Vector database retrieval and evidence-based answering for medical research topics. Use when users need knowledge-base-backed answers about methodology, disease mechanisms, drug effects, clinical research, or research tools. Input is a medical research question; output is a st...
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tessl review fix ./scientific-skills/Evidence Insight/medical-vector-search/SKILL.mdWhen users ask medical research questions, intelligently rewrite queries and call the vector knowledge base to retrieve the most relevant document segments, then provide evidence-backed answers. This ensures answers come from a reliable knowledge base rather than solely from model internal knowledge.
Before calling the API, rewrite the user's original question into a professional query suited for vector retrieval. The purpose is to improve recall, as colloquial questions often fail to match professional expressions in documents.
Rewriting principles:
Rewriting examples:
| User Original Question | Rewritten Search Query |
|---|---|
| What is the review workbench? | review workbench features usage methods |
| How to do meta-analysis? | meta-analysis systematic review methodology statistical analysis |
| What are CAR-T cell therapy side effects? | CAR-T cell therapy adverse reactions cytokine release syndrome neurotoxicity |
| I want to learn about CRISPR gene editing | CRISPR-Cas9 gene editing principles applications off-target effects |
The skill directory contains a pre-packaged search script scripts/search.py - call it directly:
python scripts/search.py "<rewritten query>"The script automatically outputs relevance scores, source titles, and document content segments.
You can also import its search() function directly in Python code:
from scripts.search import search
results = search("<rewritten query>")API parameters (fixed, no modification needed):
| Parameter | Value | Description |
| --- | --- |
| collection_alias | wiki_production | Knowledge base to search |
| search_method | hybrid_search | Hybrid retrieval (vector + keyword) |
| alpha | 0.7 | Vector weight 70% |
| score_threshold | 0.1 | Minimum relevance threshold |
| limit | 10 | Return max 10 results |
[1], [2] citation markers in the text (merge numbers when multiple passages cite the same document)helix_wiki_knowledge_name as link text and helix_wiki_node_url as URL:**References**
[1] [Protein-Protein Interaction Research - Basic Research Elements](https://helixwiki.newidea.pro/xxx)
[2] [Introduction to Research Design - Basic Scientific Research Elements and Logic](https://helixwiki.newidea.pro/yyy)URL comes from the child_chunks[0].properties.metadata.helix_wiki_node_url field in each API result.
| Field | Required | Format/Source | Example | If Missing |
|---|---|---|---|---|
| User task description | Yes | Text | Research question, writing goal, analysis objective | Stop and ask user to provide |
| Primary input material | Depends on task | Text, file path, ID, table, or literature | PMID, PDF, CSV, DOCX, keywords, etc. | Specify which material type is missing |
| Output preference | No | Text | Language, format, target journal, template | Use skill default format |
This skill accepts requests that match the documented purpose of medical-vector-search and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
medical-vector-searchonly handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
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