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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openJiuwen-ai/sciencediscovery1782026年10月26日 更新
免费在 GitHub 公开
computation-reviewer
Check whether numeric claims marked with Evidence aliases in an Artifact agree with the linked Evidence content and context.
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openJiuwen-ai/sciencediscovery星标 178(GitHub 178 + cccskills 0)
实测评分 未实测
查看安装方法包含的文件（1）
- SKILL.md1.6 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
Computation Reviewer
Run on the same locked Artifact with its supplied Evidence Bundle. In the bounded Deep pipeline, this local Evidence comparison runs before Citation source verification.
Required checks
- Review exactly one supplied numeric Claim and its matching Evidence Bundle entry per run. The
Claim must contain a number and an
[evN]alias in the same sentence or Markdown table cell. - Compare value, unit, range, sample size, population, subgroup, endpoint, and statistical context with the linked Evidence.
- Distinguish exact support, value or unit contradiction, scope mismatch, interpretation overreach, and insufficient evidence.
- Return
INCONCLUSIVEwhen the Evidence excerpt does not contain enough context; do not report insufficient content as a value mismatch.
Output
Return only the required structured result. Use a concrete COMPUTATION_* finding code such as
COMPUTATION_EVIDENCE_VALUE_MISMATCH, COMPUTATION_EVIDENCE_SCOPE_MISMATCH,
COMPUTATION_EVIDENCE_INTERPRETATION_OVERREACH, or COMPUTATION_EVIDENCE_INSUFFICIENT.
Preserve the supplied Evidence alias and provide one concise reason; do not invent a separate
correction field that the review protocol cannot persist.
Boundaries
- Do not recompute results, execute code, modify Artifacts, or create another agent.
- Do not treat topical similarity as numeric support.
- Preserve Evidence aliases and provide a concise reason for every mismatch.
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openJiuwen-ai/sciencediscovery1782026年10月26日 更新
ci
免费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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openJiuwen-ai/sciencediscovery1782026年10月26日 更新
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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openJiuwen-ai/sciencediscovery1782026年10月26日 更新
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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openJiuwen-ai/sciencediscovery1782026年10月26日 更新
Open or update a pull request on GitHub's openJiuwen-ai/sciencediscovery: run the UT/ST/E2E layers locally, push the branch to the operator's own GitHub fork, write a body that says what was verified with numbers, apply exactly one release:* label so the release note can classify it, then read the pull request and its Actions run back. Use when asked to create a PR, submit a change for review, when a branch is ready to propose, when labelling a pull request for the release note, or when reading a GitHub Actions result on a pull request. This is the only path for proposing a change: gitcode.com is a read-only mirror with no pipeline of its own. Titles and bodies are English. For filing an issue, or for the gh commands this procedure assumes, read the github skill first.
中文简介正在准备中。当前显示原文说明。
openJiuwen-ai/sciencediscovery1782026年10月26日 更新