[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DJDD89NM0YVH0MQWTJCEM7":3,"skill-same:01M6DJDD89NM0YVH0MQWTJCEM7":337},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":13,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M6DJDD89NM0YVH0MQWTJCEM7","ecosystem-standards",null,"Provides active execution protocols to rigorously audit how code, directory structures, and agent actions comply with the authoritative ecosystem specs. Trigger when validating new skills, plugins, or workflows.","richfrem\u002Fmanchurian-agent-poc","richfrem","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F36930640?v=4","01M6DJB3Y2NK4D0039PAT41PJ4","01M6DJB43VC2VTX0M6DQ2983H0",1,"MIT","https:\u002F\u002Fgithub.com\u002Frichfrem\u002Fmanchurian-agent-poc\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fecosystem-standards\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"ecosystem standards","standards","how","trigger","false","active","write","read","Bash","directory","execution","actions","audit","plugins","protocols","code","workflows","2026-11-10 10:12:30",false,"---\nname: ecosystem-standards\ndescription: Provides active execution protocols to rigorously audit how code, directory structures, and agent actions comply with the authoritative ecosystem specs. Trigger when validating new skills, plugins, or workflows.\ndisable-model-invocation: false\nallowed-tools: Bash, Read, Write\ndependencies: [\"skill:ecosystem-authoritative-sources\"]\n---\n# Ecosystem Standards Review Protocol\n\nThis skill details how to perform an audit on new or existing capabilities (Skills, Plugins, Workflows, Sub-Agents, and Hooks) against authoritative ecosystem specifications to ensure they are created, installed, and structured correctly.\n\n## Instructions\nWhen invoked to review a codebase component or a planned extension:\n\n1.  **Identify the Component Type**: Determine if the subject is a Plugin boundary, an Agent Skill, an Antigravity Workflow\u002FRule, a Sub-Agent, or a Hook.\n2.  **Recall the Specs**: Before reviewing, read the relevant specification file found in the `ecosystem-authoritative-sources` skill library.\n    *   *Path:* `..\u002F..\u002Freferences\u002F*.md`\n3.  **Perform Rigorous Audit**:\n    *   **Structure**: Does the directory schema match the standard (`.claude-plugin\u002Fplugin.json`, `my-skill\u002FSKILL.md`)? Are all supporting files strictly organized into the official optional directories (`scripts\u002F`, `references\u002F`, `assets\u002F`) rather than cluttering the skill root?\n    *   **Naming**: Verify the skill name uses the **gerund form** (`verb + -ing`, e.g., `analyzing-spreadsheets`). Reject generic nouns. Ensure the `name` is 1-64 lowercase alphanumeric chars\u002Fhyphens only, contains NO consecutive hyphens (`--`), and EXACTLY matches the parent directory name.\n    *   **Content**: Does the YAML frontmatter adhere precisely to rules (`description` 1-1024 chars, `compatibility` max 500 chars, `metadata` strictly string-to-string keys\u002Fvalues)? Provide the recommendation to run `skills-ref validate .\u002Fmy-skill` to definitively catch parse errors.\n    *   **Description Viewpoint**: Ensure the `description` is written strictly in the **third person** (\"Extracts text\", not \"I extract text\") and isn't overly vague.\n    *   **Progressive Disclosure**: For Skills, is the `SKILL.md` file appropriately constrained (\u003C 500 lines) with extraneous detail pushed to one-level deep reference files? **Reject deeply nested reference chains**.\n    *   **Reference Paths**: Verify that all file references are strictly relative to the skill's root (e.g., `scripts\u002Fextract.py`), avoiding absolute paths outside the plugin boundaries.\n    *   **Reference Readability**: Do reference files >100 lines contain a Table of Contents for partial-read navigation?\n    *   **Script Quality**: Verify python utility scripts do **not** punt errors back to the LLM (e.g., failing silently), but instead handle exceptions safely or emit clear `stderr` messages. Ensure they don't use undocumented \"magic numbers\" (voodoo constants).\n    *   **Multi-CLI Support**: When integrating agent CLI plugins, support exists for `claude-cli`, `gemini-cli`, and `copilot-cli`. Plugins must reflect the native CLI syntax in their system files.\n    *   **Anti-Patterns**: Check for hardcoded credentials and Windows style paths (`\\`).\n    *   **Connector Abstraction**: If the plugin uses MCP tools, does it include a `CONNECTORS.md` using the `~~category` abstraction pattern instead of hardcoding specific tool names? This is required for portability. Ensure all native tool calls use the fully qualified `ServerName:tool_name` format.\n    *   **Interaction Design Quality**: For skills with user interaction, verify they use appropriate patterns:\n        - Discovery phases use progressive questioning (broad → specific), not question walls\n        - Decision points offer numbered option menus (3-7 items max)\n        - Expensive operations have confirmation gates\n        - Multi-step workflows include inline copyable **checklists**.