blatant-why

Name: blatant-why
Author: 001TMF

Verified

AI-powered biologics design campaign agent — multi-agent orchestration with BoltzGen, PXDesign, Protenix, and 200+ cloud tools. Antibodies, nanobodies, de novo binders, and beyond.

100stars

10forks

Python

Installation

# Add to your Claude Code skills
git clone https://github.com/001TMF/blatant-why

Getting Started

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Security ReportVerified

Last scanned: 7/25/2026

{
  "issues": [
    {
      "type": "npm-audit",
      "message": "esbuild: esbuild allows arbitrary file read when running the development server on Windows",
      "severity": "low"
    },
    {
      "file": "CLAUDE.md",
      "line": 63,
      "type": "dangerous-command",
      "message": "Dangerous command (disables permission prompts): \"bypassPermissions\"",
      "severity": "medium"
    },
    {
      "file": "README.md",
      "line": 40,
      "type": "remote-install",
      "message": "Install command (remote install script piped to a shell — review the source before running): \"curl -LsSf https://astral.sh/uv/install.sh \\| sh\"",
      "severity": "low"
    }
  ],
  "status": "PASSED",
  "scannedAt": "2026-07-25T06:20:20.809Z",
  "npmAuditRan": true,
  "pipAuditRan": true,
  "promptInjectionRan": true
}

README.md

Frequently Asked Questions

What is blatant-why?

blatant-why is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by 001TMF. AI-powered biologics design campaign agent — multi-agent orchestration with BoltzGen, PXDesign, Protenix, and 200+ cloud tools. Antibodies, nanobodies, de novo binders, and beyond. It has 100 GitHub stars.

Is blatant-why safe to use?

Yes. blatant-why passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install blatant-why?

Clone the repository with "git clone https://github.com/001TMF/blatant-why" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is blatant-why written in?

blatant-why is primarily written in Python. It is open-source under 001TMF on GitHub, so you can review or fork the full source.

Are there alternatives to blatant-why?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh blatant-why against similar tools.

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Related Skills

superpowers

by obra

An agentic skills framework & software development methodology that works.

234,966

SoloFlow claudekit

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.

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.

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

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

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

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.

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

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:

Copy .env.example to .env.

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

For HPC (RunPod, Modal, SLURM) — add your provider key and