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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.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/JimLiu/science-skills --skill using-model-endpointコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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.
SOC 職業分類に基づく
SKILL.md を表示中
| 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).