Prepare, launch, monitor, and summarize the real RFdiffusion to ProteinMPNN to Protenix antibody pipeline on a local or remote ScienceDiscovery Runner with sandboxed Ascend NPUs.
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Research scientific questions with enabled literature and database connectors, distinguish curated annotations from paper evidence, and produce a cautious claim-to-source brief. Use for gene/protein function summaries, literature scans, evidence tables, paper abstract reading, or cited scientific reports.
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Create an auditable evidence brief from brokered public database records. Keep every factual claim within the scope of the retrieved records.
record.contentScope before using a record. Connector search returns metadata, abstracts, or curated records—not article full text—and record.fullTextRetrieved remains false even when record.pdfAvailable says a PDF could be fetched separately.record.citation to every substantive claim. Use one canonical type per connector: [arXiv:<id>](<record.url>), [EuropePMC:<id>](<record.url>), [PMID:<id>](<record.url>), or [UniProt:<accession>](<record.url>). Citation types contain no spaces and are matched case-insensitively. Never emit a bare identifier such as [41887499], and do not cite an identifier that was not returned in this turn.run_shell (for example, python -c with environment_id selecting a Python-capable environment) to save evidence_brief.md and sources.json when files are requested. Include retrieval metadata and attribution in both outputs.Funding/COI: entry. Include a concise funding or competing-interest disclosure only when it appears in retrieved metadata or the abstract; otherwise write Funding/COI: not found in retrieved metadata. This only describes the retrieved record and does not establish that no funding or conflict exists.record.citation还没有评价。欢迎分享你的使用感受。
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Prepare, launch, monitor, and summarize the real RFdiffusion to ProteinMPNN to Protenix antibody pipeline on a local or remote ScienceDiscovery Runner with sandboxed Ascend NPUs.
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Independent multi-dimensional assessor for material design candidates. Loads configurable scoring rubrics to evaluate candidates from different expert perspectives. Each dispatch produces structured scores, pros/cons, and verification data without sharing results with other assessors.
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Read, diagnose, and change the CI pipeline: GitHub Actions on pull requests, the merge queue, the nightly schedule and the release tag. Use when a job fails, when editing .github/workflows/, when asking which layer runs what, when reproducing a pipeline failure locally, or when a job needs the bubblewrap sandbox. Running the layers before proposing a change, and reading the result a proposal receives, belong to create-github-pr.
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Verify cited paper identity and decide whether claims in an Artifact are supported by their linked Evidence using a governed source snapshot.
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Use this skill when you need to write and execute Python/R code to process, transform, and analyze data, delivering reproducible computational results with complete code-level methodology documentation. Supports statistical analysis, data transformation, visualization, method justification, and structured result output.
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Check whether numeric claims marked with Evidence aliases in an Artifact agree with the linked Evidence content and context.
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