Z User
e45b68bb8b
Real IMAP connection via imapflow library: - Connects to IMAP server (IMAPFlow, TLS on port 993) - Fetches recent messages (configurable limit, default 20) - Extracts: from, fromEmail, subject, date, snippet (200 chars) - Thread-to-contact matching (PRD §6.1): 1. Match sender email against persons table 2. If no person match, extract domain and match against company.website 3. Returns matchedCompany + matchedPerson for each email - Audit-logged (action: crm.mailsync:imap) - Zod validated (host, port, user, password, folder, limit) - 502 on IMAP connection failure with detail - 400 on validation failure Tested: invalid creds → 502 'IMAP connection failed', missing host → 400
213 lines
9.3 KiB
Markdown
Executable File
213 lines
9.3 KiB
Markdown
Executable File
---
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name: aminer-daily-paper
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version: 1.1.2
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description: "Personalized academic paper recommendation via AMiner rec5 API. Activate this skill whenever the user asks for paper recommendations, whether triggered by /aminer-dp, /skill aminer-dp, or any natural language request such as 'recommend me papers on multimodal agents'. When invoked: extract topics/scholar signals from the input yourself, call handle_trigger.py with structured fields, then present the Markdown in `reply_text` to the user."
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user-invocable: true
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disable-model-invocation: false
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metadata:
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{
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"openclaw":
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{
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"emoji": "📚",
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"requires": {
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"bins": ["python3"],
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"env": ["AMINER_API_KEY"]
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},
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"primaryEnv": "AMINER_API_KEY"
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}
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}
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---
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# aminer-daily-paper
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Personalized paper recommendation via AMiner rec5 API. Token required: set `AMINER_API_KEY` env var.
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- Docs: https://open.aminer.cn/open/docs | Console: https://open.aminer.cn/open/board?tab=control
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**When to activate**: any time the user asks for paper recommendations — explicit command (`/aminer-dp ...`) or natural language (`recommend me papers on RAG`, `帮我推荐最近的多模态论文`).
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---
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## Pre-flight: Check Required Environment Variables
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**`AMINER_API_KEY`** — Always required. Check before calling the script:
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```bash
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[ -z "${AMINER_API_KEY+x}" ] && echo "AMINER_API_KEY missing" || echo "AMINER_API_KEY exists"
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```
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If missing, stop and tell the user:
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> `AMINER_API_KEY` is not set. Please obtain a token at https://open.aminer.cn and set it as an environment variable.
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No other environment variables are required.
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---
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## API Endpoint
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```
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POST https://publicapi.chatglm.cn/chatglm_public/skill/aminer/api/v3/paper/rec5
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Authorization: ${AMINER_API_KEY}
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Content-Type: application/json;charset=utf-8
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```
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### Request Fields
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| Field | Type | Required | Description |
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|-------|------|----------|-------------|
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| `author_name` | string | conditional | Scholar name (English). The backend resolves it to a scholar ID via person search. |
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| `author_org` | string | optional | Scholar institution (English full name). Required for disambiguation when the name is ambiguous. |
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| `topics` | string[] | conditional | Research topic phrases. **Use the user’s wording** (Chinese, English, or mixed). The API accepts multi-language topic strings. |
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| `size` | int | optional | Number of papers per call (1–20). Omit to let the model decide (see below). |
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| `offset` | int | optional | Pagination offset (0–100, default 0). |
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| `language_sort` | string | optional | `zh` or `en` **only when the user explicitly asks** for Chinese- or English-biased ranking (e.g. “优先中文论文” / “prefer English papers”). Otherwise omit; the request will not include this field. |
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At least one of `author_name` or `topics` should be provided. When none are given, the API returns personalized recommendations based on the account associated with `AMINER_API_KEY`.
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### Response Structure
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```json
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{
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"code": 200,
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"success": true,
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"data": [{
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"offset": 0,
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"size": 5,
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"total": 32,
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"papers": [{
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"paper_id": "...",
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"arxiv_id": "",
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"title": "...",
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"year": 2026,
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"authors": ["Author A", "Author B"],
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"keywords": ["kw1", "kw2"],
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"summary": "...",
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"structured_summary": {
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"research_problem": "...",
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"research_challenge": "...",
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"research_method": "...",
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"experimental_results": ""
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},
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"famous_authors": [],
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"aminer_author_profiles": [],
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"author_entries": [],
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"links": {
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"aminer": "https://www.aminer.cn/pub/{paper_id}",
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"arxiv": "",
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"pdf": ""
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},
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"paper_url": "https://www.aminer.cn/pub/{paper_id}",
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"source": "local_rec5"
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}]
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}]
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}
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```
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---
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## Input Formats
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Structured commands or plain natural language — both are valid.
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```
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/aminer-dp
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/aminer-dp topics: multimodal agents, tool-use
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/aminer-dp scholar: Jie Tang org: Tsinghua papers: OAG-Bench | RPC-Bench
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recommend me recent papers on RAG
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```
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`/aminer-dp` with no parameters calls the API with only the token — the API uses `AMINER_API_KEY` to identify the account and returns personalized recommendations.
