MuJoCo GPU and TPU batch simulation with MJX (mujoco.mjx in JAX) and MJWarp (mujoco_warp in CUDA): parallel rollouts (jax.vmap), trajectory scan (jax.lax.scan), differentiability, device memory (put_model, put_data), static contact sizing (max_contact_points, max_geom_pairs, nconmax, njmax), float32 precision, XLA compilation caching. Use only for MuJoCo MJX or MJWarp GPU/TPU batch rollouts, RL environments, and differentiable MuJoCo physics. Don't use for custom non-MuJoCo JAX or CUDA Warp simulations (e.g. custom 2D games, molecular dynamics, orbital mechanics, DEM granular solvers, or shallow-water PDEs that do not import `mujoco.mjx` or `mujoco_warp`), CPU simulation (mujoco-python), cross-engine parity debugging (mujoco-parity), rendering (mujoco-rendering), or GUI (mujoco-gui).
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Dear ImGui and ImPlot user interface design for MuJoCo Studio and MuJoCo viewer plugins (C++ ux/ components and Python ViewerPlugin build_gui panels): immediate-mode widget layout, multi-row widget/column alignment (Tables API), tooltip length and aspect ratio, build_gui vs render separation, ID stack rules (## label disambiguation vs ### seed reset override), list virtualization (ImGuiListClipper), theming, and input capture (WantCaptureKeyboard). Use only when building, aligning, styling, or debugging Dear ImGui widgets and ImPlot charts inside MuJoCo Studio, MuJoCo viewer plugins, or MuJoCo/robotics simulation and control tools. Don't use for general-purpose C++ or Python Dear ImGui / ImPlot / imgui_bundle desktop applications unrelated to MuJoCo or robotics, or for Studio plugin lifecycle/messaging (use mujoco-studio) or core physics stepping (use mujoco-python).
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Cross-engine physics parity comparison between MuJoCo (CPU, float64) and accelerated backends (MJWarp CUDA, MJX JAX, float32): stage-by-stage mj_forward vs mjw.forward isolation, normalized tolerances, stiff affine actuator and ill-conditioned mass matrix (qM) float32 bounds, actuation-conditioned forward passes, order-insensitive contact matching per geom pair, includemargin contact filtering, batched single-step evaluation, and float32-perturbed CPU rollouts. Use only when comparing MuJoCo CPU physics against MuJoCo MJWarp or MJX backends, or debugging MuJoCo sim-to-sim trajectory divergence, missing contacts, or force/qacc discrepancies. Don't use for float64-vs-float32 parity testing of non-MuJoCo C/C++/CUDA/JAX physics or PDE solvers, general GPU batch setup (mujoco-accelerated), or CPU-only simulation (mujoco-python).
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MuJoCo Python (`import mujoco`) simulation lifecycle (MjSpec, MjModel, MjData), stepping (mj_step, mj_step1, mj_step2, mj_forward), named access (data.body, model.geom), bind(), multi-threaded CPU batch simulation (mujoco.batch.Batch, mujoco.rollout), optimization and system ID (mujoco.minimize, mujoco.sysid, mjd_transitionFD), contact/touch sensors, sensordata, keyframe resets, integrators, mju_ spatial math (quaternions, poses), and energy diagnostics. Use only for MuJoCo Python physics simulation, MjModel/MjData state access, MuJoCo CPU batching, stepping loops, and MuJoCo sensors/sysid. Don't use for custom non-MuJoCo Python/NumPy/C++ ODE or rigid-body simulators, custom finite-difference sysid, or general spatial math without `mujoco`, nor for spec editing (mujoco-spec-editing), rendering (mujoco-rendering), GPU/TPU (mujoco-accelerated), parity (mujoco-parity), or GUI/viewers (mujoco-gui, mujoco-studio).
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MuJoCo offscreen image rendering using mujoco.Renderer in Python and the MuJoCo C/C++ rendering pipeline (mjrContext, mjvScene, mjr_render, mjr_readPixels): RGB, metric depth maps, segmentation masks (mjtObj), MJCF offwidth/offheight framebuffer sizing, MUJOCO_GL (osmesa, egl), MuJoCo cameras (fixed, tracking, free, MjvCamera), and mjvOption flags/geomgroup. Use only for headless camera rendering, dataset recording, and visual observations in MuJoCo simulations. Don't use for generic OpenGL/EGL/OSMesa offscreen rendering, glReadPixels depth/segmentation post-processing, or synthetic dataset pipelines that do not use the `mujoco` library, or for interactive viewers/Studio (mujoco-studio) or ImGui controls (mujoco-gui).
