ersilia-skills

Integrate the code of a new ML model into an Ersilia model template repository. Use this skill whenever the user has: (1) a source model repository with the original ML code, (2) an ersilia-model-template repository (already forked/cloned), and (3) optionally a PDF of the scientific article — and needs to wire up the actual model code. The skill handles all coding steps: copying checkpoints with git-lfs tracking, adapting main.py to replace the molecular-weight placeholder with real inference, creating run_columns.csv, updating install.yml with pinned versions, and producing run_input.csv and run_output.csv by actually running the model. Trigger on phrases like "incorporate model code", "fill in the template", "adapt main.py", "add checkpoints", "create run_columns", "create install.yml for ersilia", "generate example files for ersilia model", or any request to wire a source model into the eos-template format.

Tests an Ersilia Model Hub model before hub incorporation. Runs `ersilia test --shallow` on a locally cloned model repository, reads the results, identifies failing checks, and proposes a debugging strategy to the user — but does not apply fixes automatically. Use this skill whenever a user wants to test, validate, or debug an Ersilia model before submitting it to the hub, mentions test failures, says "the test is failing", or is preparing a model for hub incorporation. Trigger even if the user just says "test the model" or "run ersilia test" in any Ersilia context.

Fills in the required metadata fields for an Ersilia Model Hub model, given the metadata.yml file and the original publication PDF. Reads the source code URL directly from the metadata file — no need to provide it separately. Use this skill whenever a user wants to populate, complete, or update a metadata.yml file for an Ersilia model, mentions an Ersilia model contribution, or is working on model metadata for the Ersilia Model Hub. Trigger even if the user just says "fill in the metadata" or "help me with the metadata.yml" in any Ersilia context.

Open a model request issue on ersilia-os/ersilia for a new model to be incorporated into the Ersilia Model Hub. Use this skill whenever a user wants to request a new model, open a model request, or start the model incorporation pipeline. The skill reads the publication PDF and source code repository to propose all required fields, asks the user to confirm, then opens the GitHub issue mimicking the official ersilia-os/ersilia model request form. Trigger on phrases like "request a model", "open a model request", "add a new model to the hub", "start model incorporation", or "open an issue for a new model".

Reproduce the key performance metrics reported in a paper for an Ersilia Model Hub model. Reads the publication PDF, extracts reported results (AUC, RMSE, accuracy, R², etc.), assesses whether each benchmark dataset is publicly available, acquires the datasets, runs the incorporated model, and compares reproduced vs reported metrics with tolerance-aware verdicts. Use this skill whenever a user wants to verify that an incorporated model performs as described in its paper, after running model-incorporation-code. Trigger on phrases like "reproduce the results", "check model performance", "validate the model against the paper", "does the model match what the paper reports", "benchmark the model", or any request to verify an incorporated Ersilia model against its publication.

Produce the weekly Ersilia literature digest. Primary mission: surface every model and dataset that could plausibly join the Ersilia Model Hub — across all six Hub task families (Property prediction, Activity prediction, Featurization, Projection, Similarity search, Generation), with antimicrobial / antipathogen drug discovery as the most-prioritised disease lens. Secondary mission: keep the team current on agentic AI for science, automation in chemistry, open-source drug discovery, and global health. Apply an explicit LMIC and decolonisation lens throughout. Use this skill whenever the user asks to prepare, run, or refresh the literature digest. Triggers include: "weekly literature digest", "literature digest for Ersilia", "/literature-digest", "lit digest this week", "what did we miss last week", "digest the literature". Always use this skill for digest requests even if the ask seems simple.

Conduct deep, structured literature research for Ersilia drug discovery work. Use this skill whenever a user asks to find, review, or summarise scientific papers about a disease, biological target, compound class, or AI/ML method in the context of drug discovery — especially for neglected tropical diseases (NTDs), antimicrobial resistance (AMR), or global health. Triggers include target names (e.g. Mtb, PfDHFR, TryR, InhA), disease names (e.g. malaria, leishmaniasis, tuberculosis, Chagas, schistosomiasis), model or method types (e.g. GNN, QSAR, ChemBERTa), and requests like "find papers on", "what does the literature say about", "summarise research on", "literature review for", or "what's been published about". Always use this skill for Ersilia-context literature work, even if the request is phrased casually.

Audit a set of candidate small molecules and turn them into an interactive, shareable visual report. Use this skill whenever the user has a table of compounds with computed features (activity, ADMET, drug-likeness, structural flags — from the Ersilia Model Hub or elsewhere) and wants to make sense of the columns, build a condensed curated deliverable (renaming, aggregating, filtering, dropping liabilities), and produce a self-contained HTML explorer with 2D structures, consensus badges, and optional per-compound SWOT one-liners. Trigger on "audit these molecules", "build a compound explorer", "make sense of this screening output", "prioritise these hits", or when the user provides a CSV with model-output columns and wants a report.

How to create Python plots using the stylia package — Ersilia's matplotlib wrapper for publication-ready figures. ALWAYS use this skill when the user says anything like "make a plot", "plot this", "plot the results", "visualize", "prepare a plotting function", "show me a chart", "can you plot", "add a figure", or any similar phrasing during a coding session. This includes scatter plots, line plots, bar charts, heatmaps, histograms, ROC curves, and any other chart type. Also trigger on requests to visualize data, compare values, show distributions, or create any kind of figure — even if the user does not mention stylia or matplotlib explicitly. Never generate matplotlib figures without stylia — always use stylia.create_figure() instead of plt.figure() or plt.subplots().

A minimal test skill to verify that the ersilia-skills repository and local setup (symlinks, git hook) are working correctly. Use this skill to confirm that skill loading, slash commands, and the setup.sh workflow are functioning as expected. Trigger on phrases like "run test skill", "check skill setup", or "verify ersilia skills".