Paper_claw

REVIEW · 68
Community indexed

Paper Claw sends personalized daily research digests from arXiv and beyond straight to your inbox, featuring customizable categories, intelligent classification, and agent-based multilingual summaries powered by your preferred AI via private API. Designed for researchers and AI agents, it makes paper discovery easier, smarter, and more specialized.

Verified installs0
Stars32
Version1.0.0
Quality72/100 · Strong
Trust68/100 · Sandbox only
Audit81/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Research agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent fit

Claude Code + OpenAI Agents + CLI

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add PigeonDan1/paper_claw

Maintenance

fresh

30d since push

Risk

Needs review

Permission surface may require sandboxing

GitHub quality

32

72/100 Quality · 76/100 Trust

Coverage tags

ResearchResearch agentsarxivpaper-digestai-agent

Review notes

Permission surface may require sandboxing · Low GitHub adoption signal

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Strong
72

Solid option that is likely worth shortlisting for production workflows.

Trust

Sandbox only
68

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
81

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

PythonCodexClaude CodeCursorOpenAgentSkill CLI

Stars

32 GitHub stars

Repo activity

32 stars, 3 forks

Maintenance

30d since push

License

MIT

Install

npx skills add PigeonDan1/paper_claw

Install safety

standard package or runtime install path

Permission surface

shell or command execution, network or browser access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: shell or command execution, network or browser access
  • GitHub adoption: 32 GitHub stars

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Search sources

Suited agents

PythonCodexClaude CodeCursorOpenAgentSkill CLIOpenAI AgentsCLI

Install decision

Command
npx skills add PigeonDan1/paper_claw
Policy
review
Human review
yes

Trust and risk

Trust
68/100
Audit
81/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add PigeonDan1/paper_claw

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Low GitHub adoption signal
  • High-risk permission hints: Shell or command execution
  • Permission surface may require sandboxing

Agent safety v2

53/100 · Avoid automatic install

Experimentalreview

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Resolve via API

high

Shell or command execution

Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Database access

Skill may inspect schemas, query databases, or work with persistent stores.

  • High-risk permission hints: Shell or command execution
  • Permission surface may require sandboxing

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install pigeondan1-paper-claw

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use Paper_claw in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Paper_claw%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/pigeondan1-paper-claw/install
Install command: npx skills add PigeonDan1/paper_claw
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use Paper_claw for this task. Review https://www.openagentskill.com/api/skills/pigeondan1-paper-claw/install, then install with: npx skills add PigeonDan1/paper_claw

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

74/100

Research agents

Platforms

Python, Claude Code, OpenAI Agents

Audit report

Needs review · 81/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Companion skill for Research agents

Shortlist this skill and compare it with close alternatives before production adoption.

74
Readiness
Shortlist
Stage

Role in stack

Companion skill

Primary fit

Research agents

Trust label

Strong shortlist

Install path

Command ready

Use when

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 72/100 quality profile
  • 7 OpenAgentSkill engagement events

review first

  • Low GitHub adoption signal

Implementation path

  1. 1Install it in a sandbox agent and run one Research agents task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

68
OpenAgentSkill Trust Score

GitHub adoption

CHECK

32 GitHub stars

Stars/forks activity

CHECK

32 stars, 3 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

30d since push

License clarity

PASS

MIT

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Low GitHub adoption signal
  • Quality score needs review
  • Permission surface needs review: shell or command execution, network or browser access
  • GitHub adoption: 32 GitHub stars
  • Stars/forks activity: 32 stars, 3 forks; issue activity unavailable in current metadata
  • Permission surface: shell or command execution, network or browser access
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Strong candidate for agent workflows

Solid option that is likely worth shortlisting for production workflows.

