asta-skill — Semantic Scholar via Ai2 Asta MCP 🔭

中文文档 | Asta MCP Overview | Request API Key

What it does

• Search the Semantic Scholar academic corpus by keyword, title, author, or full-text snippet
• Look up a paper from any ID (DOI, arXiv, PMID, PMCID, CorpusId, MAG, ACL, SHA, URL)
• Traverse citations — find who cited a given paper, with filtering and pagination
• Batch-lookup multiple papers in one call via get_paper_batch
• Snippet search — retrieve ~500-word passages from paper bodies for evidence grounding
• Author discovery — find researchers and list their publications
• Zero-code integration — the skill is a pure instruction pack; all I/O goes through the Asta MCP server
• Triggers automatically whenever the user asks for papers, citations, academic search, or literature discovery and Asta tools are registered

Multi-Platform Support

Works with any agent that speaks MCP and any host that loads Agent Skills:

Platform	Status	Details
Claude Code	✅ Full support	Native SKILL.md + claude mcp add registration
Codex	✅ Full support	MCP entry in ~/.codex/config.toml
Cursor / Windsurf / Hermes	✅ Full support	Standard mcpServers JSON block
opencode	✅ Full support	Native skills + MCP in ~/.config/opencode/opencode.json
OpenClaw/ClawHub	✅ Full support	metadata.openclaw namespace + MCP config
pi-mono	✅ Full support	metadata.pimo namespace
SkillsMP	✅ Indexed	GitHub topics configured
LM Studio	⚠️ Manual	MCP tools work (0.3.17+); skill instructions must be pasted into the system prompt

Comparison

vs. semanticscholar-skill (our REST-based sibling)

Capability	semanticscholar-skill	asta-skill
Transport	Python + direct REST (s2.py)	MCP (streamable HTTP)
Host requirement	Python + S2_API_KEY	Host with MCP support
Auth variable	S2_API_KEY	ASTA_API_KEY (via x-api-key)
Best for	Scripted batch workflows, custom filters	Zero-code agent integration
Works in Cursor / Windsurf out of the box	❌	✅

vs. no skill (native agent)

Feature	Native agent	This skill
Knows Asta endpoint & x-api-key header	❌	✅
Intent → tool decision table	❌	✅
Workflow patterns (discovery / seed expansion / author / evidence)	❌	✅
Warns against context-blowing fields=citations	❌	✅
Install recipes for every MCP host	❌	✅

Prerequisites

• An agent host with MCP support (Claude Code, Codex, Cursor, Windsurf, opencode, OpenClaw/ClawHub, pi-mono, etc.)

• An Asta API key — request here

  export ASTA_API_KEY=xxxxxxxxxxxxxxxx

MCP Server Registration

Register the Asta MCP server with your host before installing the skill.

Claude Code

claude mcp add -t http -s user asta https://asta-tools.allen.ai/mcp/v1 \
  -H "x-api-key: $ASTA_API_KEY"

Then restart Claude Code so the MCP tools load at session start.

Codex CLI

Edit ~/.codex/config.toml:

[mcp_servers.asta]
type = "http"
url = "https://asta-tools.allen.ai/mcp/v1"
headers = { "x-api-key" = "${ASTA_API_KEY}" }

Cursor / Windsurf / Hermes / other MCP clients

{
  "mcpServers": {
    "asta": {
      "serverUrl": "https://asta-tools.allen.ai/mcp/v1",
      "headers": { "x-api-key": "<YOUR_API_KEY>" }
    }
  }
}

LM Studio (manual mode)

LM Studio (0.3.17+) speaks MCP but does not auto-discover Agent Skills. Use it in two steps:

1. Register the MCP server — App Settings → Program → Integrations → edit mcp.json:

   {
     "mcpServers": {
       "asta": {
         "url": "https://asta-tools.allen.ai/mcp/v1",
         "headers": { "x-api-key": "YOUR_ASTA_API_KEY" }
       }
     }
   }

2. Paste the skill instructions — copy the body of SKILL.md into the chat's System Prompt so the model follows the intent routing and safe defaults.

Use a tool-calling-capable local model (e.g. Qwen2.5-Instruct, Llama 3.1 Instruct, Mistral Nemo, GPT-OSS). Plain chat models cannot invoke MCP tools.

Skill Installation

Claude Code

# Global (available in all projects)
git clone https://github.com/Agents365-ai/asta-skill.git ~/.claude/skills/asta-skill

# Project-level
git clone https://github.com/Agents365-ai/asta-skill.git .claude/skills/asta-skill

Codex

git clone https://github.com/Agents365-ai/asta-skill.git ~/.codex/skills/asta-skill

OpenClaw/ClawHub

git clone https://github.com/Agents365-ai/asta-skill.git ~/.openclaw/skills/asta-skill

# Project-level
git clone https://github.com/Agents365-ai/asta-skill.git skills/asta-skill

pi-mono

git clone https://github.com/Agents365-ai/asta-skill.git ~/.pimo/skills/asta-skill

