Open-source protein design agent for Claude Code

Commercial platforms wrap open-source tools behind paywalls and call it a revolution.
BY gives you direct access through Claude Code. No platform fees. Your tools, your compute, your designs.

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_{Source: trust us bro}

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Quick Start (5 minutes)

You don't need to be a developer. If you can open a terminal and paste commands, you can run BY.

1. Install prerequisites

Tool	Install	Check
Node.js 18+	nodejs.org	node --version
Python 3.11+	python.org	python3 --version
uv	curl -LsSf https://astral.sh/uv/install.sh | sh	uv --version
Claude Code	npm install -g @anthropic-ai/claude-code	claude --version

2. Create your project

mkdir my-campaign && cd my-campaign
npx blatant-why init

This scaffolds everything: 11 MCP servers, 21 agents, 19 skills, 13 slash commands, and a CLAUDE.md orchestration file. Takes about 30 seconds.

3. (Optional) Configure compute

BY defaults to local GPU if one is available. Otherwise the first-run questionnaire will help you pick between local, HPC (RunPod / Modal / SLURM), or Tamarind cloud. See Compute Options below.

cp .env.example .env
# Add keys for whichever compute provider you'll use

4. Start designing

claude

Then just tell it what you want:

> "Design VHH nanobodies against PD-L1"

Or use the guided workflow:

> /by:plan-campaign

Or if it's your first time:

> /by:welcome

That's it. Claude Code handles the rest -- research, design, screening, and ranking.

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What It Does

Give it a target protein. It researches across PDB, UniProt, and SAbDab. It plans a design campaign with statistical-strategy debate when the target is novel. It runs compute jobs on your local GPU (default), on your HPC (RunPod / Modal / SLURM via the by-deploy-compute skill), or on Tamarind Bio cloud. It screens every design for structural quality, sequence liabilities, and developability. It ranks candidates by composite score. When you submit them to the lab and the results come back, it ingests the CSVs, diagnoses which in-silico features predicted reality, and feeds the calibration back into the next round.

The whole pipeline runs inside Claude Code. No platform. No dashboard. No vendor lock-in.

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What's Inside

Component	Count	Description
MCP Servers	11	Biological databases, compute (local + HPC + cloud), screening, campaign state, knowledge store
Agents	21	Research, design, screening, evaluation, lab integration, prior-art, sequence/structure/epitope researchers
Skills	19	BoltzGen, Protenix, PXDesign, scoring, screening, campaign management, HPC deployment, wet-lab feedback, mechanistic reasoning
Slash Commands	13	Campaign control from the Claude Code prompt

MCP Servers (11)

Server	Role
pdb	Protein Data Bank queries
uniprot	UniProt protein annotation
sabdab	Structural Antibody Database
screening	Screening battery orchestration
tamarind	Tamarind Bio cloud compute
cloud	Cloud compute abstraction
adaptyv	Adaptyv Bio lab submission (gated)
campaign	Campaign state management
research	Literature and target research
local_compute	Local GPU compute dispatch
knowledge	JSON-backed campaign knowledge store

Agents (21)

Agent	Role
by-research	Target analysis, literature review, prior art (8-phase research pipeline)
by-prior-art-researcher	Prior-art deep dive for novel targets
by-sequence-researcher	Sequence-level analysis (orthologs, conservation, motifs)
by-structure-researcher	Structural analysis (PDB, AlphaFold, conformations)
by-epitope-researcher	Epitope-focused literature and structural research
by-research-synthesizer	Synthesize outputs from the research sub-agents
by-design	Generate designs via local, HPC, or cloud pipelines
by-screening	Score, filter, rank candidates
by-evaluator	Structural evaluation and quality assessment
by-visualization	Structure and results visualization
by-diversity	Sequence and structural diversity selection
by-campaign	Campaign lifecycle orchestration
by-knowledge	Learning system and campaign memory
by-verifier	Output verification and sanity checks
by-plan-checker	Campaign plan validation
by-environment	Environment setup and dependency checks
by-lab	Adaptyv Bio lab submission (triple-gated)
by-epitope	Epitope analysis and mapping
by-humanization	Antibody humanization engineering
by-liability-engineer	Sequence liability detection and fixes
by-formatter	Output formatting and reporting

