campaign-manager
Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool skills.
npx skills add BioTender-max/awesome-bio-agent-skills --skill campaign-manager --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Campaign Manager ## Goal-oriented design ### From goal to pipeline When user says: "I need 10 good binders for EGFR" **Campaign Planning:** ``` Goal: 10 high-quality binders for EGFR ├── Achievable: Yes (standard target) ├── Recommended pipeline: rfdiffusion → proteinmpnn → colabfold → protein-qc ├── Estimated designs needed: 500 backbones (to get ~50 passing QC) ├── Estimated time: 8-12 hours total ├── Estimated cost: ~$60 (Modal GPU compute) └── Expected yield: ├── After backbone (500): 500 structures ├── After sequence (×8): 4,000 sequences ├── After validation: 4,000 predictions ├── After QC (~10-15%): 400-600 candidates └── After clustering: 10-20 diverse final designs ``` --- ## Complete pipeline generator ### Standard miniprotein binder campaign ```bash # Step 1: Fetch and prepare target (5 min) curl -o target.pdb "https://files.rcsb.org/download/{PDB_ID}.pdb" # Trim to binding region if needed # Step 2: Generate backbones (2-3h, ~$15) modal run modal_rfdiffusion.py \ --pdb target.pdb \ --contigs "A1-150/0 70-100" \ --hotspot "A45,A67,A89" \ --num-designs 500 # Checkpoint: ls output/*.pdb | wc -l # Should be 500 # Step 3: Design sequences (1-2h, ~$10) for f in output/*.pdb
- Goal-oriented design
- From goal to pipeline
- Complete pipeline generator
- Standard miniprotein binder campaign
- Campaign size recommendations
- Tool selection guide
- When to use each tool
- Target difficulty assessment
- Campaign health assessment
- Quick metrics check
- Interpreting results
- Cost estimation
- Per-tool costs (Modal)
- Campaign cost estimates
Step 1: Fetch and prepare target (5 min)
curl -o target.pdb "https://files.rcsb.org/download/{PDB_ID}.pdb"
Trim to binding region if needed
Step 2: Generate backbones (2-3h, ~$15)
modal run modal_rfdiffusion.py \
Step 3: Design sequences (1-2h, ~$10)
for f in output/*.pdb; do
modal run modal_proteinmpnn.py \
done
Step 4: Quick ESM2 filter (30 min, ~$5, optional)What does the campaign-manager skill do?
Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool skills.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill campaign-manager --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.