scientific-skills/Evidence Insights/encori-api/SKILL.md
Access ENCORI (StarBase) database for miRNA-target, RNA-RNA, and other regulatory data. Invoke when user asks to search ENCORI or retrieve regulatory interactions.
npx skillsauth add aipoch/medical-research-skills encori-apiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
4 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
scripts/encori_client.py plus 1 additional script(s).requests librarySee ## Usage above for related details.
cd "20260316/scientific-skills/Evidence Insight/encori-api"
python -m py_compile scripts/encori_client.py
python scripts/encori_client.py --help
Example run plan:
CONFIG block or documented parameters if the script uses fixed settings.python scripts/encori_client.py with the validated inputs.scripts/encori_client.py with additional helper scripts under scripts/.Run this minimal command first to verify the supported execution path:
python scripts/validate_skill.py --help
This skill allows you to query the ENCORI (The Encyclopedia of RNA Interactomes) database programmatically. It supports multiple endpoints for retrieving data on miRNA-target interactions, RNA-RNA networks, RBP-target interactions, and more.
The skill provides access to the following data modules via the encori_client.py script:
Run the python script .trae/skills/encori-api/scripts/encori_client.py with the appropriate subcommand and arguments.
Most endpoints support:
--assembly: Genome version (default: hg38)--geneType: Main gene type (default: mRNA)--cellType: Cell type (default: all)Get all miRNA data for PDCD4 in HeLa cells:
python .trae/skills/encori-api/scripts/encori_client.py miRNATarget --target PDCD4 --cellType HeLa
Get all miRNA cleavage data for TP53:
python .trae/skills/encori-api/scripts/encori_client.py degradomeRNA --target TP53
Get interaction networks of TP53-mRNA:
python .trae/skills/encori-api/scripts/encori_client.py RNARNA --RNA TP53
Get ceRNAs for a specific miRNA family:
python .trae/skills/encori-api/scripts/encori_client.py ceRNA --family "miR-10-5p"
Get data of all RBPs that bind to TP53 in HeLa cells:
python .trae/skills/encori-api/scripts/encori_client.py RBPTarget --target TP53 --cellType HeLa
Get RBP-MYC interactions in breast carcinoma:
python .trae/skills/encori-api/scripts/encori_client.py RBPDisease --tissue breast --disease carcinoma --target MYC
Retrieve binding motifs containing 'UGCAUG':
python .trae/skills/encori-api/scripts/encori_client.py RBPMotifScan --motif UGCAUG
Retrieve binding sites for a specific dataset ID:
python .trae/skills/encori-api/scripts/encori_client.py bindingSite --datasetID SBDH2131
encori_api_result.md unless the skill documentation defines a better convention.Run this minimal verification path before full execution when possible:
python scripts/encori_client.py --help
Expected output format:
Result file: encori_api_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
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.