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**Natural language input** — you (the model) must parse it into fields before calling the script. **Critical for `topics`:**
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1. **`topics` — do not “translate away” the user’s intent**
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- If the user already wrote `topics:` in the trigger (e.g. `具身智能`, `环境保护`), pass **those exact strings** into `handle_trigger.py`’s `--text`. **Do not** replace them with unrelated English terms (e.g. do **not** map arbitrary topics to “Knowledge Distillation”, “Smart agriculture”, or any other field the user did not ask for).
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- If you add English for retrieval, it must be a **faithful** alias of the same concept (e.g. 具身智能 → `embodied intelligence`, 环境保护 → `environmental protection`). When in doubt, **keep the user’s original words** and do not invent synonyms.
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- **Never** change the user’s topic into a different research area.
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2. **Scholars and institutions (person search still English-oriented)**
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- `author_name` / `author_org`: use commonly used **English** forms when resolving scholars (e.g. `Jie Tang`, `Tsinghua University`), expand well-known institution abbreviations to full official names, and add `author_org` when the name is ambiguous. If you cannot map a name safely, ask the user.
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3. **`language_sort`** — Put `language_sort: zh` or `language_sort: en` in the trigger **only if** the user clearly wants recommendations ranked with a **Chinese** or **English** preference. If they did not ask, **do not** add it (the API call omits `language_sort`).
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4. Decide `size` and whether to make multiple calls (see **Call Strategy**).
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5. Reconstruct the trigger, then call `handle_trigger.py`.
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Example (Chinese topics — **keep as-is**):
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- User: `/aminer-dp topics: 具身智能, 环境保护`
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- You call: `handle_trigger.py --text "/aminer-dp topics: 具身智能, 环境保护"`
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(Do **not** rewrite `topics` into unrelated English.)
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Example:
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- User: `/aminer-dp 我做多模态智能体和 tool-use，帮我推荐最近论文`
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- You extract: `topics: multimodal agents, tool-use`
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- You call: `handle_trigger.py --text "/aminer-dp topics: multimodal agents, tool-use size: 5"`
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Example (scholar):
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- User: `/aminer-dp 我是唐杰，清华大学，做多模态和知识图谱`
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- You extract: `scholar: Jie Tang, org: Tsinghua University, topics: multimodal, knowledge graph`
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- You call: `handle_trigger.py --text "/aminer-dp scholar: Jie Tang org: Tsinghua University topics: multimodal, knowledge graph"`
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Example (ambiguous name, ask user):
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- User: `/aminer-dp 推荐张伟方向的论文`
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- You: "张伟是一个常见名字，请提供机构信息以便精确匹配，例如：张伟，北京大学。或者直接提供 aminer_author_id。"
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**`papers` field**: representative paper titles (e.g. `papers: OAG-Bench | RPC-Bench`) accompany `scholar`/`author_name` for disambiguation context. They do not map directly to an API field.
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---
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## Call Strategy
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You decide `size` and whether to make multiple calls based on the input:
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| Scenario | Action |
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|----------|--------|
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| Single topic or scholar, casual request | 1 call, omit `size` (default 10) |
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| User explicitly asks for a number (e.g. "give me 5") | 1 call, honor the number (max 20) |
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| Multiple distinct topics (e.g. RAG + multimodal agents) | 1 call per topic group, `size: 5` each |
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| Broad open-ended request with no topics | 1 call, omit `size` (default 10) |
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**Multi-call rules:**
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- Call `handle_trigger.py` once per topic group, passing a focused `topics:` subset each time.
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- Keep each `topics:` list to 1–3 closely related terms for precision.
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- Make calls sequentially; present all results together after all calls finish.
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- Total papers across all calls should not exceed ~15 unless the user asks for more.
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---
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## Execution
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Only one supported entrypoint:
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```bash
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python3 "{baseDir}/scripts/handle_trigger.py" \
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--base-dir "{baseDir}" \
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--text "<trigger text with explicit fields>" \
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[--config /path/to/config.yaml]
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```
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- `--text`: reconstructed trigger with explicit fields (`topics:`, `scholar:`, etc.)
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- `--config`: optional path to a YAML config (defaults to `{baseDir}/config.yaml` when the file exists, via the runtime copy under `outputs/`)
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`handle_trigger.py` parses the fields, calls the rec5 API, and returns JSON including `reply_text` (Markdown) for you to show to the user.
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---
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## Contract
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- Every explicit invocation is a new run.
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- Do not answer with status-only text.
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- Do not search, install, or repair skills.
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- After running `handle_trigger.py`, check `final_response` in the JSON output:
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- `TEXT` — Normal path. Present `reply_text` (Markdown) to the user. Optional: you may still refine wording for the active channel; `prompts/enrich.md` is a reference for Chinese enrichment if you want richer copy.
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- Any error → report the `reply_text` (or error detail) to the user.
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**Note:** The skill only returns JSON with `reply_text`; it does not implement channel-specific sending.
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---
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## Error Handling
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- `AMINER_API_KEY` missing → stop, prompt user to set it.
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- No profile input → prompt user to provide topics, scholar name, or `aminer_author_id`.
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- API error → report the error stage; do not fall back to other skills.
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