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agentic-paper-digest-skill
by
ModestyRichards
· GitHub ↗
· v1.0.2
· MIT-0
81
Downloads
0
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0
Active Installs
3
Versions
Install in OpenClaw
/install modesty-agentic-paper-digest-skill
Description
Fetches and summarizes recent papers from arXiv and Hugging Face, providing JSON digests and optional local API access for customizable research updates.
Usage Guidance
This skill appears to do what it claims (download a paper-digest pipeline and run it locally), but it does so by cloning/downloading code from a GitHub repo and running pip install on requirements. Before installing or running bootstrap.sh: (1) inspect the GitHub repository contents (requirements.txt, the paper_finder package, and any scripts) to ensure you trust the source; (2) prefer cloning a pinned release/tag rather than main branch if you can; (3) review requirements.txt for unusual packages or post-install scripts; (4) run the bootstrap and server inside an isolated environment (container or VM) and avoid putting a long-lived high-privilege API key in an uncontrolled .env — use a scoped/limited key if possible; (5) resolve the metadata mismatch (README vs bootstrap repo owner) — ask the publisher which upstream repo is authoritative. If you cannot validate the remote repo and dependencies, treat this skill as potentially risky and avoid running its install scripts on sensitive hosts.
Capability Analysis
Type: OpenClaw Skill
Name: modesty-agentic-paper-digest-skill
Version: 1.0.2
The skill acts as a wrapper that downloads and executes code from an external GitHub repository (matanle51/agentic_paper_digest) via scripts/bootstrap.sh. It installs arbitrary Python dependencies and uses SKILL.md to provide mandatory instructions that steer the AI agent to perform these downloads and configurations automatically. While this behavior is aligned with the stated purpose of installing a paper digest tool, the 'bootstrap' pattern of fetching and running remote artifacts represents a significant supply chain risk and potential for remote code execution if the external repository is compromised.
Capability Tags
Capability Assessment
Purpose & Capability
The skill's name and description (arXiv/Hugging Face paper digests) align with the declared runtime requirements: Python, network access, and an LLM API key (SKILLBOSS_API_KEY). Requesting an LLM API key is proportional for summarization. However, registry metadata/README references a different repo owner than the bootstrap scripts (README suggests ModestyRichards, scripts clone matanle51), which is an inconsistency worth investigating.
Instruction Scope
SKILL.md instructs the agent to fetch the repo, read config files (topics/settings/affiliations), ask the user about preferences, and run either a CLI or local API. Those steps are within the stated purpose. The agent is instructed to read and update local config files under PROJECT_DIR and to load a .env containing SKILLBOSS_API_KEY — this entails local file read/write but not unexpected for this skill.
Install Mechanism
There is no registry install spec, but included scripts (bootstrap.sh) will clone or download a ZIP from GitHub (main branch) and then pip install -r requirements.txt into a freshly created .venv. Downloading the repository and pip-installing remote dependencies writes code to disk and executes package installers — a higher-risk install pattern. The GitHub URL is a direct main-branch zip (not an immutable release), so contents could change between review and install.
Credentials
The only required credential is SKILLBOSS_API_KEY (mapped to LITELLM API variables), which is consistent with needing LLM access for summarization. The skill uses a local .env file to store this key. No unrelated credentials or broad host/system config paths are requested.
Persistence & Privilege
always is false and the skill does not request persistent global privileges. It creates files under PROJECT_DIR (virtualenv, sqlite DB at data/papers.sqlite3, .env), but does not modify other skills or global agent configuration. The skill can run autonomously per platform defaults (not in itself a flag).
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install modesty-agentic-paper-digest-skill - After installation, invoke the skill by name or use
/modesty-agentic-paper-digest-skill - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
- No files were changed in this release.
- Documentation and workflow details remain unchanged from the previous version.
- This version does not introduce new features or bug fixes.
v1.0.1
- Updated the SkillBoss API Hub endpoint from api.heybossai.com to api.skillboss.co in environment configuration instructions.
- No changes to core functionality or invocation commands.
- Minor edits to documentation for clarity.
v1.0.0
- Initial release of Agentic Paper Digest Skill.
- Fetch and summarize recent arXiv and Hugging Face papers via CLI or API.
- Flexible configuration of topics, sources, time window, categories, and result limits.
- JSON outputs suitable for chaining in agent workflows.
- Detailed setup instructions and proactive user guidance for optimal results.
Metadata
Frequently Asked Questions
What is agentic-paper-digest-skill?
Fetches and summarizes recent papers from arXiv and Hugging Face, providing JSON digests and optional local API access for customizable research updates. It is an AI Agent Skill for Claude Code / OpenClaw, with 81 downloads so far.
How do I install agentic-paper-digest-skill?
Run "/install modesty-agentic-paper-digest-skill" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is agentic-paper-digest-skill free?
Yes, agentic-paper-digest-skill is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does agentic-paper-digest-skill support?
agentic-paper-digest-skill is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created agentic-paper-digest-skill?
It is built and maintained by ModestyRichards (@modestyrichards); the current version is v1.0.2.
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