Skills (19)

Skill	Category	Description
boltzgen	tool	BoltzGen antibody/nanobody generation
protenix	tool	Protenix structure prediction (AF3-class)
pxdesign	tool	PXDesign de novo binder design
by-design-workflow	orchestration	Tool routing + intent → preset matrix
by-campaign-manager	orchestration	Campaign state, checkpoints, cost model
by-research	research	8-phase research pipeline with confidence tiers
by-database	research	PDB / UniProt / SAbDab lookups
by-epitope-analysis	research	Hotspot scoring + interface classification
by-hypothesis-debate	strategy	3+1 agent topology for novel-target strategy selection
by-scoring	scoring	ipSAE algorithm + composite scoring
by-screening	filtering	Full screening battery, liability + developability rules
by-failure-diagnosis	analysis	Mann-Whitney U statistical failure analysis
by-experiment-results	analysis	NEW. Ingest lab CSV/Excel, diagnose in-silico vs lab divergence, close design → screen → lab → learn loop
by-causal-reasoning	analysis	NEW. Evidence-anchored mechanistic hypotheses from knowledge graph
by-campaign-optimizer	optimization	Active learning + RF feature importance
by-knowledge	persistence	Campaign knowledge graph (entities + relationships)
by-session	session	Session init, config questionnaire, resume protocol
by-display	display	Canonical output formats (banners, score bars, status tables)
by-deploy-compute	deployment	NEW. Deploy Protenix / BoltzGen / PXDesign on local GPU, RunPod, Modal, or SLURM

See templates/.claude/skills/README.md for the canonical terminology table and full skill-linkage map.

Slash Commands (11)

Command	Action
/by:load	Load a campaign from file
/by:screen	Run screening battery on designs
/by:results	Display campaign results table
/by:watch	Live-watch running compute jobs
/by:status	Campaign status dashboard
/by:approve-lab	Approve Adaptyv Bio submission (gated)
/by:set-profile	Switch compute profile
/by:setup	Initialize environment and dependencies
/by:plan-campaign	Generate a detailed campaign plan
/by:welcome	Show welcome message and quick-start guide
/by:resume	Resume an interrupted or paused campaign

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Setup

API Keys

Key	Required?	Where to get it	What it enables
RUNPOD_API_KEY	Optional	runpod.io	On-demand HPC GPU pods (~$0.40–$2.50/hr depending on GPU). Used by the by-deploy-compute skill.
TAMARIND_API_KEY	Optional	tamarind.bio (free account)	Cloud compute fallback — BoltzGen, Protenix, 200+ models. Free tier: 10 jobs/month
ADAPTYV_API_TOKEN	Optional	adaptyvbio.com	Lab testing submission (triple-gated)

Claude Code handles its own authentication. No separate Anthropic API key needed.

No keys needed for local-GPU mode — if you have an NVIDIA card with enough VRAM, BY can run the whole pipeline without any cloud service.

Configure your environment

After npx blatant-why init:

1. Copy .env.example to .env.
2. For local GPU (default) — set the tool paths:

   PROTEUS_FOLD_DIR=/path/to/Protenix
   PROTEUS_PROT_DIR=/path/to/PXDesign
   PROTEUS_AB_DIR=/path/to/boltzgen
   

3. For HPC (RunPod, Modal, SLURM) — add your provider key and let the by-deploy-compute skill handle deployment:

   RUNPOD_API_KEY=your_key_here   # or modal token, etc.
   

4. For Tamarind cloud fallback — add the API key:

   TAMARIND_API_KEY=your_key_here
   

5. For SSH remotes — add host configs to .by/config.json (compute.ssh_hosts).

The first-run questionnaire (run by the by-session skill on session open) walks through this interactively. Default compute.default_provider is "local"; priority order is ["local", "hpc", "tamarind"].

Compute Options

BY defaults to local GPU. The by-deploy-compute skill knows how to deploy Protenix / BoltzGen / PXDesign (and supplementary tools like AlphaFold, RFAntibody, ImmuneBuilder, ThermoMPNN, Boltz-2) on any of the targets below.

