[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7316R2JBWWT24DEVFGVZ1WW":3,"skill-same:01M7316R2JBWWT24DEVFGVZ1WW":663},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":16,"sourceHtmlUrl":17,"categories":18,"keywords":19,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":14,"repositoryHtmlUrl":31,"repositoryHomepageUrl":6,"ownerHtmlUrl":32,"ownerBlogUrl":6,"ownerDescription":33,"updatedAt":34,"install":35,"resources":48},"01M7316R2JBWWT24DEVFGVZ1WW","yao-meta-skill",null,"Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse.","JOHNadonis\u002Fclaude-code-skills","JOHNadonis","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31716775?v=4","01M72YRBKCVM4ZYJFF7Z71KVQQ","Hongcai Wang","01M72YRBMFSBZNVRJ4D48MWMZ3",0,"MIT","https:\u002F\u002Fgithub.com\u002FJOHNadonis\u002Fclaude-code-skills\u002Fblob\u002Fmain\u002Fskills\u002Fyao-meta-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FJOHNadonis\u002Fclaude-code-skills\u002Fblob\u002Fmain\u002Fskills\u002Fyao-meta-skill\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: yao-meta-skill\ndescription: Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse.\nmetadata:\n  author: Yao Team\n  philosophy: \"structured design, evaluation loop, template ergonomics, operational packaging\"\n---\n\n# Yao Meta Skill\n\nBuild reusable skill packages, not long prompts.\n\n## Router Rules\n\n- Route by frontmatter `description` first.\n- Keep `SKILL.md` to routing plus the minimal execution skeleton.\n- Push long guidance to `references\u002F`, deterministic logic to `scripts\u002F`, and evidence to `reports\u002F`.\n- Use the lightest process that still makes the skill reliable.\n\n## Modes\n\n- `Scaffold`: exploratory or personal.\n- `Production`: team reuse with focused gates.\n- `Library`: shared infrastructure or meta skill.\n\nMode rules: [Operating Modes](references\u002Foperating-modes.md), [QA Ladder](references\u002Fqa-ladder.md), [Resource Boundary Spec](references\u002Fresource-boundaries.md), [Skill Engineering Method](references\u002Fskill-engineering-method.md).\n\n## Compact Workflow\n\n1. Decide whether the request should become a skill, then choose the lightest archetype.\n2. Capture the recurring job, outputs, trigger phrases, and exclusions.\n3. Write the `description` early, then test route quality before expanding the package.\n4. Add only the folders and gates that earn their keep: `trigger_eval.py`, `optimize_description.py`, `judge_blind_eval.py`, `resource_boundary_check.py`, `governance_check.py`, `cross_packager.py`.\n\nPlaybooks: [Skill Engineering Method](references\u002Fskill-engineering-method.md), [Skill Archetypes](references\u002Fskill-archetypes.md), [Gate Selection](references\u002Fgate-selection.md), [Non-Skill Decision Tree](references\u002Fnon-skill-decision-tree.md), [Operating Modes](references\u002Foperating-modes.md), [Trigger And Eval Playbook](references\u002Feval-playbook.md).\n\n## Output Contract\n\nUnless the user asks otherwise, produce:\n\n1. a working skill directory\n2. a trigger-aware `SKILL.md`\n3. aligned `agents\u002Finterface.yaml`\n4. optional `references\u002F`, `scripts\u002F`, `evals\u002F`, `reports\u002F`, and `manifest.json` only when justified\n5. a short summary of boundary, exclusions, gates, and next steps\n\n## Reference Map\n\n- [Skill Engineering Method](references\u002Fskill-engineering-method.md)\n- [Skill Archetypes](references\u002Fskill-archetypes.md)\n- [Gate Selection](references\u002Fgate-selection.md)\n- [Non-Skill Decision Tree](references\u002Fnon-skill-decision-tree.md)\n- [Operating Modes](references\u002Foperating-modes.md)\n- [Governance Model](references\u002Fgovernance.md)\n- [Resource Boundary Spec](references\u002Fresource-boundaries.md)\n- [Trigger And Eval Playbook](references\u002Feval-playbook.md)\n","# Yao Meta Skill\n\nBuild reusable skill packages, not long prompts.\n\n## Router Rules\n\n- Route by frontmatter `description` first.\n- Keep `SKILL.md` to routing plus the minimal execution skeleton.\n- Push long guidance to `references\u002F`, deterministic logic to `scripts\u002F`, and evidence to `reports\u002F`.\n- Use the lightest process that still makes the skill reliable.\n\n## Modes\n\n- `Scaffold`: exploratory or personal.\n- `Production`: team reuse with focused gates.\n- `Library`: shared infrastructure or meta skill.\n\nMode rules: [Operating Modes](references\u002Foperating-modes.md), [QA Ladder](references\u002Fqa-ladder.md), [Resource Boundary Spec](references\u002Fresource-boundaries.md), [Skill Engineering Method](references\u002Fskill-engineering-method.md).\n\n## Compact Workflow\n\n1. Decide whether the request should become a skill, then choose the lightest archetype.\n2. Capture the recurring job, outputs, trigger phrases, and exclusions.\n3. Write the `description` early, then test route quality before expanding the package.\n4. Add only the folders and gates that earn their keep: `trigger_eval.py`, `optimize_description.py`, `judge_blind_eval.py`, `resource_boundary_check.py`, `governance_check.py`, `cross_packager.py`.\n\nPlaybooks: [Skill Engineering Method](references\u002Fskill-engineering-method.md), [Skill Archetypes](references\u002Fskill-archetypes.md), [Gate Selection](references\u002Fgate-selection.md), [Non-Skill Decision Tree](references\u002Fnon-skill-decision-tree.md), [Operating Modes](references\u002Foperating-modes.md), [Trigger And Eval Playbook](references\u002Feval-playbook.md).\n\n## Output Contract\n\nUnless the user asks otherwise, produce:\n\n1. a working skill directory\n2. a trigger-aware `SKILL.md`\n3. aligned `agents\u002Finterface.yaml`\n4. optional `references\u002F`, `scripts\u002F`, `evals\u002F`, `reports\u002F`, and `manifest.json` only when justified\n5. a short summary of boundary, exclusions, gates, and next steps\n\n## Reference Map\n\n- [Skill Engineering 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