denniswei9898/paper-mentor/chem-paper-search
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Searches Semantic Scholar and the open web for chemistry / chemical engineering / materials science papers given a topic and keyword sets. Returns a triaged table with title, first author, year, citation count, PDF availability, and one-line summary. Used as a sub-skill by `paper-mentor` during Phase 2; can also be invoked directly. Triggers on phrases like "find papers on", "幫我找文獻", "search Semantic Scholar", "literature on X".
Install
npx vskill@latest install denniswei9898/paper-mentor/chem-paper-searchSKILL.md
chem-paper-search — Chemistry / Materials Paper Discovery
What this does
Given a research topic + 3-5 keyword sets, this skill returns a curated list of 10-20 papers, prioritizing those with open-access PDFs.
This is a sub-skill called by paper-mentor Phase 2. When invoked directly, it produces the same output as the literature search step.
Inputs (when called)
Workflow
Step 1: Semantic Scholar search (primary)
Use WebFetch to call Semantic Scholar API:
For each keyword set, run the API call and merge results. Deduplicate by title.
Step 2: WebSearch fallback
If Semantic Scholar API rate-limits (429) or returns <5 results:
First: retry with exponential backoff (10s, 30s, 60s) — most 429s clear within a minute.
If still failing, fall back to WebSearch:
Then WebFetch the Semantic Scholar pages to extract metadata.
⚠️ Known limitation: WebFetch-scraped metadata loses structured fields (citationCount, openAccessPdf, tldr). When fields are missing:
- Mark
cites = unknown,pdf = unknown,summary = unknown - Do NOT auto-filter these papers — show them all and let the user / orchestrator decide
- Note in output: "⚠️ Step 3 filter ran with reduced metadata; manual review recommended"
Step 3: Filter
Apply filters in order:
- ✅ Has openAccessPdf → PRIORITY
- ✅ citationCount > 20 (or > 5 for papers <2 years old)
- ✅ year >= year_min
- ❌ Predatory journal flag (use Beall's list — check journal name)
- ❌ Off-topic (manual review of abstract)
Step 4: Triage
Mark each paper as:
- 🔴 Must-read (必讀): Direct competitor, foundational, or very high citations
- 🟡 Reference (參考): Useful method or comparison data
- ⚪ Skippable (可略): Low rigor or off-topic borderline
Step 5: Output
Backup search engines
If Semantic Scholar is down or insufficient:
| Engine | URL pattern | When to use | |
|---|---|---|---|
| Elicit | https://elicit.com/search?q={query} | When you need AI-extracted research questions | |
| Connected Papers | https://www.connectedpapers.com/search?q={query} | When you want a citation graph view | |
| Google Scholar | (manual via WebSearch) | Last resort, low metadata quality |
Hard rules
- Never invent papers. If 0 results, say so explicitly.
- Always show searches that returned nothing — these are evidence of gaps.
- Always verify citation count with a second source if it seems high (>500).
- Filter out predatory journals — names ending in "International Journal of [Field] Research" with sketchy publishers should be excluded.
- Quote exact API responses when uncertain — do not paraphrase metadata.
Common issues
| Issue | Fix | |
|---|---|---|
| Semantic Scholar returns 429 | Wait 10s, retry. If persists, switch to WebSearch fallback. | |
| All papers are too old | Drop year_min by 2 years and retry; if still old, the field is dormant. | |
| No open PDFs | Switch search to include arXiv preprints (they have open PDF). | |
| Topic is too narrow | Loosen by removing 1-2 keywords; the field may not have studied it. | |
| Topic is too broad | Tighten by adding specific technique or material; expect 100+ raw results. |
Output downstream
This output feeds into:
- paper-mentor Phase 2 Step 3: passes the table to
chem-nlm-helperfor NotebookLM ingestion - Direct invocation: returned to user as-is for manual reading
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/chem-paper-search/SKILL.md
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