SkillsMP

skills install asta-skill

Installation paths summary

Platform	Global path	Project path
Claude Code	~/.claude/skills/asta-skill/	.claude/skills/asta-skill/
Codex	~/.codex/skills/asta-skill/	N/A
OpenClaw/ClawHub	~/.openclaw/skills/asta-skill/	skills/asta-skill/
pi-mono	~/.pimo/skills/asta-skill/	—
SkillsMP	N/A (installed via CLI)	N/A

Usage

Just describe what you want:

> Use Asta to get the paper with DOI 10.48550/arXiv.1706.03762

> Search Asta for recent papers on mixture-of-experts at NeurIPS since 2023

> Who cited "Attention Is All You Need"? Show me the top 20 by citation count

> Find snippets in the Asta corpus that mention "flash attention latency"

> Look up Yann LeCun on Asta and list his 2024 papers

The skill picks the right Asta tool, attaches safe fields, and follows the documented workflow patterns.

Example: Search + Batch Download (chained with paper-fetch)

asta-skill only handles search and metadata; it does not download PDFs. To go from a search query to local PDFs, chain it with a paper-fetch skill (or any DOI-based downloader of your choice):

> Use Asta to find the 5 most cited papers on "single-cell ATAC-seq batch correction"
  since 2022, then hand the DOIs to paper-fetch to download all PDFs into ./papers/

What happens under the hood:

1. asta-skill → search_papers_by_relevance with publication_date_range="2022:" and fields=title,year,authors,venue,tldr,externalIds (note externalIds to expose DOI)
2. Agent extracts externalIds.DOI for each hit; falls back to externalIds.ArXiv when DOI is absent
3. paper-fetch → batch-resolves each DOI/arXiv ID through Unpaywall → arXiv → bioRxiv/medRxiv → PMC → SS → Sci-Hub fallback chain
4. PDFs land in ./papers/, one per paper

paper-fetch is a separate skill — install it if you need download capability. asta-skill itself stays scoped to the Semantic Scholar corpus.

Step 1 — Asta returns the top 5 papers with DOIs:

Step 2 — paper-fetch downloads all 5 PDFs into ./papers/:

Available Asta Tools

Tool	Purpose
get_paper	Single-paper lookup by any supported ID
get_paper_batch	Batch lookup of multiple IDs in one call
search_papers_by_relevance	Broad keyword search with venue + date filters
search_paper_by_title	Title-based lookup
get_citations	Paginated citation traversal
search_authors_by_name	Author profile search
get_author_papers	All papers by a given author
snippet_search	~500-word passages from paper bodies

Files

• SKILL.md — the only required file. Loaded by all hosts as the skill instructions.
• README.md — this file (English, displayed on GitHub homepage)
• README_CN.md — Chinese documentation

Verification

After registering the MCP server and restarting your host, ask:

"Use Asta to get the paper ARXIV:1706.03762 with fields title,year,authors,venue,tldr"

A successful call returns Attention Is All You Need, NeurIPS 2017, Vaswani et al., with TLDR.

FAQ

Why do I need this skill if Asta is already an MCP server?

The MCP server gives your agent raw tools (function names + parameter schemas). The skill gives your agent the expertise to use them well. Without the skill, the agent must figure everything out from scratch each session:

Layer	What it provides
MCP server	8 callable tools with input/output schemas
This skill	Intent routing, safe defaults, workflow patterns, pitfall warnings

Concretely, the skill adds:

1. Intent → tool mapping — which of the 8 tools to call for "find papers about X" vs. "who cited paper Y"
2. Context-overflow protection — warns agents to never request fields=citations (a single high-citation paper returns 200k+ characters)
3. Multi-step workflow patterns — topic discovery, seed-paper expansion, author deep-dive, evidence retrieval
4. Parallel batching guidance — prefer get_paper_batch over N sequential get_paper calls
5. Safe fields defaults — curated field list that prevents context blowups
6. Consistent output formatting — tables, counts, follow-up menus

Think of it like API documentation vs. the API itself: the schema tells the agent what's possible, the skill tells it what's wise.

Known Limitations

• fields=citations / fields=references blows up context — a single highly-cited paper returns 200k+ characters. Use the dedicated get_citations tool (which paginates) instead. The SKILL.md warns against this explicitly.
• API key required for production use — unauthenticated access hits strict rate limits fast
• Author disambiguation — common names collide; always inspect affiliations in search_authors_by_name before calling get_author_papers
• MCP loads at session start — if you register the server mid-session, restart your host to pick up the new tools
• Abstract availability — not every paper in the corpus has a full abstract; use snippet_search or tldr as fallback

Contributing

Suggestions, bug reports, and pull requests are all welcome! If you have ideas to improve this skill — new workflow patterns, better defaults, additional MCP host recipes, documentation fixes, or anything else — feel free to open an issue or submit a PR directly.

This skill is community-friendly: every contribution, no matter how small, helps make it better for everyone.

License

MIT

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Author

Agents365-ai

• Bilibili: https://space.bilibili.com/441831884
• GitHub: https://github.com/Agents365-ai