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Agent Skill · 研究检索

paper-fetch(取纸)

paper-fetch 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。

paper-fetch 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;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 20,321。

总安装 5,256周安装 219GitHub Stars 1下载 1,752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
0.5.0

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:paper-fetch(取纸)
来源仓库:https://github.com/agents365-ai/paper-fetch
安装命令:
openclaw skills install paper-fetch
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 paper-fetch

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。

合法获取开放获取学术论文 PDF 的工具。支持 DOI、标题或 URL 输入,尝试多源下载。集成 Unpaywall、arXiv 等平台,提高文献获取效率。注意遵守各平台版权政策与速率限制规则。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

paper-fetch 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 paper-fetch;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

    它需要接触敏感数据,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

paper-fetch 怎么安装?

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

paper-fetch 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。

paper-fetch 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

paper-fetch 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。

paper-fetch 用的人多吗?

公开数据是累计安装 5,256 次,近一周安装 219 次,下载 1,752 次,GitHub 1 星。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。

paper-fetch 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
paper-fetch

取纸 · 本页

5,2561接触敏感数据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
paper-fetch
description
Use when the user wants to download a paper PDF from a DOI, title, or URL via legal open-access sources. Tries Unpaywall, arXiv, bioRxiv/medRxiv, PubMed Central, and Semantic Scholar in order. Never uses Sci-Hub or paywall bypass.
homepage
https://github.com/Agents365-ai/paper-fetch
metadata
{"openclaw":{"requires":{"bins":["python3"]},"emoji":"📄"},"pimo":{"category":"research","tags":["paper","pdf","doi","open-access","download"]}}

paper-fetch

Fetch the legal open-access PDF for a paper given a DOI (or title). Tries multiple OA sources in priority order and stops at the first hit.

Agent-native. Structured JSON envelope on stdout, NDJSON progress on stderr, stable exit codes, machine-readable schema, TTY-aware format default, idempotent retries.

Resolution order

  1. Unpaywall — https://api.unpaywall.org/v2/{doi}?email=$UNPAYWALL_EMAIL, read best_oa_location.url_for_pdf (skipped if UNPAYWALL_EMAIL not set)
  2. Semantic Scholar — https://api.semanticscholar.org/graph/v1/paper/DOI:{doi}?fields=openAccessPdf,externalIds
  3. arXiv — if externalIds.ArXiv present, https://arxiv.org/pdf/{arxiv_id}.pdf
  4. PubMed Central OA — if PMCID present, https://www.ncbi.nlm.nih.gov/pmc/articles/{pmcid}/pdf/
  5. bioRxiv / medRxiv — if DOI prefix is 10.1101, query https://api.biorxiv.org/details/{server}/{doi} for the latest version PDF URL
  6. Otherwise → report failure with title/authors so the user can request via ILL

If only a title is given, resolve to a DOI first via Semantic Scholar search_paper_by_title (asta MCP) or Crossref.

Usage

python scripts/fetch.py <DOI> [options]
python scripts/fetch.py --batch <FILE|-> [options]
python scripts/fetch.py schema           # machine-readable self-description

Flags

FlagDefaultDescription
doi—DOI to fetch (positional). Use - to read a single DOI from stdin
--batch FILE—File with one DOI per line for bulk download. Use - to read from stdin
--out DIRpdfsOutput directory
--dry-runoffResolve sources without downloading; preview PDF URL and destination
--formatautojson for agents, text for humans. Auto-detects: json when stdout is not a TTY, text when it is
--prettyoffPretty-print JSON with 2-space indent
--streamoffEmit one NDJSON per line on stdout as each DOI resolves, then a summary line (batch mode)
--overwriteoffRe-download even when destination file already exists
--idempotency-key KEY—Safe-retry key. Re-running with the same key replays the original envelope from <out>/.paper-fetch-idem/ without network I/O
--timeout SECONDS30HTTP timeout per request
--version—Print CLI + schema version and exit

Agent discovery: schema subcommand

python scripts/fetch.py schema

Emits a complete machine-readable description of the CLI on stdout (no network). Includes cli_version, schema_version, parameter types, exit codes, error codes, envelope shapes, and environment variables. Agents should read this once, cache it against schema_version, and re-read when the cached version drifts.

