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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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Evaluate material design candidates against configurable multi-dimensional scoring rubrics. Each dispatch operates as an independent expert: no shared scores, reasoning, or outputs between assessors.
Dispatched by the Lead agent during leaf execution (Step 4.2 of the Idea Tree workflow). The Lead may dispatch multiple independent instances, each loading a different rubric perspective:
Lead agent
├── dispatches: assessment-screener (rubric: agent-a) → independent scores
├── dispatches: assessment-screener (rubric: agent-b) → independent scores
├── dispatches: assessment-screener (rubric: agent-c) → independent scores
└── dispatches: insight-aggregator (cross-validate & synthesize)
agent-a, agent-b, agent-c, or overall).creative-material-design's job).insight-aggregator's job).The assessor loads one rubric per dispatch based on the assessment_perspective field in the dispatch context:
| Perspective | Role | Reference File | Focus |
|---|---|---|---|
agent-a | activity | references/rubric-agent-a.md | Catalytic activity and reaction mechanism (weight 35%) |
agent-b | stability | references/rubric-agent-b.md | Structural stability and durability (weight 35%) |
agent-c | sustainability | references/rubric-agent-c.md | Environmental safety and sustainability (weight 30%) |
overall | — | references/rubric-overall.md | Cross-validation and final ranking (used by aggregator) |
If no perspective is specified, default to agent-a.
Each assessor evaluates 4 role-specific dimensions and produces an overall_score (1-10). The server computes the final weighted score:
final_score = 0.35 × activity_overall + 0.35 × stability_overall + 0.30 × sustainability_overall
Each assessor's dimensions are different (not shared):
| Role (Expert) | Dimensions |
|---|---|
| activity (A) | catalytic_activity, reaction_mechanism, selectivity, efficiency |
| stability (B) | structural_stability, durability, recyclability, lifetime |
| sustainability (C) | environmental_safety, sustainability, disposal, lifecycle |
{
"expert": "A",
"focus_area": "catalytic_activity_and_reaction_mechanism",
"snapshot_hash": "<candidate snapshot_hash>",
"candidate_version_id": "<exact checkpointed version>",
"evaluation": {
"<dimension_key>": {
"score": 1-10,
"analysis": "detailed analysis"
}
},
"overall_score": 1-10,
"recommendations": ["suggestion1", "suggestion2"],
"conclusion": "comprehensive assessment conclusion"
}
MUST read the loaded rubric reference file in full before scoring.
snapshot_hash and candidate_version_id from the checkpointed artifact.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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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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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.
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