| name | aminer-daily-paper |
| description | Get personalized academic paper recommendations. Activate whenever the user asks for paper recommendations — explicit command (/aminer-dp) or natural language (e.g. 'recommend me papers on RAG', 'suggest recent papers on multimodal agents'). Workflow: extract topics / author / aminer_author_id from the input, invoke scripts/recommend.py, return results as Markdown. |
aminer-daily-paper
Personalized academic paper recommendation.
When to activate
Any time the user asks for paper recommendations:
- Explicit command:
/aminer-dp, /aminer-dp topics: RAG, multimodal agents
- Natural language:
recommend me recent papers on multimodal agents, suggest some papers on tool-use, I work on RAG, give me a few papers
Input parsing (done by the model)
Before calling the script, extract from the user input:
| Field | Description |
|---|
topics | Research topics, 1–3 closely related terms work best |
author_name | Scholar name |
author_org | Scholar institution (improves disambiguation) |
aminer_author_id | AMiner scholar ID (24-char hex) |
size | Number of papers, default 5, max 20 |
language_sort | zh or en, optional |
At least one of topics / author_name / aminer_author_id should be provided.
Call strategy
| Scenario | Strategy |
|---|
| Single topic or scholar | 1 call, size=5 |
| User specifies a number | 1 call, honor the number (max 20) |
| Multiple distinct topics | 1 call per topic group, size=3–5 each, ~15 papers total |
| Broad request with no topics | 1 call, size=5 |
Execution
The script reads .z-ai-config (JSON) following the z-ai-web-dev-sdk
convention, searching in this order:
./.z-ai-config (cwd)~/.z-ai-config (home)/etc/.z-ai-config (system)
Required fields: baseUrl, apiKey, token (JWT for X-Token).
Optional: chatId, userId.
python3 "{baseDir}/scripts/recommend.py" \
[--topic "multimodal agents"] \
[--topic "tool-use"] \
[--author-name "Jie Tang"] \
[--author-org "Tsinghua University"] \
[--aminer-author-id "696259801cb939bc391d3a37"] \
[--size 5] \
[--language-sort zh]
The script POSTs to ${baseUrl}/functions/invoke with headers
Authorization: Bearer ${apiKey}, X-Z-AI-From: Z, and X-Token: ${token}
(plus optional X-Chat-Id / X-User-Id), and prints the paper list as
Markdown.
Output handling
- On success, the script prints a Markdown paper list — forward it directly to the user.
- On non-zero exit, stderr contains the error — relay it concisely to the user.
Error handling
- API returns an error → relay the error; do not switch to another skill.
- No results → suggest the user broaden topics or adjust the query.
