

  • .cursor/skills/related-works-report-from-paper-mds/scripts/fetch_arxiv_abs.py
  • .cursor/skills/related-works-report-from-paper-mds/scripts/jsonl_to_abstract_batch_md.py

Search rules

  • Tavily MCP only
  • query shape: "<paper title>" arXiv
  • preferred matches: abs, then html, then pdf
  • convert html/pdf to canonical https://arxiv.org/abs/<id>
  • no fallback search provider

Batch rules

  • process batches sequentially
  • inside a batch, process titles one by one
  • if Tavily rate limits, wait and retry Tavily only
  • if Tavily still fails, record the error in the JSONL and leave arXiv URL and Abstract empty

Low-context pattern

For each processed title, immediately append one JSON line to:

  • WORKDIR/abstract_batches/batch_XX_fetches.jsonl

Each line should include at least:

  • dedup_id
  • input_title
  • tavily_status
  • tavily_error
  • arxiv_url
  • fetch

After a batch completes, render markdown with:

python3 ".cursor/skills/related-works-report-from-paper-mds/scripts/jsonl_to_abstract_batch_md.py" \
  "WORKDIR/abstract_batches/batch_XX_fetches.jsonl" \
  "WORKDIR/abstract_batches/batch_XX_results.md"

Phase 4: Final assembly

Use the final builder script stored inside this skill:

python3 ".cursor/skills/related-works-report-from-paper-mds/scripts/build_final_related_works_report.py" \
  "WORKDIR/step1_extracted_related_works_and_citations.md" \
  "WORKDIR/step2_deduplicated_paper_list.md" \
  "WORKDIR/abstract_batches" \
  "WORKDIR/final_related_works_report.md" \
  "WORKDIR/step1_normalized_related_works.md"

The final report should contain:

  • Summary
  • Part 1: Related Works original text
  • Part 1B: citation-normalized companion text
  • Part 2: BibTeX-style entries with retrieved abstracts when available

Key Rules

  • Never fabricate a paper match or abstract.
  • Never use non-Tavily search when resolving titles to arXiv.
  • Keep all process artifacts inside WORKDIR.
  • Prefer scripts inside this skill over ad hoc in-message code.
  • Preserve source-paper order in Part 1.
  • Preserve dedup order from step2 in Part 2.

Utility Scripts

  • scripts/fetch_arxiv_abs.py — compact metadata + abstract extraction from a known arXiv URL
  • scripts/jsonl_to_abstract_batch_md.py — render one batch markdown from JSONL
  • scripts/build_final_related_works_report.py — assemble the final report from workdir artifacts

Additional Resources

  • For copy-paste invocations and expected WORKDIR contents, see examples.md

Example Invocation

/related-works-report-from-paper-mds \
  "0_refs/paper_mds/2025_ConfidenceVLA.md 0_refs/paper_mds/2025_SAFE.md 0_refs/paper_mds/2025_FAIL_Detect.md --workdir 0_docs/related_works_report_run_02"

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安全审计

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通过

ClawScan

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Static analysis

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权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。目前只有一个公开来源,建议同时核对源仓库。

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