scientific-skills/Evidence Insights/reagent-substitute-scout/SKILL.md
Find validated alternative reagents based on literature citation data.
npx skillsauth add aipoch/medical-research-skills reagent-substitute-scoutInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
See ## Features above for related details.
scripts/main.py.references/ for task-specific guidance.See ## Usage above for related details.
cd "20260318/scientific-skills/Evidence Insight/reagent-substitute-scout"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
CONFIG block or documented parameters if the script uses fixed settings.python scripts/main.py with the validated inputs.See ## Workflow above for related details.
scripts/main.py.references/ contains supporting rules, prompts, or checklists.Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
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
When specific reagents are discontinued or out of stock, find validated alternatives based on literature citation data.
This Skill analyzes reagent usage data from scientific literature to identify alternative reagents that have been repeatedly validated and widely cited, helping researchers quickly find reliable alternatives when the original reagent is unavailable.
# Query alternatives for a specific reagent
python skills/reagent-substitute-scout/scripts/main.py --reagent "TRIzol Reagent"
# Query by CAS number
python skills/reagent-substitute-scout/scripts/main.py --cas "15596-18-2"
# Query by molecular formula
python skills/reagent-substitute-scout/scripts/main.py --formula "C17H34N2O6P"
# Specify output format
python skills/reagent-substitute-scout/scripts/main.py --reagent "TRIzol" --format json
# Limit result count
python skills/reagent-substitute-scout/scripts/main.py --reagent "TRIzol" --limit 10
# Specify application field filter
python skills/reagent-substitute-scout/scripts/main.py --reagent "TRIzol" --field "RNA extraction"
# Include detailed literature citations
python skills/reagent-substitute-scout/scripts/main.py --reagent "TRIzol" --verbose
Configuration file path: ~/.config/reagent-substitute-scout/config.json
{
"data_sources": {
"pubmed": {
"enabled": true,
"api_key": "your_ncbi_api_key"
},
"google_scholar": {
"enabled": true,
"api_key": "your_scholar_api_key"
},
"chembl": {
"enabled": true
},
"pubchem": {
"enabled": true
}
},
"scoring": {
"citation_weight": 0.4,
"recency_weight": 0.3,
"similarity_weight": 0.3,
"min_citations": 5
},
"output": {
"default_format": "table",
"default_limit": 5
}
}
┌────────────────────────┬─────────────┬────────────┬──────────────┬─────────────┐
│ Substitute │ CAS │ Similarity │ Citation │ Reliability │
├────────────────────────┼─────────────┼────────────┼──────────────┼─────────────┤
│ QIAzol Lysis Reagent │ 104888-69-9 │ 0.92 │ 2,341 │ ★★★★★ │
│ TRI Reagent │ 93249-88-8 │ 0.89 │ 1,876 │ ★★★★★ │
│ RNAzol RT │ 105697-57-2 │ 0.85 │ 892 │ ★★★★☆ │
└────────────────────────┴─────────────┴────────────┴──────────────┴─────────────┘
{
"query": {
"reagent": "TRIzol Reagent",
"cas": "15596-18-2"
},
"results": [
{
"name": "QIAzol Lysis Reagent",
"cas": "104888-69-9",
"molecular_formula": "C17H34N2O6P",
"similarity_score": 0.92,
"citation_count": 2341,
"reliability_score": 4.8,
"validated_applications": ["RNA extraction", "tissue homogenization"],
"literature_evidence": [
{
"pmid": "30212345",
"title": "Comparison of RNA extraction methods",
"year": 2019,
"citation_count": 156
}
]
}
]
}
Alternative scoring is based on the following dimensions:
Total Score = Citation Score × 0.4 + Recency Score × 0.3 + Similarity Score × 0.3
Where:
- Citation Score = log(citation count of this alternative) / log(max citation count)
- Recency Score = Proportion of citations in the last 5 years
- Similarity Score = Chemical structure similarity + functional characteristic match
# Install dependencies
pip install -r skills/reagent-substitute-scout/requirements.txt
# Configure API keys
cp skills/reagent-substitute-scout/config.example.json ~/.config/reagent-substitute-scout/config.json
# Edit configuration file and fill in API keys
OpenClaw Skill Development
MIT
| 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 |
# Python dependencies
pip install -r requirements.txt
Every final response should make these items explicit when they are relevant:
scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.This skill accepts requests that match the documented purpose of reagent-substitute-scout 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:
reagent-substitute-scoutonly handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Use the following fixed structure for non-trivial requests:
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.
tools
Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
development
Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
tools
Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.
testing
Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.