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cccskills
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skill-creator

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

openJiuwen-ai/sciencediscovery스타 178(GitHub 178 + cccskills 0)

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  • SKILL.md2.7 KB

SKILL.md(원문)

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Skill Creator

Create a focused, portable Skill that preserves the user's stated intent.

Creation contract

  • Act only when the user explicitly asks to create or install a Skill.
  • Infer ordinary details from the description. Ask a question only when missing information would materially change the Skill.
  • Use a lowercase kebab-case name of at most 64 characters and a concise, discriminating description.
  • Treat 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.
  • Assume the Agent is already capable. Instructions should contain only useful workflow guidance, non-obvious constraints, and concrete validation criteria.
  • Keep simple Skills self-contained. Add UTF-8 resources under references/, scripts/, or assets/ only when they have a concrete reusable purpose.
  • Packaged scripts are retained as read-only resources by this runtime; they are not automatically installed or executed. Do not promise automatic execution.
  • Never include credentials, private conversation details, or unrelated workspace content.

Workflow

  1. Translate the user's description into one stable Skill name, selection description, Markdown instructions, and optional version/resources.
  2. Check that the instructions preserve authorization boundaries and do not generalize a one-off preference into a universal rule.
  3. Normally call 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.
  4. A matching managed Skill name creates a draft based on its current revision; a matching unconfirmed draft is updated in place and compared with the previous Agent proposal; built-in Skills remain read-only.
  5. Report one pending Skill and direct the user to its review action in the conversation or Settings > Skills. The draft is inactive until the user explicitly confirms it.

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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같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

openJiuwen-ai/sciencediscovery1782026년 10월 26일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

openJiuwen-ai/sciencediscovery1782026년 10월 26일 업데이트

ci

무료

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

openJiuwen-ai/sciencediscovery1782026년 10월 26일 업데이트

Verify cited paper identity and decide whether claims in an Artifact are supported by their linked Evidence using a governed source snapshot.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

openJiuwen-ai/sciencediscovery1782026년 10월 26일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

openJiuwen-ai/sciencediscovery1782026년 10월 26일 업데이트

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

openJiuwen-ai/sciencediscovery1782026년 10월 26일 업데이트

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