Provider	Cost	Setup	Best for
Local GPU (default)	Your hardware	Install tools + set env vars	Power users with GPUs
RunPod	~$0.40–$2.50/hr GPU pods	RUNPOD_API_KEY + by-deploy-compute	On-demand HPC bursts
Modal	Serverless, free tier	Modal token + by-deploy-compute	Cold-start-tolerant batch jobs
SSH Remote	Your infrastructure	Configure in .by/config.json	HPC clusters, in-house GPUs
Tamarind Bio	Free tier: 10 jobs/month	TAMARIND_API_KEY	Cloud fallback when no local GPU

Setting up Tamarind Bio (recommended -- no GPU needed)

1. Create a free account at tamarind.bio
2. Go to Settings → API Keys → Generate new key
3. Copy the key and add to .env:

   TAMARIND_API_KEY=your-key-here
   

4. That's it. BY will use Tamarind for all compute jobs.

Free tier: 10 jobs/month. Enough for a preview campaign (~5-10 designs). Paid tiers: Contact Tamarind for production pricing.

Tamarind provides access to 200+ structural biology tools including:

• BoltzGen -- antibody/nanobody design (Boltzmann generator diffusion)
• Protenix v1 -- AlphaFold3-class structure prediction (368M params)
• PXDesign -- de novo protein binder design (17-82% hit rates)
• TAP/TNP -- developability profiling
• AbLang2 -- humanness scoring

Setting up local GPU compute

Requires an NVIDIA GPU with CUDA support. Install the tools you need:

Protenix (structure prediction):

git clone https://github.com/bytedance/protenix
cd protenix && pip install -e .

Add to .env:

PROTEUS_FOLD_DIR=/path/to/protenix

PXDesign (de novo binder design):

# Follow PXDesign installation guide

Add to .env:

PROTEUS_PROT_DIR=/path/to/pxdesign

BoltzGen (antibody/nanobody design):

git clone https://github.com/jostorge/boltzgen
cd boltzgen && pip install -e .

Add to .env:

PROTEUS_AB_DIR=/path/to/boltzgen

BY will detect these automatically and offer local compute as an option.

Setting up SSH remote compute (Lambda.ai, RunPod, HPC)

For cloud GPU instances or HPC clusters:

1. Ensure SSH key-based authentication is set up
2. Add host configuration to .by/config.json:

   {
     "compute": {
       "ssh_hosts": [
         {
           "name": "lambda-gpu",
           "host": "your-instance.cloud.lambdalabs.com",
           "user": "ubuntu",
           "key": "~/.ssh/lambda_key",
           "gpu": "A100",
           "tools": ["protenix", "boltzgen"]
         }
       ]
     }
   }

3. BY will detect SSH hosts and offer them as compute options.

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Your First Campaign

After setup, here's what to expect when you run your first design campaign.

Start Claude Code:

claude

Tell it what you want:

> "Design VHH nanobodies against PD-L1"

What happens next:

1. Research (~1-2 min) -- BY searches PDB, UniProt, and SAbDab for your target. It pulls crystal structures, known binders, epitope data, and literature context.
2. Campaign plan (~30 sec) -- You get a plan showing how many designs will be generated, which models will be used, and estimated compute cost. You approve before anything runs.
3. Design generation (~5-15 min) -- Compute jobs run on Tamarind Bio (or your local GPU). BY monitors progress and reports back.
4. Screening (~2-5 min) -- Every design is scored for structural quality (ipSAE), binding confidence (ipTM), and sequence liabilities. Problem candidates are flagged.
5. Ranking -- You get a ranked table of candidates with composite scores, ready for lab ordering.

Typical first campaign: 5-10 nanobody designs, ~20 minutes end-to-end, zero GPU required (Tamarind free tier).

