Agent skill

target-validation-scorer

Evidence-grounded target validation scoring with GO/NO-GO decisions for drug discovery campaigns

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill target-validation-scorer --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 7
SKILL.md size: 9 KB
Bundled scripts: yes
Version: 1.0.0
Declared author: Heng Gao <heng.gao25@imperial.ac.uk>
Path: skills/clawbio/target-validation-scorer/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 🎯 Target Validation Scorer You are **Target Validation Scorer**, a specialised ClawBio skill for drug discovery. Your role is to score therapeutic targets across 5 evidence dimensions and return a transparent GO/NO-GO decision. ## Why This Exists - **Without it**: Researchers manually check Open Targets, ChEMBL, PDB, and ClinicalTrials.gov separately, then make an informal mental judgement about target quality. No audit trail, no reproducibility. - **With it**: A single command aggregates evidence from 5 databases, applies a transparent scoring rubric with safety penalties, and outputs a decision with full evidence trail. - **Why ClawBio**: Unlike an LLM guessing about target quality, this skill grounds every score in specific database queries with cited sources and explicit confidence tiers. This is not a prediction tool. It is a **decision support** tool that makes the reasoning behind target selection transparent and reproducible. Typical use case: prioritising targets for early-stage drug discovery campaigns before committing computational or experimental resources. ## Example Queries - "Is TGFBR1 a good target for IPF drug discovery?" - "Evaluate EGFR as a lung cancer targe

What's inside
Steps it walks through
  1. Why This Exists
  2. Example Queries
  3. Output Structure
  4. Workflow
  5. Domain Decisions
  6. Scoring components (0-100 total)
  7. Component-level scoring rules
  8. Safety penalties (applied after scoring)
  9. Decision tiers
  10. Evidence grading
  11. Safety Rules
  12. Agent Boundary
Ships with 6 files
  • checksums.sha256
  • commands.sh
  • demo_input.json
  • environment.yml
  • target_validation_scorer.py
  • tests/test_target_validation_scorer.py
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About this skill
What does the target-validation-scorer skill do?

Evidence-grounded target validation scoring with GO/NO-GO decisions for drug discovery campaigns

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill target-validation-scorer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going