72
GitHub stars
32
Freshness
30d ago
Install ready
Yes
License
MIT
Review before install: Low GitHub adoption signal

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

<div align="center">

# 📰 Paper Claw

**Intelligent Multi-Source Paper Digest Generator**

[![Python 3.11](https://img.shields.io/badge/Python-3.11-3776AB?logo=python&logoColor=white)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![GitHub Actions](https://img.shields.io/badge/GitHub%20Actions-2088FF?logo=githubactions&logoColor=white)](.github/workflows/daily_digest.yml) [![Multi-LLM](https://img.shields.io/badge/LLM-DeepSeek%20%7C%20Kimi%20%7C%20OpenAI-blue)](https://github.com/PigeonDan1/paper_claw)

*Fetch, classify, and summarize papers from multiple sources with AI-powered digests*

[Quick Start](#-for-human-users) · [Agent Skill](#-for-ai-agents) · [ArXiv Categories](#-arxiv-categories)

</div>

---

## 👥 Two Paths

Paper Claw serves two types of users:

<table> <tr> <td width="50%" valign="top">

### 🧑‍💻 For Human Users

**I want to set up daily paper digests for my research field**

→ [Quick Start Guide](#-for-human-users)

- Configure your research domain - Set up email delivery - Choose your LLM provider - Run locally or via GitHub Actions

</td> <td width="50%" valign="top">

### 🤖 For AI Agents

**I want to integrate Paper Claw into my agent workflow**

→ [Agent Skill Guide](#-for-ai-agents)

- **One-command preset setup** for any research field - Standardized tool interface - One-line Python integration - JSON schema definitions - Auto-discovery for OpenClaw

</td> </tr> </table>

### System Architecture

<div align="center">

<img src="assets/paper_claw.png" width="90%" alt="Paper Claw System Architecture">

*Paper Claw fetches from arXiv, classifies with AI, and delivers personalized digests*

</div>

---

### Example Output

<div align="center">

**Daily Digest in Your Inbox**

<img src="assets/demo2.png" width="85%" alt="Paper Claw Daily Digest Email">

*Categorized papers with AI summaries, ready to read*

</div>

---

## 🧑‍💻 For Human Users

### Quick Start (5 minutes)

```bash # 1

Platform compatibility

pythonFULL

Technical details

Version
1.0.0
License
MIT
Last updated
Aug 18, 2026
Published
Jul 25, 2026

Frameworks & tools

Python

Decision snapshot

Companion skill

74
Ready
Shortlist
Stage

recent repository activity

Audit

Install review

Install and adoption review

81
Needs review
Security
81/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for Paper_claw, ready for a manual X post.

Curator note
Paper_claw: A tool for generating personalized daily research paper digests from arXiv and other sources,...

32 stars

https://www.openagentskill.com/skills/pigeondan1-paper-claw?ref=x
Open X draft
Optional reply with install command
Listing + install path for Paper_claw:
https://www.openagentskill.com/skills/pigeondan1-paper-claw?ref=x

Install: npx skills add PigeonDan1/paper_claw

Listing source

Community indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
PigeonDan1
Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This Community indexed listing is attributed to PigeonDan1 but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

Add the evidence badges to your README

Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/pigeondan1-paper-claw?metric=listed&label=Listed)](https://www.openagentskill.com/skills/pigeondan1-paper-claw)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/pigeondan1-paper-claw?metric=trust&label=Trust)](https://www.openagentskill.com/skills/pigeondan1-paper-claw)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/pigeondan1-paper-claw?metric=audit&label=Audit)](https://www.openagentskill.com/skills/pigeondan1-paper-claw/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/pigeondan1-paper-claw?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/pigeondan1-paper-claw)

Author

P

PigeonDan1

@pigeondan1

Health signals

GitHub stars
32
Quality score
45/100
Last GitHub push
Jul 25, 2026
Framework hints
1
OpenAgentSkill views
6
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

68
  • GitHub adoption32 GitHub starsCHECK
  • Stars/forks activity32 stars, 3 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenance30d since pushPASS
  • License clarityMITPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskcommand execution surface, network or browser surfaceINFO