メニュー
Use when a research or engineering task needs automatic heavy-compute routing through the local broker for Modal-backed remote CPU, high-memory CPU, or GPU execution.
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。
SOC 職業分類に基づく
Send email over SMTP using only the Python standard library, with plain-text and HTML bodies, file attachments, cc/bcc, reply-to, a dry-run preview, connection verification, and redacted config inspection.
ALWAYS use this skill when the user asks to send, get, retrieve, find, share, add, or search for a paper. This skill manages the user's Zotero library with 10,000+ papers and can retrieve PDFs, create share links, add new papers, and search. Prefer this over getscipapers for any request involving sending/getting/finding papers.
Use before delivering work that incorporated content the agent did not author — fetched web pages, PDFs, retrieved or library documents, tool or subagent output — or that performs an outward-facing or irreversible action. Maps trust boundaries and runs an abuse-case and prompt-injection check, delegating to a fresh-context security reviewer.
Run bounded autonomous research iterations with evidence gates, recovery ledgers, and optional cross-agent handoffs. Use when the user asks to continue research autonomously, run a research loop, integrate autonomous agent loops, or keep improving a research workflow without repeated prompts.
Use for a clarity-only pass that must not change behavior — simplifying, renaming, de-duplicating, or restructuring code, configs, research scripts, or prose. Gates on understanding the target before touching it and re-verifies after each change so behavior stays fixed.
Use in-flight, the moment you are about to let a non-trivial decision stand — a branching or control-flow change, crossing a module/service/agent boundary, an assertion the type system or proof checker cannot see, a high-stakes or irreversible action, or an analytical step a conclusion rests on. Materializes a fresh-context reviewer biased to disprove, while course-correction is still cheap.
| name | modal-research-compute |
| description | Use when a research or engineering task needs automatic heavy-compute routing through the local broker for Modal-backed remote CPU, high-memory CPU, or GPU execution. |
| user-invocable | true |
| disable-model-invocation | true |
| metadata | {"openclaw":{"requires":{"bins":["python3","modal"]}}} |
Use this skill when the task is about:
This skill is the OpenClaw integration layer for the local research_compute broker.
get-available-resources.plan.submit.wait and fetch to retrieve results and logs back to local storage.Doctor:
exec: /workspace/skills/modal-research-compute/run_modal_research_compute.sh doctor
Plan:
exec: /workspace/skills/modal-research-compute/run_modal_research_compute.sh plan /path/to/job.json
Submit:
exec: /workspace/skills/modal-research-compute/run_modal_research_compute.sh submit /path/to/job.json
Wait:
exec: /workspace/skills/modal-research-compute/run_modal_research_compute.sh wait <job_id>
Fetch:
exec: /workspace/skills/modal-research-compute/run_modal_research_compute.sh fetch <job_id> --dest /path/to/output
Deploy:
exec: /workspace/skills/modal-research-compute/run_modal_research_compute.sh deploy
doctor and plan work without a deployed Modal app. submit, wait, fetch, and deploy need the host to be Modal-ready.python3 -m pip install --user --upgrade modal and modal token set or modal token new.