Create a reviewable reusable Agent Skill draft from a user's explicit natural-language description. Use when the user asks the Agent to create, build, or revise a Skill; do not use merely because a workflow seems reusable.
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Create a focused, portable Skill that preserves the user's stated intent.
name as the Skill's stable identity, not a proposal title. Every revision or alternative version of one logical Skill must reuse exactly the same name; put labels such as lite, systematic, v2, or experimental in metadata.version, the description, or the instructions instead of appending them to the name.references/, scripts/, or assets/ only when they have a concrete reusable purpose.create_skill exactly once with the complete proposed package. If the user explicitly requests multiple versions or alternatives of the same Skill, call it once per requested version in order and reuse the exact same name every time; each later call updates that draft and becomes another comparable proposal in its version history.If draft creation reports a read-only name or validation error, stop and explain the correction needed. Do not rename or retry without the user's direction.
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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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