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Claude Skills by 001TMF
github.com/001TMF21 skills0 installs40 views
- BoltzgenBoltzGen 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 CA-RMSD metrics. This skill teaches the exact **Write → Bash → Read** pattern for invoking BoltzGen via its CLI on local GPU (default), HPC (RunPod via `by-deploy-compute`), or Tam...Votes: 0GitHub stars: 117
- By Campaign ManagerPlan, 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. ---Votes: 0GitHub stars: 117
- By Campaign OptimizerMulti-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 thresholds and design counts that target the most promising regions of feature space. The optimizer is the **iteration loop**: it runs *after* the diagnosis step (`by-failure-diagnosis`) ...Votes: 0GitHub stars: 117
- By Causal ReasoningMost "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, ranked list of mechanistic hypotheses — each one falsifiable, each one traceable to a campaign, paper, or prior failure record. It sits between **statistical diagnosis** (which...Votes: 0GitHub stars: 117
- By DatabaseUse the BY MCP database tools to query PDB, UniProt, and SAbDab for target characterization, antibody scaffold selection, and competition analysis. ---Votes: 0GitHub stars: 117
- By Deploy ComputeBY is a **local-first** agent. The default compute provider is `"local"` (see `.by/config.json` → `compute.default_provider`). 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 — so campaigns run without depending on a managed cloud provider. Tamarind remains available as a cloud fallback when local compute is unavailable. It is never the...Votes: 0GitHub stars: 117
- By Design Workflow`by-design-workflow` is the **routing brain** of BY. It does not run any engine itself. Instead, it answers four questions before any GPU compute is committed: 1. **Which engine?** — Protenix (fold), PXDesign (de novo binder), or BoltzGen (antibody / nanobody / fallback binder). 2. **Which preset / protocol / tier?** — Preview vs Standard vs Production vs Exploratory; `nanobody-anything` vs `antibody-anything` vs `protein-anything`; PXDesign `preview` vs `extended`. 3. **Which compute target?...Votes: 0GitHub stars: 117
- By DisplayStandard 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. Every other BY skill (research, screening, scoring, campaign-manager, ...) calls into these templates when it is time to talk to the user. This file is the canonical source; the literal templates live in [`references/output-format-spec.md`](references/outp...Votes: 0GitHub stars: 117
- By Epitope AnalysisYou 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. ---Votes: 0GitHub stars: 117
- By Experiment ResultsA 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 table the screener emitted, and runs a calibration analysis that asks one question for every in-silico feature: > **Did this feature actually predict whether the design worked in the...Votes: 0GitHub stars: 117
- By Failure DiagnosisClosing 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 campaign-parameter changes for the next iteration. It is the bridge between **screening** (which produces PASS/FAIL labels) and **campaign optimization** (which adjusts ...Votes: 0GitHub stars: 117
- By Hypothesis DebatePicking 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. The pattern is borrowed from adversarial ML and red-team review — it works because three independently-constrained agents will surface failure modes that a single planner misses, a...Votes: 0GitHub stars: 117
- By KnowledgePersistent 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. The store is intentionally minimal: append-only JSON files, no server process, no embeddings — keyword overlap is enough for the scale we operate at (hundreds to low thousands of campaigns). Six MCP tools wrap the storage layer so every agent in the BY s...Votes: 0GitHub stars: 117
- By ResearchThorough 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.Votes: 0GitHub stars: 117
- By ScoringInterpret 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 ranking formula. ipSAE uses the open-source DunbrackLab formula (Dunbrack et al. 2025) with no proprietary dependencies. Use this skill whenever you need to score designs, interpret s...Votes: 0GitHub stars: 117
- By ScreeningComprehensive 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. Always run the full screening pipeline before presenting final candidates to the user. Never present unscreened designs as ready for validation. ---Votes: 0GitHub stars: 117
- By SessionSession 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. The session skill is the single source of truth for **which compute provider BY will use this session**. Every downstream skill (by-design-workflow, by-screening, the design en...Votes: 0GitHub stars: 117
- ProtenixProtenix 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 scripts instead of ad-hoc bash invocations. The default compute target is the **local GPU**. HPC (RunPod) is the second choice and Tamarind cloud is the fallback — set the target via...Votes: 0GitHub stars: 117
- PxdesignYou 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`). PXDesign is invoked from the command line via the `pxdesign` binary. This skill uses a strict **Write → Bash → Read** pattern: write a YAML config, run the CLI, read the `summary.csv`. Do not...Votes: 0GitHub stars: 117
- Meta Harness ProteusRun one iteration of proteus memory-summary evolution. Called by meta_harness.py.Votes: 0GitHub stars: 76
- Meta HarnessRun a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill...Votes: 0GitHub stars: 76