001TMF/blatant-why

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

114Stars on the repository
58Mods indexed here, across every type
18d agoLast push, which is what freshness is scored on
MITLicence, which decides whether bodies are shown

boltzgen

01

001TMF/blatant-why

Skill Claude CodeCodex

BoltzGen is an all-atom diffusion model that generates antibody, nanobody, and de novo miniprotein backbones conditioned on a target structure and a set of binding (hotspot) residues. Sequences are assigned by AntiFold, then each design is independently refolded with Protenix to produce ipTM, pTM, pLDDT, ipSAE, and…

not rated 114 18d ago A 3 tokens original MIT

by-campaign-manager

02

001TMF/blatant-why

Skill Claude CodeCodex

Plan, execute, monitor, and assess protein/antibody design campaigns. This skill governs how to size a campaign, track run state, coordinate multi-run efforts, estimate cost and time on each compute target (local GPU, HPC, Tamarind), monitor progress, and evaluate campaign health.

not rated 114 18d ago A 4 tokens original MIT

001TMF/blatant-why

Skill Claude CodeCodex

Multi-round design campaigns generate scored designs at every iteration. This skill turns that scoring history into actionable parameter changes for the next round — training a lightweight Random Forest on the designs you already have, ranking which features actually discriminate good from bad, and proposing…

not rated 114 18d ago A 5 tokens original MIT

by-causal-reasoning

04

001TMF/blatant-why

Skill Claude CodeCodex

Most "AI scientist" demos fake reasoning by chaining LLM calls and calling the chain a hypothesis. This skill does the opposite: it constrains the LLM with structured evidence retrieved from the BY knowledge graph and forces every claim to cite an existing entity with a defined evidence tier. The output is a short…

not rated 114 18d ago A 6 tokens original MIT

by-database

05

001TMF/blatant-why

Skill Claude CodeCodex

Use the BY MCP database tools to query PDB, UniProt, and SAbDab for target characterization, antibody scaffold selection, and competition analysis.

not rated 114 18d ago A 3 tokens original MIT

by-deploy-compute

06

001TMF/blatant-why

Skill Claude CodeCodex

BY is a local-first agent. The default compute provider is "local" (see .by/config.json → compute.defaultprovider). This skill teaches you how to stand up each design and folding tool on hardware the user owns or pays for directly — a personal GPU workstation, a RunPod pod, a Modal app, or a generic SLURM/PBS cluster…

not rated 114 18d ago B 5 tokens original MIT

by-design-workflow

07

001TMF/blatant-why

Skill Claude CodeCodex

Skill "by-design-workflow" from 001TMF/blatant-why, covering by design workflow — master orchestration skill, when to use this skill, inputs, outputs and clarification questions.

not rated 114 18d ago A 4 tokens original MIT

by-display

08

001TMF/blatant-why

Skill Claude CodeCodex

Standard display formats for all campaign output. They use Unicode box-drawing characters and markdown that render natively in Claude Code's terminal. Never use ANSI escape codes in response text — they render as literal characters.

not rated 114 18d ago A 2 tokens original MIT

by-epitope-analysis

09

001TMF/blatant-why

Skill Claude CodeCodex

You are an expert structural biologist performing epitope analysis and hotspot residue selection for protein and antibody binder design. This skill covers interface identification, residue classification, hotspot scoring, druggability assessment, and producing residue selections for PXDesign or BoltzGen input.

not rated 114 18d ago A 6 tokens original MIT

001TMF/blatant-why

Skill Claude CodeCodex

A design campaign only learns when lab outcomes are joined back to the in-silico predictions that produced them. This skill is the wet-lab feedback loop closer: it ingests lab readouts (Adaptyv batch CSVs, internal ELISA plate-reader output, BLI/Octet kinetics, expression QC), joins them to the per-design feature…

not rated 114 18d ago A 4 tokens original MIT

001TMF/blatant-why

Skill Claude CodeCodex

Closing the design feedback loop requires understanding why designs fail, not just that they fail. This skill compares the distribution of every continuous feature between PASS and FAIL designs using non-parametric statistics, ranks features by discriminating power, and translates the result into concrete threshold or…

not rated 114 18d ago A 6 tokens original MIT

001TMF/blatant-why

Skill Claude CodeCodex

Picking the wrong design strategy on a novel target wastes a GPU day. This skill forces an explicit, structured debate before any compute is spent: three hypothesis agents propose competing strategies in parallel, then a reflection agent ranks them against a fixed rubric. The winner becomes the campaign config.

not rated 114 18d ago A 6 tokens original MIT

by-knowledge

13

001TMF/blatant-why

Skill Claude CodeCodex

Persistent structured memory that turns isolated campaigns into a learning system. Each campaign writes outcomes, top designs, and failure modes into a JSON-backed graph so the next campaign benefits from prior art without re-running compute.

not rated 114 18d ago A 3 tokens original MIT

by-research

14

001TMF/blatant-why

Skill Claude CodeCodex

Thorough target research before design prevents wasted compute and failed campaigns. This skill defines an 8-phase pipeline that retrieves, validates, and packages research findings with quality gates and anti-drift checkpoints at every stage.

not rated 114 18d ago A 3 tokens original MIT

by-scoring

15

001TMF/blatant-why

Skill Claude CodeCodex

Interpret and apply BY custom scoring metrics for protein and antibody design. This skill covers ipSAE (interface Predicted Structural Accuracy Error) — the primary custom metric that differentiates BY from generic structure prediction tools — along with ipTM, pLDDT, RMSD, liability scoring, and the BY composite…

not rated 114 18d ago A 3 tokens original MIT

by-screening

16

001TMF/blatant-why

Skill Claude CodeCodex

Comprehensive screening battery for evaluating protein binder and antibody designs produced by PXDesign and BoltzGen. This skill encodes all quality filters, scoring thresholds, liability checks, and developability assessments used to triage designs before experimental validation.

not rated 114 18d ago A 3 tokens original MIT

by-session

17

001TMF/blatant-why

Skill Claude CodeCodex

Session initialization and configuration for BY projects. This skill defines the full session-start sequence (banner, environment check, status display) and the first-run configuration questionnaire that writes .by/config.json. It is not optional — it runs every time a new session opens in a BY project directory.

not rated 114 18d ago A 2 tokens original MIT

protenix

18

001TMF/blatant-why

Skill Claude CodeCodex

Protenix v1 is an AF3-class structure prediction model (368M parameters) for proteins, complexes, and protein-ligand systems. This skill wraps the protenix CLI with a documented input spec, an input-validating Python entry point, and a multi-seed ensemble aggregator so that callers can drive predictions through…

not rated 114 18d ago A 3 tokens original MIT

pxdesign

19

001TMF/blatant-why

Skill Claude CodeCodex

You are an expert at designing de novo protein binders using PXDesign. This skill covers YAML config construction, CLI invocation, output parsing, and result interpretation. PXDesign achieves 17–82% pass rate for de novo binder design depending on target difficulty (see references/de-novo-strategy.md).

not rated 114 18d ago A 2 tokens original MIT

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