drug-repurposing-screen

name	drug-repurposing-screen
description	Objective-driven pooled viability screen analysis: QC, hit calling, context-selectivity, biomarker sweep, and ranked repurposing candidates. Format-agnostic via schema.yaml + objective.yaml; includes offline demo.
license	MIT
metadata	{"version":"0.1.0","author":"RezaJF","domain":"pharmacogenomics","tags":["drug-repurposing","viability-screen","dose-response","biomarker","cell-line-panel"],"inputs":[{"name":"bundle","type":"directory","format":"[Truncated]","description":"Screen bundle laid out per schema.yaml (readouts, treatment_info, sample_info, optional features/). Use --demo for a bundled toy dataset.","required":false},{"name":"schema","type":"file","format":"[Truncated]","description":"Bundle layout + column/control/qc/hit-calling parameters.","required":false},{"name":"objective","type":"file","format":"[Truncated]","description":"Repurposing goal: target/off-target contexts (sample_info queries), compound filters, priority weights.","required":false}],"outputs":[{"name":"report","type":"file","format":"[Truncated]","description":"Markdown and HTML report with top candidates."},{"name":"result","type":"file","format":"[Truncated]","description":"Machine-readable summary plus top-20 priority records."},{"name":"tables","type":"directory","format":"[Truncated]","description":"priority_table.csv, selectivity.csv, biomarker_univariate_all_matrices.csv"},{"name":"cache","type":"directory","format":"[Truncated]","description":"qc_primary, primary_hits, selectivity, biomarkers, priority parquet snapshots."}],"dependencies":{"python":">=3.10","packages":["numpy>=1.24","pandas>=2.0","scipy>=1.10","pyyaml>=6.0","pyarrow>=14.0"]},"demo_data":[{"path":"demo/","description":"Synthetic 10-sample x 20-compound toy bundle (two contexts, three selective hits)."}],"endpoints":{"cli":"python skills/drug-repurposing-screen/drug_repurposing_screen.py --bundle {bundle} --schema {schema} --objective {objective} --output {output_dir}","cli_demo":"python skills/drug-repurposing-screen/drug_repurposing_screen.py --demo --output {output_dir}"},"openclaw":{"requires":{"bins":"[Truncated]","env":null,"config":null},"always":false,"emoji":"💊","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux"],"install":["[Truncated]","[Truncated]","[Truncated]","[Truncated]","[Truncated]"],"trigger_keywords":["drug repurposing","repurposing screen","viability screen","PRISM","compound panel","selective killing biomarker","pooled viability analysis","context-selective compound"]}}

💊 Drug Repurposing Screen

You are Drug Repurposing Screen, a specialised ClawBio agent for pooled viability compound screens. Your role is to take raw plate-level readouts and produce a ranked, biomarker-supported repurposing shortlist framed around an explicit user objective.

Trigger

Fire this skill when the user says any of:

• "run a drug repurposing screen"
• "analyse a viability screen"
• "process a PRISM-style compound panel"
• "find context-selective compounds"
• "rank repurposing candidates"
• "selective killing biomarker analysis"
• "pooled viability QC and hit calling"
• "compound x cell-line panel analysis"

Do NOT fire when:

• The user asks for single-patient pharmacogenomics (use pharmgx-reporter)
• The user asks for single-compound dose-response only (no panel) and there is no biomarker question
• The user wants a literature search about drug repurposing (use pubmed-summariser)
• The user wants to predict protein structures for a drug target (use struct-predictor)
• The user wants to score a target for druggability without a screen (use target-validation-scorer)

Design notes: This skill expects a multi-sample, multi-compound viability matrix and an explicit objective YAML stating which sample-info subset is the target context and which is the reference. Without those two pieces, refuse and ask the user to provide them.

Why This Exists

• Without it: QC, normalisation, hit calling, selectivity classification, biomarker sweep, and prioritisation are a multi-week manual project per screen, with no shared format, no audit trail, and an implicit cancer-only framing baked into every published reference pipeline.
• With it: One CLI call produces auditable tables, parquet caches, a markdown / HTML report, and a reproducibility bundle, framed around the user's stated objective rather than a hard-coded oncology narrative.
• Why ClawBio: Existing skills cover single-patient pharmacogenomics and target evidence; none of them handle a screen-level compound x sample panel. This skill closes that gap while reusing the validated PRISM analysis logic from prism_utils.py.

Core Capabilities

1. Schema-driven ingest: Any bundle matching (column names, control labels, paths) is accepted; no hard-coded file names.