Output contract

stdout emits a single JSON envelope. Every envelope carries a meta slot.

Success (all DOIs resolved):

{
  "ok": true,
  "data": {
    "results": [
      {
        "doi": "10.1038/s41586-021-03819-2",
        "success": true,
        "source": "unpaywall",
        "pdf_url": "https://www.nature.com/articles/s41586-021-03819-2.pdf",
        "file": "pdfs/Jumper_2021_Highly_accurate_protein_structure_predic.pdf",
        "meta": {"title": "Highly accurate protein structure prediction with AlphaFold", "year": 2021, "author": "Jumper"},
        "sources_tried": ["unpaywall"]
      }
    ],
    "summary": {"total": 1, "succeeded": 1, "failed": 0},
    "next": []
  },
  "meta": {
    "request_id": "req_a908f5156fc1",
    "latency_ms": 2036,
    "schema_version": "1.1.0",
    "cli_version": "0.3.0",
    "sources_tried": ["unpaywall"]
  }
}

Partial (batch mode — some DOIs failed, exit code reflects the failure class):

{
  "ok": "partial",
  "data": {
    "results": [
      { "doi": "10.1038/s41586-021-03819-2", "success": true, "source": "unpaywall", ... },
      {
        "doi": "10.1234/nonexistent",
        "success": false,
        "source": null,
        "pdf_url": null,
        "file": null,
        "meta": {},
        "sources_tried": ["unpaywall", "semantic_scholar"],
        "error": {
          "code": "not_found",
          "message": "No open-access PDF found",
          "retryable": true,
          "retry_after_hours": 168,
          "reason": "OA availability changes over time; retry after embargo lifts or preprint appears"
        }
      }
    ],
    "summary": {"total": 2, "succeeded": 1, "failed": 1},
    "next": ["paper-fetch 10.1234/nonexistent --out pdfs"]
  },
  "meta": { ... }
}

The next slot is an array of suggested follow-up commands: re-invoking them retries only the failed subset. Combine with --idempotency-key to make the whole batch safely retriable without re-downloading the already-succeeded items.

Failure (bad arguments, exit code 3):

{
  "ok": false,
  "error": {
    "code": "validation_error",
    "message": "Provide a DOI or --batch file",
    "retryable": false
  },
  "meta": { ... }
}

Per-item skipped (destination already exists, no --overwrite):

{
  "doi": "10.1038/s41586-021-03819-2",
  "success": true,
  "source": "unpaywall",
  "pdf_url": "https://...",
  "file": "pdfs/Jumper_2021_...pdf",
  "skipped": true,
  "skip_reason": "file_exists",
  "sources_tried": ["unpaywall"]
}

Idempotency replay (re-run with the same --idempotency-key):

The cached envelope is returned verbatim, but meta.request_id and meta.latency_ms are re-stamped for the current call, and meta.replayed_from_idempotency_key is set. No network I/O occurs.

Stderr progress (NDJSON)

When --format json, stderr emits one JSON object per line for liveness:

{"event": "start",       "request_id": "req_...", "elapsed_ms": 2,    "doi": "10.1038/..."}
{"event": "source_try",  "request_id": "req_...", "elapsed_ms": 2,    "doi": "...", "source": "unpaywall"}
{"event": "source_hit",  "request_id": "req_...", "elapsed_ms": 2036, "doi": "...", "source": "unpaywall", "pdf_url": "..."}
{"event": "download_ok", "request_id": "req_...", "elapsed_ms": 4120, "doi": "...", "file": "..."}

Event types: start, source_try, source_hit, source_miss, source_skip, source_enrich, source_enrich_failed, download_ok, download_error, download_skip, dry_run, not_found, update_check_spawned. All events share request_id and elapsed_ms, letting an orchestrator correlate progress across stderr and the final stdout envelope.

source_enrich fires when Semantic Scholar is called purely to backfill missing author / title after another source already provided the PDF URL; its fields array lists exactly which fields were filled in. source_enrich_failed fires when that enrichment call fails — the Unpaywall PDF URL is still used and the filename falls back to unknown_<year>_….

When --format text, stderr emits human-readable prose.

