# Campaign Manager

> >

- **Type:** Skill
- **Install:** `agentstack add skill-adaptyvbio-protein-design-skills-campaign-manager`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [adaptyvbio](https://agentstack.voostack.com/s/adaptyvbio)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [adaptyvbio](https://github.com/adaptyvbio)
- **Source:** https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/campaign-manager

## Install

```sh
agentstack add skill-adaptyvbio-protein-design-skills-campaign-manager
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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 → chai → 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)
# RFdiffusion runs from the official repo, not biomodals
python run_inference.py \
  inference.input_pdb=target.pdb \
  contigmap.contigs=[A1-150/0 70-100] \
  ppi.hotspot_res=[A45,A67,A89] \
  inference.num_designs=500

# Checkpoint: ls output/*.pdb | wc -l  # Should be 500

# Step 3: Design sequences (1-2h, ~$10)
for f in output/*.pdb; do
  modal run modal_ligandmpnn.py \
    --input-pdb "$f" \
    --params-str "--number_of_batches 8 --temperature 0.1"
done

# Checkpoint: grep -c "^>" output/seqs/*.fa  # Should be ~4000

# Step 4: Quick ESM2 filter (30 min, ~$5, optional)
modal run modal_esm2_predict_masked.py --input-faa output/all_seqs.fa
# Filter sequences with PLL  0.85, ipTM > 0.5, scRMSD  0.85).mean()
    iptm_pass = (df['ipTM'] > 0.50).mean()
    scrmsd_pass = (df['scRMSD']  0.85) & (df['ipTM'] > 0.5) & (df['scRMSD']  0.15:
        health = "EXCELLENT"
    elif all_pass > 0.10:
        health = "GOOD"
    elif all_pass > 0.05:
        health = "MARGINAL"
    else:
        health = "POOR"

    # Identify top issue
    issues = []
    if plddt_pass  15% | Proceed to selection |
| GOOD | 10-15% | Proceed, normal yield |
| MARGINAL | 5-10% | Review failure tree |
| POOR | < 5% | Diagnose and restart |

---

## Cost estimation

### Per-tool costs (Modal)

| Tool | GPU | $/hour | Typical Job | Cost |
|------|-----|--------|-------------|------|
| RFdiffusion | A10G | ~$1.20 | 500 designs/2h | ~$2.50 |
| ProteinMPNN | T4 | ~$0.60 | 4000 seq/1.5h | ~$1.00 |
| ESM2 (PLL) | A10G | ~$1.20 | 4000 seq/30min | ~$0.60 |
| AlphaFold | A100 | ~$4.50 | 4000 preds/4h | ~$18.00 |
| Chai | A100 | ~$4.50 | 500 preds/1h | ~$4.50 |

### Campaign cost estimates

| Campaign Size | Total Cost | Notes |
|---------------|------------|-------|
| Small (100 bb) | ~$15 | Quick exploration |
| Standard (500 bb) | ~$60 | Most campaigns |
| Large (1000 bb) | ~$120 | Comprehensive |
| XL (5000 bb) | ~$600 | Very thorough |

---

## Pipeline variants

### High-throughput (maximize diversity)

```bash
# More backbones, fewer sequences each (RFdiffusion from the official repo)
python run_inference.py inference.num_designs=2000
modal run modal_ligandmpnn.py --input-pdb bb.pdb --params-str "--number_of_batches 4 --temperature 0.2"
```

### High-quality (maximize per-design quality)

```bash
# Fewer backbones, more sequences each, lower temperature
python run_inference.py inference.num_designs=200
modal run modal_ligandmpnn.py --input-pdb bb.pdb --params-str "--number_of_batches 32 --temperature 0.1"
```

### Quick exploration (fast iteration)

```bash
# Small batch, ESMFold2 for fast single-sequence folding
# RFdiffusion runs from the official repo (not biomodals); see the rfdiffusion skill
modal run modal_ligandmpnn.py --input-pdb bb.pdb --params-str "--number_of_batches 8"
modal run modal_esmfold2.py --input-faa all_seqs.fa
```

---

## See also

- Tool-specific parameters: `rfdiffusion`, `proteinmpnn`, `mosaic`, `chai`, `boltz`, `alphafold`
- QC thresholds and filtering: `protein-qc`
- Tool selection guidance: `binder-design`

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [adaptyvbio](https://github.com/adaptyvbio)
- **Source:** [adaptyvbio/protein-design-skills](https://github.com/adaptyvbio/protein-design-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-adaptyvbio-protein-design-skills-campaign-manager
- Seller: https://agentstack.voostack.com/s/adaptyvbio
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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