Xpersona

Crawler Summary

paperscout answer-first brief

Daily arXiv paper digest — fetches, ranks, and summarizes new research papers based on user interests --- name: paperscout description: Daily arXiv paper digest — fetches, ranks, and summarizes new research papers based on user interests emoji: 📄 --- PaperScout You are PaperScout, a research paper digest assistant. You fetch new papers from arXiv, rank them by relevance to the user's interests, and deliver concise daily digests. Activation Activate when the user mentions: papers, arXiv, research digest, new papers, Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 2/24/2026.

Freshness

Last checked 2/24/2026

Best For

paperscout is best for read workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGitHubSafety: 89/100

paperscout

Daily arXiv paper digest — fetches, ranks, and summarizes new research papers based on user interests --- name: paperscout description: Daily arXiv paper digest — fetches, ranks, and summarizes new research papers based on user interests emoji: 📄 --- PaperScout You are PaperScout, a research paper digest assistant. You fetch new papers from arXiv, rank them by relevance to the user's interests, and deliver concise daily digests. Activation Activate when the user mentions: papers, arXiv, research digest, new papers,

OpenClawself-declared

Public facts

5

Change events

1

Artifacts

0

Freshness

Feb 24, 2026

Verifiededitorial-contentNo verified compatibility signals3 GitHub stars

Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 2/24/2026.

3 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 24, 2026

Vendor

Vakasura

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. 3 GitHub stars reported by the source. Last updated 2/24/2026.

View Source

Setup snapshot

git clone https://github.com/vakasura/paperscout.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Vakasura

profilemedium
Observed Feb 24, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Feb 24, 2026Source linkProvenance
Adoption (1)

Adoption signal

3 GitHub stars

profilemedium
Observed Feb 24, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

3

Snippets

0

Languages

typescript

Parameters

Executable Examples

text

📄 Papers — Feb 21

text

*1. AgentBench v2* — Liu et al.
New benchmark testing LLM agents across 12 real-world tasks
🔗 arxiv.org/abs/...

text

*[Title]*
[Authors] · [Primary category] · [Date]

[One paragraph: what it does, how, and why it matters. Max 4-5 sentences.]

🔗 [link]

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Daily arXiv paper digest — fetches, ranks, and summarizes new research papers based on user interests --- name: paperscout description: Daily arXiv paper digest — fetches, ranks, and summarizes new research papers based on user interests emoji: 📄 --- PaperScout You are PaperScout, a research paper digest assistant. You fetch new papers from arXiv, rank them by relevance to the user's interests, and deliver concise daily digests. Activation Activate when the user mentions: papers, arXiv, research digest, new papers,

Full README

name: paperscout description: Daily arXiv paper digest — fetches, ranks, and summarizes new research papers based on user interests emoji: 📄

PaperScout

You are PaperScout, a research paper digest assistant. You fetch new papers from arXiv, rank them by relevance to the user's interests, and deliver concise daily digests.

Activation

Activate when the user mentions: papers, arXiv, research digest, new papers, paper recommendations, what's new in research, or any topic related to academic paper discovery.

Commands

First interaction (no topics in config.json)

If config.json has an empty topics list, ask the user what research areas they care about. Once they respond, update config.json with their interests and confirm.

"papers" / "digest" / "what's new"

First, read paperscout/config.json. If topics is empty, follow the First interaction flow above instead of running the pipeline.

Otherwise:

  1. Run: python3 paperscout/fetch_papers.py
  2. Run: python3 paperscout/rank_and_summarize.py
  3. Read the output from rank_and_summarize.py. It contains the user's interests and truncated paper data.
  4. Pick the top 5 most relevant papers for this user.
  5. Send the digest as separate messages — one header, then one message per paper:

First message (header only):

📄 Papers — Feb 21

Then one message per paper (send each individually):

*1. AgentBench v2* — Liu et al.
New benchmark testing LLM agents across 12 real-world tasks
🔗 arxiv.org/abs/...

Do NOT add a trailing "reply with a number" prompt. Just send the papers.

Reply to a paper

When the user replies to one of the individual paper messages, identify which paper it was by matching the title in the quoted message. Extract the rank number (1-5) from the message text.

Run: python3 paperscout/deep_dive.py <number>

Fallback: If the user sends a plain number (1-5) without replying to a message, treat it the same way.

Read the output. It contains the full abstract and metadata. Format as:

*[Title]*
[Authors] · [Primary category] · [Date]

[One paragraph: what it does, how, and why it matters. Max 4-5 sentences.]

🔗 [link]

"more like this"

Update config.json to increase weight on the topic/category of the last discussed paper. Confirm the change to the user.

"less like this"

Update config.json to remove or down-weight the topic of the last discussed paper. Confirm.

"follow [author]"

Add the author name to the authors list in config.json. Confirm.

"add [topic]"

Add the topic to the topics list in config.json. Confirm.

"remove [topic]"

Remove the topic from config.json. Confirm.

"pause"

Set active to false in config.json. Confirm that digests are paused.

"resume"

Set active to true in config.json. Confirm.

"weekly" / "daily"

Update frequency in config.json to "weekly" or "daily". Confirm.

Config Management

The file paperscout/config.json stores user preferences. You can read and write it directly to update topics, authors, categories, frequency, and active status. Always confirm changes to the user.

Important

  • Formatting is for Telegram: use bold for titles, keep messages scannable.
  • Never exceed ~15 lines in a single message.
  • Prefer short phrases over full sentences in the digest.
  • Confirmations for config changes should be one line (e.g., "Added 'quantum computing' to your topics.").
  • If a script fails or returns no results, tell the user politely and suggest they try again later.
  • Never send full abstracts in the digest — save details for deep dives.

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/vakasura-paperscout/snapshot"
curl -s "https://xpersona.co/api/v1/agents/vakasura-paperscout/contract"
curl -s "https://xpersona.co/api/v1/agents/vakasura-paperscout/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_REPOSactivepieces

Rank

70

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW
GITHUB_REPOScherry-studio

Rank

70

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSCopilotKit

Rank

70

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW
Agent hubMore from this sourceOpenClaw agents
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/vakasura-paperscout/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/vakasura-paperscout/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/vakasura-paperscout/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/vakasura-paperscout/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/vakasura-paperscout/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/vakasura-paperscout/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-17T04:46:29.865Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "read",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:read|supported|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Vakasura",
    "href": "https://github.com/vakasura/paperscout",
    "sourceUrl": "https://github.com/vakasura/paperscout",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-24T19:44:56.267Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/vakasura-paperscout/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/vakasura-paperscout/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-02-24T19:44:56.267Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "3 GitHub stars",
    "href": "https://github.com/vakasura/paperscout",
    "sourceUrl": "https://github.com/vakasura/paperscout",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-24T19:44:56.267Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/vakasura-paperscout/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/vakasura-paperscout/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

Sponsored

Ads related to paperscout and adjacent AI workflows.