\n        - Skills end with next-action menus, not dead ends\n        - Workflows taking long documents gracefully degrade using Document Format Agnosticism.\n    *   **Execution Safety (Plan-Validate-Execute)**: Do destructive or massive workflows mandate an intermediate verifiable plan file (e.g., `changes.json`) before execution?\n    *   **Dual-Mode Architecture**: If the skill both creates new artifacts AND improves existing ones, verify it implements the Bootstrap + Iteration dual-mode pattern with separate sections and trigger phrases.\n    *   **Output Templates**: If the skill generates reports or artifacts, verify it either defines an output template or negotiates the format with the user.\n    *   **Escalation and Safety**: Workflows with external risk must explicitly implement Graduated Autonomy Routing and Escalation Trigger Taxonomies rather than blanket-stopping on all issues.\n    *   **Source Transparency**: Data synthesis output MUST conclude with explicit `Sources Checked` and `Sources Unavailable` blocks.\n4.  **Produce Feedback**: Provide explicit, granular feedback outlining exactly which ecosystem constraints were violated and concrete suggestions for fixing them. 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Are all supporting files strictly organized into the official optional directories (`scripts\u002F`, `references\u002F`, `assets\u002F`) rather than cluttering the skill root?\n    *   **Naming**: Verify the skill name uses the **gerund form** (`verb + -ing`, e.g., `analyzing-spreadsheets`). Reject generic nouns. Ensure the `name` is 1-64 lowercase alphanumeric chars\u002Fhyphens only, contains NO consecutive hyphens (`--`), and EXACTLY matches the parent directory name.\n    *   **Content**: Does the YAML frontmatter adhere precisely to rules (`description` 1-1024 chars, `compatibility` max 500 chars, `metadata` strictly string-to-string keys\u002Fvalues)? Provide the recommendation to run `skills-ref validate .\u002Fmy-skill` to definitively catch parse errors.\n    *   **Description Viewpoint**: Ensure the `description` is written strictly in the **third person** (\"Extracts text\", not \"I extract text\") and isn't overly vague.\n    *   **Progressive Disclosure**: For Skills, is the `SKILL.md` file appropriately constrained (\u003C 500 lines) with extraneous detail pushed to one-level deep reference files? **Reject deeply nested reference chains**.\n    *   **Reference Paths**: Verify that all file references are strictly relative to the skill's root (e.g., `scripts\u002Fextract.py`), avoiding absolute paths outside the plugin boundaries.\n    *   **Reference Readability**: Do reference files >100 lines contain a Table of Contents for partial-read navigation?\n    *   **Script Quality**: Verify python utility scripts do **not** punt errors back to the LLM (e.g., failing silently), but instead handle exceptions safely or emit clear `stderr` messages. Ensure they don't use undocumented \"magic numbers\" (voodoo constants).\n    *   **Multi-CLI Support**: When integrating agent CLI plugins, support exists for `claude-cli`, `gemini-cli`, and `copilot-cli`. Plugins must reflect the native CLI syntax in their system files.\n    *   **Anti-Patterns**: Check for hardcoded credentials and Windows style paths (`\\`).\n    *   **Connector Abstraction**: If the plugin uses MCP tools, does it include a `CONNECTORS.md` using the `~~category` abstraction pattern instead of hardcoding specific tool names? This is required for portability. Ensure all native tool calls use the fully qualified `ServerName:tool_name` format.\n    *   **Interaction Design Quality**: For skills with user interaction, verify they use appropriate patterns:\n        - Discovery phases use progressive questioning (broad → specific), not question walls\n        - Decision points offer numbered option menus (3-7 items max)\n        - Expensive operations have confirmation gates\n        - Multi-step workflows include inline copyable **checklists**.\n        - Skills end with next-action menus, not dead ends\n        - Workflows taking long documents gracefully degrade using Document Format Agnosticism.\n    *   **Execution Safety (Plan-Validate-Execute)**: Do destructive or massive workflows mandate an intermediate verifiable plan file (e.g., `changes.json`) before execution?\n    *   **Dual-Mode Architecture**: If the skill both creates new artifacts AND improves existing ones, verify it implements the Bootstrap + Iteration dual-mode pattern with separate sections and trigger phrases.\n    *   **Output Templates**: If the skill generates reports or artifacts, verify it either defines an output template or negotiates the format with the user.\n    *   **Escalation and Safety**: Workflows with external risk must explicitly implement Graduated Autonomy Routing and Escalation Trigger Taxonomies rather than blanket-stopping on all issues.\n    *   **Source Transparency**: Data synthesis output MUST conclude with explicit `Sources Checked` and `Sources Unavailable` blocks.\n4.  **Produce Feedback**: Provide explicit, granular feedback outlining exactly which ecosystem constraints were violated and concrete suggestions for fixing them. 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