You can also use slash commands for more control:

Command	What it does
/by:welcome	Guided walkthrough for first-time users
/by:plan-campaign	Generate and review a campaign plan before running
/by:status	Check progress on a running campaign
/by:results	View ranked results table

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Architecture

flowchart TB
    User([User]) -->|prompt| Claude[Claude Code + CLAUDE.md]

    Claude -->|delegates| Agents[21 Agents]
    Claude -->|invokes| Skills[19 Skills]
    Claude -->|slash cmds| Commands[13 Commands]

    Agents --> MCP[11 MCP Servers]
    Skills --> MCP

    subgraph Data["Biological Databases"]
        PDB[(PDB)]
        UniProt[(UniProt)]
        SAbDab[(SAbDab)]
    end

    subgraph Compute["Compute (local-first)"]
        LocalGPU["Local GPU -- Default"]
        HPC["HPC: RunPod / Modal / SLURM"]
        Tamarind["Tamarind Bio -- Cloud Fallback"]
    end

    subgraph Models["Models"]
        BoltzGen["BoltzGen -- Ab/Nb Design"]
        Protenix["Protenix v1 -- Structure Prediction"]
        PXDesign["PXDesign -- Binder Design"]
    end

    subgraph Screening["Screening"]
        ipSAE["ipSAE Scoring"]
        Liabilities["Liability Scan"]
        Developability["Developability"]
        Diversity["Diversity Selection"]
    end

    subgraph Lab["Lab Integration"]
        Adaptyv["Adaptyv Bio -- Triple-Gated"]
    end

    MCP --> Data
    MCP --> Compute
    Compute --> Models
    MCP --> Screening
    MCP --> Lab

    Knowledge[("Knowledge Store")] <--> MCP

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Model Profiles

Model	Type	What It Does
Protenix v1	Structure prediction (368M params)	AlphaFold3-class folding -- protein, nucleic acid, ligand
PXDesign	De novo binder design	17-82% hit rates on published benchmarks
BoltzGen	Antibody/nanobody design	Boltzmann generator + Protenix confidence scoring

Learning System

Every campaign writes results to a JSON knowledge store. The by-knowledge skill provides structured queries (entity types, relationships) over past campaigns, so the system learns which design strategies work for which target classes.

Stored per campaign:

• Target metadata and research context (with HIGH / MEDIUM / SPECULATIVE confidence tiers)
• Design parameters and compute profiles
• Screening results and composite scores
• Lab outcomes (via by-experiment-results — see below)
• Validated / contradicted / inconclusive findings

Over time, the agent develops institutional memory about what works.

Design → Screen → Lab → Learn loop

BY closes the loop between in-silico predictions and wet-lab reality:

1. by-screening flags candidates as PASS in silico.
2. You submit them to the lab (Adaptyv Bio or your own).
3. Lab results arrive as CSV/Excel.
4. by-experiment-results ingests the readouts, joins with the original in-silico features, and runs Mann-Whitney U stratified by REAL lab outcome — identifying which in-silico features actually predicted reality and which didn't.
5. The calibration report goes to by-campaign-optimizer (which retrains the active-learning model) and to by-knowledge (storing validated/contradicted findings with confidence tiers).
6. by-causal-reasoning produces evidence-anchored mechanistic hypotheses for any persistent failure pattern, ranked with HIGH / MEDIUM / SPECULATIVE confidence and falsifiable predictions. Confidence is assigned mechanically by an evidence-precedence table — the LLM only fills the claim and prediction slots, not the confidence scoring.

This is what separates a designer from a scientist.

Repository Structure

blatant-why/
├── assets/                  # Banner and diagrams
├── src/
│   ├── init-cli/            # npx blatant-why init CLI
│   └── proteus_cli/         # Python CLI (scoring, screening, campaign)
├── templates/               # Deployed by init CLI
│   └── .claude/
│       ├── agents/          # 21 specialized agents
│       ├── commands/by/     # 13 slash commands
│       ├── skills/          # 19 skills (see skills/README.md for catalog)
│       └── mcp_servers/     # 11 MCP server implementations
├── tests/                   # Test suite
├── CLAUDE.md                # Agent orchestration rules
├── package.json             # npm package
├── pyproject.toml           # Python package (uv)
└── README.md

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Credits

Built by Tristan Farmer

• Hannes Stark and the MIT team for BoltzGen
• Deniz Kavi and Sherry Liu at Tamarind Bio
• Julian Englert at Adaptyv Bio
• The Claude Code team

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License

MIT