本文へ移動
cccskills
無料GitHub で公開

assessment-screening

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.

日本語の概要は準備中です。原文の説明を表示しています。

openJiuwen-ai/sciencediscoveryスター 178(GitHub 178 + cccskills 0)

実行スコア 未検証

インストール方法を見る

含まれるファイル(4)

  • SKILL.md4.7 KB
  • references/rubric-agent-a.md2.1 KB
  • references/rubric-agent-b.md2.1 KB
  • references/rubric-agent-c.md2.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Assessment Screening

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.

Role in the Workflow

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)

When to Use

  • The Lead agent has checkpointed a candidate material design artifact and needs independent multi-dimensional scoring.
  • The dispatch context specifies which rubric perspective to load (agent-a, agent-b, agent-c, or overall).

Do NOT Use For

  • Generating material designs (that is creative-material-design's job).
  • Synthesizing insights or cross-validating other assessors (that is insight-aggregator's job).
  • Literature search, evidence extraction, or report writing.

Rubric Loading

The assessor loads one rubric per dispatch based on the assessment_perspective field in the dispatch context:

PerspectiveRoleReference FileFocus
agent-aactivityreferences/rubric-agent-a.mdCatalytic activity and reaction mechanism (weight 35%)
agent-bstabilityreferences/rubric-agent-b.mdStructural stability and durability (weight 35%)
agent-csustainabilityreferences/rubric-agent-c.mdEnvironmental safety and sustainability (weight 30%)
overall—references/rubric-overall.mdCross-validation and final ranking (used by aggregator)

If no perspective is specified, default to agent-a.

Scoring Model

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

Critical Rules

  1. Real evaluation only — Provide genuine assessments based on available data, not fabricated scores.
  2. No fabricated data — Do not fabricate tool results, database identifiers, MP-IDs, CAS numbers, or any other identifiers.
  3. Independent assessment — Each dispatch is fully independent. Do not reference or attempt to align with other assessors' results.
  4. Failure reporting — If verification data is unavailable, explicitly state this and explain the implications for the score.

Output Format

{
  "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"
}

Methodology

MUST read the loaded rubric reference file in full before scoring.

  1. Identify material — Classify the material type from the candidate artifact.
  2. Verify data — Cross-check claimed properties against available sources. Do not fabricate missing data; explain gaps.
  3. Score each dimension — Apply the rubric's 1-10 scale strictly. Do not assign scores that contradict the criteria.
  4. Generate feedback — Provide specific, actionable improvement suggestions.
  5. Produce output — Return the JSON structure above with exact snapshot_hash and candidate_version_id from the checkpointed artifact.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

コメント

ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。

まだコメントはありません。最初のコメントを書いてみませんか。

送信するとすぐに公開されます。メールアドレスや電話番号などの個人情報は書かないでください。URL は 2 つまでです。

0 / 1000 文字

このフォームは reCAPTCHA で保護されており、Google のプライバシーポリシーと利用規約が適用されます。

同じリポジトリのスキル

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.

日本語の概要は準備中です。原文の説明を表示しています。

openJiuwen-ai/sciencediscovery1782026年10月26日 更新

ci

無料

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

openJiuwen-ai/sciencediscovery1782026年10月26日 更新

Check whether numeric claims marked with Evidence aliases in an Artifact agree with the linked Evidence content and context.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

openJiuwen-ai/sciencediscovery1782026年10月26日 更新

openJiuwen-ai のスキルをすべて見る

このスキルの問題を報告する