Target Novelty Scorer | Skills Pool
Target Novelty Scorer Score the novelty of biological targets through literature mining and.
openclaw 4,189 スター 2026/04/02 ID: 177
When to Use
Use this skill when the task needs Score the novelty of biological targets through literature mining and.
Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
Key Features
See ## Features above for related details.
Scope-focused workflow aligned to: Score the novelty of biological targets through literature mining and.
Packaged executable path(s): scripts/main.py.
Reference material available in references/ for task-specific guidance.
Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
Python 3.9+
requests
pandas
biopython (Entrez API)
クイックインストール
Target Novelty Scorer npx skillvault add openclaw/openclaw-skills-skills-aipoch-ai-target-novelty-scorer-1-skill-md
スター 4,189
更新日 2026/04/02
職業
numpy
Example Usage See ## Usage above for related details.
cd "20260318/scientific-skills/Evidence Insight/target-novelty-scorer"
python -m py_compile scripts/main.py
python scripts/main.py --help
Confirm the user input, output path, and any required config values.
Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
Run python scripts/main.py with the validated inputs.
Review the generated output and return the final artifact with any assumptions called out.
Implementation Details See ## Workflow above for related details.
Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
Primary implementation surface: scripts/main.py.
Reference guidance: references/ contains supporting rules, prompts, or checklists.
Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
Quick Check Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
python -m py_compile scripts/main.py
python scripts/main.py --help
Workflow
Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
Description Score the novelty of biological targets based on literature mining. By analyzing literature in academic databases such as PubMed and PubMed Central, assess the research popularity, uniqueness, and innovation potential of target molecules in the research field.
Features
🔬 Literature Retrieval : Automatically retrieve literature related to targets from PubMed and other databases
📊 Novelty Scoring : Calculate target novelty score based on multi-dimensional indicators (0-100)
📈 Trend Analysis : Analyze temporal trends in target research
🧬 Cross-validation : Verify current research status of targets by combining multiple databases
📝 Report Generation : Generate detailed novelty analysis reports
Scoring Criteria
Research Heat (0-25 points) : Number of related publications and citations in recent yearsUniqueness (0-25 points) : Distinction from known popular targetsResearch Depth (0-20 points) : Progress of preclinical/clinical researchCollaboration Network (0-15 points) : Diversity of research institutions/teamsTemporal Trend (0-15 points) : Research growth trends in recent years
Usage
Basic Usage cd /Users/z04030865/.openclaw/workspace/skills/target-novelty-scorer
python scripts/main.py --target "PD-L1"
Advanced Options python scripts/main.py \
--target "BRCA1" \
--db pubmed \
--years 10 \
--output report.json \
--format json
Parameters Parameter Type Default Description --targetstring required Target molecule name or gene symbol --dbstring pubmed Data source (pubmed, pmc, all) --yearsint 5 Analysis year range --outputstring stdout Output file path --formatstring text Output format (text, json, csv) --verboseflag false Verbose output
JSON Output {
"target": "PD-L1",
"novelty_score": 72.5,
"confidence": 0.85,
"breakdown": {
"research_heat": 18.5,
"uniqueness": 20.0,
"research_depth": 15.2,
"collaboration": 12.0,
"trend": 6.8
},
"metadata": {
"total_papers": 15234,
"recent_papers": 3421,
"clinical_trials": 89,
"analysis_date": "2026-02-06"
},
"interpretation": "This target has moderate novelty, with moderate research heat in recent years..."
}
API Requirements
NCBI API Key (for PubMed retrieval)
Optional: Europe PMC API
Installation pip install -r requirements.txt
License MIT License - Part of OpenClaw Bioinformatics Skills Collection
Risk Assessment Risk Indicator Assessment Level Code Execution Python scripts with tools High Network Access External API calls High File System Access Read/write data Medium Instruction Tampering Standard prompt guidelines Low Data Exposure Data handled securely Medium
Security Checklist
Prerequisites
# Python dependencies
pip install -r requirements.txt
Evaluation Criteria
Success Metrics
Test Cases
Basic Functionality : Standard input → Expected outputEdge Case : Invalid input → Graceful error handlingPerformance : Large dataset → Acceptable processing time
Lifecycle Status
Current Stage : DraftNext Review Date : 2026-03-06Known Issues : NonePlanned Improvements :
Performance optimization
Additional feature support
Output Requirements Every final response should make these items explicit when they are relevant:
Objective or requested deliverable
Inputs used and assumptions introduced
Workflow or decision path
Core result, recommendation, or artifact
Constraints, risks, caveats, or validation needs
Unresolved items and next-step checks
Error Handling
If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
Do not fabricate files, citations, data, search results, or execution outcomes.
This skill accepts requests that match the documented purpose of target-novelty-scorer 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:
target-novelty-scorer only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
References
Response Template Use the following fixed structure for non-trivial requests:
Objective
Inputs Received
Assumptions
Workflow
Deliverable
Risks and Limits
Next Checks
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.
02
Key Features