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Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASE_URL preloaded). Load once a task needs predictions from a registered model endpoint.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.
Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.
Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.
Set up a compute environment on a remote provider so Claude Science jobs can run there. Covers direct SSH/conda hosts, Slurm clusters, container-via-bridge runners, and managed-API providers (Modal, GCP, RunPod). Use when standing up a new provider, porting an env to a different backend, adding a tool that needs its own software stack, or wiring weight caches. Triggers on "new compute provider", "set up env on", "port env to", "build GPU image", "weight cache", "compute_details", "conda env on the box", "apptainer on slurm".
Create, configure, and maintain custom agent profiles and author new skills via the `repl` tool. Use when the user wants to create an agent profile, build a custom agent, modify agent capabilities, attach or detach skills/connectors on a profile, author a skill, or inspect which connectors and tools are available. Also use whenever you need the `host.agents.*` or `host.skills.*` Python SDK.
| name | using-model-endpoint |
| description | Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASE_URL preloaded). Load once a task needs predictions from a registered model endpoint. |
| license | Apache-2.0 |
You are a pure HTTP client of BASE_URL. Each registered model endpoint
gets its own inference kernel — a Python REPL whose network egress is scoped
to exactly that endpoint — reached via
compute_provider({'provider': '<slug>', 'code': '…'}) (<slug> from
list_compute, without the infer: prefix).
BASE_URL is preloaded (as a Python variable AND as
os.environ["BASE_URL"]) — build request URLs from it, never hardcode
hosts/ports. Call the model's native API with httpx (preinstalled)
or requests; request shapes live in the provider's own runbook skill
(the registration's skillName).Authorization: Bearer $INFER_API_KEY (always the
canonical env name when a credential is delivered; the credential's own
name is usually aliased too). Local endpoints need no auth header.HTTP_PROXY/HTTPS_PROXY are set) —
don't disable it (e.g. trust_env=False) or the endpoint is unreachable.Managed endpoints (entries with managed: true / a location field in
list_compute): their lifecycle — daemon-owned start/stop, registration,
free_port()/register() — lives in the
managed-model-endpoints skill. Cells against them are still just
HTTP calls to BASE_URL; the daemon brings the model up on demand (a cold
start streams its progress into your cell and can take minutes).