본문으로 건너뛰기
cccskills
무료GitHub에 공개

progressive-disclosure

Refactor large DataRobot skill files by moving detailed content into directly linked reference files while preserving meaning. Use when a skill triggers context-window warnings, needs progressive disclosure, or should be chunked without changing guidance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Progressive Disclosure

Use this repo-local meta skill to split large skills into a concise SKILL.md plus adjacent reference files. This guides content moves and reference creation; it is not a content rewrite pass.

Guardrails

  • Do not rewrite, reinterpret, simplify, or change the meaning of skill content.
  • Do not delete details because they seem verbose. Move details to reference files instead.
  • Do not change SDK guidance, commands, workflows, safety notes, or examples except to preserve links after moving content.
  • Do not edit plugin manifests, versions, CODEOWNERS, or unrelated repo metadata unless the user explicitly asks.
  • Do not create deeply nested references. Reference files should be directly linked from SKILL.md.
  • Do not move first-use trigger guidance, required safety constraints, or critical prerequisites out of SKILL.md.
  • If content needs substantive editing, stop and ask the user before making that change.

Target Shape

Keep SKILL.md focused on:

  • Frontmatter with accurate name and description
  • When to use the skill
  • Quick start or primary workflow
  • Key decision points and safety constraints
  • Direct links to reference files

Move long supporting content into files such as:

  • reference.md
  • examples.md
  • troubleshooting.md
  • platform-setup.md
  • sdk-operations.md
  • ci-cd-patterns.md

Choose names that match the moved section. Prefer a small number of meaningful files over many tiny fragments.

Use folders only when the supporting material has a clear type or grouping:

  • references/ for longer conceptual guides, examples, and workflow details
  • scripts/ for executable helpers that the skill may ask the agent to run
  • Language folders such as python/, typescript/, or r/ when a skill supports multiple programming languages

Keep these folders directly under the skill directory. SKILL.md should link directly to files inside them, such as references/workflow-details.md, without requiring the agent to follow a chain of references.

Workflow

  1. Inspect the target skill:

    • Read skills/<skill-name>/SKILL.md.
    • Check current line count and major headings.
    • Check current token warning/error status from task test:integration when available.
    • Identify sections that are detailed reference material, long examples, command recipes, troubleshooting tables, platform-specific variants, or API details.
  2. Propose the split before editing when the change is substantial:

    • List which sections will stay in SKILL.md.
    • List which sections will move and the destination reference file for each.
    • Keep a simple traceability map: original heading -> destination file -> replacement link text.
    • Confirm any ambiguous sections with the user.
  3. Move content with minimal transformation:

    • Preserve headings and body text as much as possible.
    • Adjust heading levels only when needed for valid Markdown structure.
    • Preserve code blocks exactly unless a relative link/path must change.
    • Keep warnings, prerequisites, and safety notes attached to the relevant content.
  4. Replace moved content with concise pointers:

    For detailed deployment configuration, see [deployment-reference.md](deployment-reference.md).
    

    Each pointer should explain when to read the reference, not summarize the whole reference.

  5. Keep links one level deep:

    • SKILL.md should link directly to every reference file the agent may need.
    • Reference files may link back to SKILL.md if useful.
    • Avoid chains like SKILL.md -> references/overview.md -> references/deep-reference.md.
  6. Validate:

    • Run task test:integration for structural checks.
    • Run task lint when tooling is available.
    • If the goal is reducing context-window warnings, rerun the relevant tests and compare warning output. This repo warns at an estimated 3300 tokens and errors at 6700 tokens.

Candidate Section Heuristics

Good move candidates:

  • Long examples that are not needed for initial task routing
  • Repeated setup variants by platform or provider
  • Detailed SDK method inventories
  • Troubleshooting catalogs
  • Extended CI/CD snippets
  • Long validation or debugging procedures

Usually keep in SKILL.md:

  • The first-pass decision tree
  • Required safety constraints
  • Minimal happy-path workflow
  • Short command checklist needed for most runs
  • Links to all supporting references

Diff Review Checklist

Before finishing, verify:

  • Every moved block still exists in a reference file.
  • SKILL.md links directly to each new reference file.
  • The frontmatter description still includes clear trigger scenarios.
  • The final response includes the traceability map for reviewer confidence.
  • No instruction, recommendation, or example changed.
  • No unrelated formatting churn was introduced.
  • Repo validation passes or any local tooling gap is clearly reported.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Use when the user wants to design, build, code, simulate, or deploy an AI agent (not a predictive model) to DataRobot; mentions agent_spec.yaml, dr-assist, datarobot-agent-assist, dress rehearsal, swarm simulation, or the DataRobot agent template; wants to scaffold a LangGraph, CrewAI, LlamaIndex, NAT, or Base agent targeting DataRobot; wants to add an MCP server, backend API, or React frontend to a DataRobot agent application; or uses the DataRobot CLI (dr) to build or deploy an agentic custom application; or wants to harden, stress-test, or battle-test an agent. Covers the full workflow: agent design, agent_spec.yaml authoring, dress-rehearsal simulation via the DataRobot LLM Gateway, adversarial swarm simulation, template-based coding, and deployment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

datarobot-oss/datarobot-agent-skills322026년 10월 23일 업데이트

Use when the user wants to design, build, code, or deploy an AI agent on DataRobot; mentions agent_spec.yaml, dress rehearsal, the DataRobot agent template, LangGraph, CrewAI, LlamaIndex, NAT, Base agents, MCP servers, backend APIs, custom frontends, or the DataRobot CLI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

datarobot-oss/datarobot-agent-skills322026년 10월 23일 업데이트

Use when an agent assist user wants to deploy their agent, put it on DataRobot, get a link to it, or share it with others, the agent can run as a single container, and the project has no `infra/Pulumi.yaml`. Not for agents built from the agent template, which deploy with Pulumi through datarobot-agent-assist. Users usually just say "deploy" or "deploy to DataRobot" and won't mention containers, workloads or the CLI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

datarobot-oss/datarobot-agent-skills322026년 10월 23일 업데이트

Use when the user wants to adversarially test, evaluate, or harden an implemented AI agent before deployment; mentions swarm simulation, attack testing, persistence testing, evaluation criteria, or eval_report.md.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

datarobot-oss/datarobot-agent-skills322026년 10월 23일 업데이트

Use when the user wants to configure LLM integration for a DataRobot agent application. This skill helps to change LLM model, switch between the LLM integrations the project supports (LLM Gateway, a DataRobot-deployed LLM, an external provider, an LLM Blueprint, and any others its config declares), or set up provider credentials. The skill reads the project's .datarobot/cli/llm.yml for the real options, interviews the user, then runs sync_llm_env.py with the chosen values as CLI args to merge into .env.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

datarobot-oss/datarobot-agent-skills322026년 10월 23일 업데이트

Guidance for setting up CI/CD pipelines for DataRobot application templates using GitLab, GitHub Actions, and Pulumi for infrastructure as code. Use when setting up CI/CD pipelines, configuring deployments, or managing infrastructure for DataRobot application templates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

datarobot-oss/datarobot-agent-skills322026년 10월 23일 업데이트

datarobot-oss의 스킬 모두 보기

이 스킬의 문제 신고