Exit codes

CodeMeaningRetryable class
0All DOIs resolved / previewed—
1Unresolved — one or more DOIs had no OA copy; no transport failureNot now (retry after retry_after_hours)
2Reserved for auth errors (currently unused)—
3Validation error (bad arguments, missing input)No
4Transport error (network / download / IO failure)Yes

The taxonomy lets an orchestrator route failures deterministically: exit 4 is worth retrying immediately, exit 1 is not, exit 3 is a bug in the caller.

Error codes in JSON

CodeMeaningRetryable
validation_errorBad arguments or empty inputNo
not_foundNo open-access PDF foundYes (retry after retry_after_hours: 168)
download_network_errorNetwork failure during downloadYes
download_not_a_pdfResponse was not a PDF (HTML landing page)No
download_host_not_allowedPDF URL host not in allowlistNo
download_size_exceededResponse exceeded 50 MB limitYes
download_io_errorLocal filesystem write failedYes
internal_errorUnexpected errorNo

Examples

# Single DOI (JSON output when piped; text when in a terminal)
python scripts/fetch.py 10.1038/s41586-020-2649-2

# Dry-run preview (resolve without downloading)
python scripts/fetch.py 10.1038/s41586-020-2649-2 --dry-run

# Force JSON (for agents even inside a terminal)
python scripts/fetch.py 10.1038/s41586-020-2649-2 --format json

# Human-readable with pretty colors in a pipeline
python scripts/fetch.py 10.1038/s41586-020-2649-2 --format text

# Batch download, safely retriable
python scripts/fetch.py --batch dois.txt --out ./papers \
    --idempotency-key monday-review-batch

# Pipe DOIs from another tool
zot -F ids.json query ... | jq -r '.[].doi' | python scripts/fetch.py --batch -

# Agent discovery
python scripts/fetch.py schema --pretty

# Streaming mode — one result per line as each DOI resolves
python scripts/fetch.py --batch dois.txt --stream

# Works without UNPAYWALL_EMAIL (skips Unpaywall, uses remaining 4 sources)
python scripts/fetch.py 10.1038/s41586-020-2649-2

Environment

VariableDefaultPurpose
UNPAYWALL_EMAILunsetContact email for Unpaywall API. Optional but recommended. Without it, Unpaywall is skipped (remaining 4 sources still work).
PAPER_FETCH_ALLOWED_HOSTSunsetComma-separated extra hostnames to extend the download allowlist
PAPER_FETCH_NO_AUTO_UPDATEunsetSet to any value to disable silent background self-update
PAPER_FETCH_UPDATE_INTERVAL86400Cooldown in seconds between update checks

Notes

  • Auth is delegated. The agent never runs a login subcommand. The human or the orchestrator sets UNPAYWALL_EMAIL in the environment; the agent inherits it. Missing email degrades gracefully to the remaining 4 sources.
  • Trust is directional. CLI arguments are validated once at the entry point. The host allowlist and 50 MB size cap are enforced in the environment layer, not at the agent's request. An agent cannot loosen safety by passing a flag — only by the operator setting PAPER_FETCH_ALLOWED_HOSTS.
  • Downloads are naturally idempotent. Re-running against the same --out skips files that already exist (deterministic filename: {first_author}_{year}_{short_title}.pdf). Pair with --idempotency-key to also replay the exact envelope without any network I/O.
  • Never attempts to bypass paywalls. If no OA copy exists, the skill reports failure honestly — do not suggest Sci-Hub or similar.
  • Default output directory: ./pdfs/.

Auto-update

When installed via git clone, the skill keeps itself in sync with upstream automatically. On each invocation, fetch.py spawns a detached background git pull --ff-only in the skill directory:

  • Non-blocking — the current invocation is not delayed; the pull runs in a new session and is fully detached
  • Silent — all git output goes to /dev/null, the stdout envelope is never polluted
  • Throttled — at most once every 24 hours (stamped via .git/.paper-fetch-last-update)
  • Safe — --ff-only refuses to merge if you have local edits; conflicts never happen
  • Observable — when a pull is spawned, stderr emits {"event": "update_check_spawned", ...} (JSON mode) or a prose notice (text mode)
  • Convergence — updates apply on the next invocation, not the current one (because the pull is backgrounded)

Force an immediate check with rm <skill_dir>/.git/.paper-fetch-last-update.

平台分布

OpenClaw

80.29%
按下载量换算1,407

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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