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Builds and launches interactive MuJoCo Studio viewers and plugins in Python and C++, covering decoupled sim/viewer threads, Snapshot and Event messaging, lifecycle events, WebViewer browser streaming, and Python hot reload. Use only when launching MuJoCo Studio viewers (mujoco_studio.launch_web, launch_native, launch_passive, LaunchStudio) for a MuJoCo MjModel/MjData/MjSpec simulation, authoring MuJoCo Studio Python ViewerPlugin/SimPlugin or C++ RegisterPlugin (mjPLUGIN_LIB_INIT) extensions, synchronizing MuJoCo sim/viewer state (ViewerHandle.sync, @messages.handler), hot-reloading plugins/models (HotReloadEvent), configuring WebViewer WASM streaming, or debugging Studio logs. Don't use for non-MuJoCo simulations or generic web viewers/sidecars not using MuJoCo, Dear ImGui/ImPlot layout (mujoco-gui), core stepping (mujoco-python), or headless rendering (mujoco-rendering).
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Acme includes a number of pre-built agents listed below. All agents can be used to run synchronous single-threaded or distributed experiments. Distributed experiments are using Launchpad and can be executed either on a single machine (--lp_launch_type=[local_mt|local_mp] command line flag for multi-threaded or multi-process execution) or multi machine setup on GCP (--lp_launch_type=vertex_ai). For details please refer to Launchpad documentation. We've listed the agents below in separate sections based on their different use cases, however these distinction are often subtle. For more…
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Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
3 projects use a version of this
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Constructs deep-links and URLs for the AlphaGenome Atlas website. Supports generating single-variant exploration links (1-based VCF-style coordinates), genomic locus views (1-based closed chr:start-end), candidate summary tables, and AlphaGenome reference vs. alternate predictions. Use when visualizing, exploring, charting, or linking genetic variants and genomic loci on the AlphaGenome Atlas, or when asked to inspect, view, or link predictions for a genomic variant. Don't use for computing raw AlphaGenome variant predictions directly without Atlas URLs (use alphagenome-single-variant-analysis or alphagenome-variant-impact-score).
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Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.
2 projects use a version of this
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Score, annotate, and analyze the functional impact of genetic variants using AlphaGenome Variant Impact (AVI) scores. Query variants in chr:pos:ref>alt format, annotate VCF/tabular callsets, perform saturation mutagenesis window scans (1-based closed chr:start-end), and extract GENCODE v46 GTF gene/exon/junction coordinates all via the AlphaGenome Atlas API.
1 project uses a version of this
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Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.
10 projects use a version of this
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Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility/inclusion criteria; count trials across conditions or time periods; identify a sponsor's trial portfolio; find recruiting trials for patient matching.
2 projects use a version of this
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Use when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls for human genomic variants.
9 projects use a version of this
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Instructions for handling API keys and credentials safely, verifying their presence, and prompting the user to add them if missing using a safe protocol.
12 projects use a version of this
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Use when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database. Resolves between rsIDs, genomic coordinates in VCF format, and HGVS strings. For an rsID, returns variant type, gene associations, clinical significance, allele frequencies, and genomic coordinates (GRCh38).
2 projects use a version of this
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Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search for terms, retrieve details, navigate hierarchies (parents, children, ancestors), look up properties and individuals, get autocomplete suggestions, or access ontology metadata and statistics.
2 projects use a version of this
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Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.). Use when you want to query regulatory annotations or raw experimental data across human cell types.
2 projects use a version of this
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Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.
9 projects use a version of this
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Plain text files in a repository that tell a coding agent how the project works: commands to run, conventions to follow and things to avoid. CLAUDE.md, AGENTS.md, cursor rules and skills are the common kinds.
CLAUDE.md or AGENTS.md?
CLAUDE.md is read by Claude Code. AGENTS.md is an open format that Codex, Cursor and other agents read. Many projects keep one and point the other at it.
What is a skill?
A folder with a SKILL.md that describes one capability, such as filling PDFs or reviewing code. The agent loads it only when the task calls for it.
Can I search my own team's files too?
Your agents already can, over MCP, limited to the files you're allowed to read. Searching them from this page is coming.
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