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.
SkillScore91Maintained
Automatic
◆Research pack
Last verified 8/16/2026
What it changes
Passed 17 of 19 test cases with the skill, and 3 of 19 without it.
Without skill14%
With skill91%
Checks it passed that the model alone missed
Flags plate pairs below the default SSMD threshold of 1.5.
Identifies BRD-0003 as a top prioritised context-selective hit.
Identifies BRD-0015 as a top prioritised context-selective hit.
and 29 more in the full report
17 fixed3 passed either way2 still failing0 broke
Pass-rate lift
+77 pp
Tokens
+106%
Agent turns
0%
Response time
-54%
Model gemini-3.6-flashMeasured 8/16/2026v120 of 22 cases passed with skill · 19 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 16
Prompt
If an analysis pipeline run was interrupted or needs re-running without recomputing primary QC and hit calling from scratch, what CLI flag enables reusing existing parquet files?
What was checked, without and with the skill
Checked
Without skill
With skill
Uses the --resume flag to enable reading cached parquet files from previous runs.
Missed
Passed
case-21 · gemini-3.6-flash · 8/16/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md249 linessingle file
💊 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
Schema-driven ingest: Any bundle matching schema.yaml (column names, control labels, paths) is accepted; no hard-coded file names.
Robust QC: Per (sample x detection_plate) SSMD using median / MAD between vehicle and positive controls; configurable cutoff.
Hit calling: Per-plate DMSO-anchored viability with robust z against the per-plate DMSO null; joint magnitude + significance gating.
Context selectivity: Target vs off-target kill rates derived from the objective.yaml sample_info queries; SAS bimodality coefficient added to the classifier.
Biomarker sweep: Spearman associations across every features/*.csv matrix (expression, methylation, copy number, etc.) with BH-FDR.
Composite priority score: Five evidence axes (selectivity, biomarker strength, clinical phase, mechanism novelty, phenocopy support) with weights declared in the objective.