一键导入
content-documenter
Generate structured ContentSummary JSON from analyzed content. Use after media-reviewer to produce final output with all 16 fields.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Generate structured ContentSummary JSON from analyzed content. Use after media-reviewer to produce final output with all 16 fields.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
End-to-end testing for looplia CLI from local source. Builds the CLI, initializes workspace, runs the writing-kit workflow, and verifies outputs. Use when testing local development or validating workflow execution.
This skill should be used when the user wants to execute a looplia workflow, run workflow steps, or process a workflow.md file. Use when someone says "run the looplia workflow", "execute this looplia pipeline", "/run writing-kit", "start the looplia automation", or "process these workflow steps". Architecture: One workflow step triggers one general-purpose subagent call, which then invokes skills to accomplish the step's mission. Each step = separate context window. Handles sandbox management, per-step orchestration, and validation state tracking. v0.6.9: Unified general-purpose subagent strategy for all providers (context offload).
Bump version across all looplia-core package.json files, changelog, and documentation. Use when: releasing a new version, preparing a version bump PR, updating version numbers across the monorepo. Handles all 5 package.json files, CHANGELOG.md, docs/README.md, and the landing page version badge.
This skill should be used when the user wants to create a new looplia workflow, generate a workflow definition file, or compose workflow steps from skill recommendations. Use when someone says "create a looplia workflow", "generate workflow.md", "compose workflow steps", "build me an automation pipeline", or "/build" (final step). Final step in looplia workflow building: transforms skill recommendations into valid v0.7.0 workflow YAML/Markdown files. Each step uses skill: + mission: syntax, following the one workflow step → one skill-executor → multiple skills architecture. v0.7.0: Generates explicit `skills:` declaration for selective plugin loading. v0.6.3: Supports input-less workflows using input-less capable skills (e.g., search).
Load the skill catalog for workflow building. This skill should be used when discovering available looplia skills or listing what capabilities are available. Use when someone says "what looplia skills are installed", "list available skills", "/build", "what can looplia do", or "show me all looplia capabilities". v0.7.0: Replaces plugin-registry-scanner with skill catalog for faster access. Auto-syncs from all sources on every build to ensure freshest skill catalog.
This skill should be used when matching user requirements to available skills from a compiled registry. It receives a skill registry (from registry-loader) and user requirements, then scores each skill based on capability alignment, producing prioritized matches with confidence scores. Triggers: "match skills to requirements", "find relevant skills for workflow", "which skill handles X", "score skill capabilities", "/build" (after registry load), "find skills for this task", "match my requirements to skills". Second step in looplia workflow building pipeline: takes user requirements and skill registry, recommends skill sequences with missions. Designs one workflow step → one skill-executor → multiple skills orchestration pattern.
| name | content-documenter |
| description | Generate structured ContentSummary JSON from analyzed content. Use after media-reviewer to produce final output with all 16 fields. |
| tools | Read, Write |
Transforms media-reviewer analysis into structured JSON documentation.
Takes your media-reviewer analysis and produces ContentSummary JSON with all 16 required fields.
You should have already used media-reviewer skill which provides:
contentId (string)
headline (string, 10-200 chars)
tldr (string, 20-500 chars)
bullets (string[], 1-10 items)
tags (string[], 1-20 items)
sentiment ("positive" | "neutral" | "negative")
category (string)
score.relevanceToUser (number, 0-1)
overview (string, min 50 chars)
keyThemes (string[], 3-7 items)
detailedAnalysis (string, min 100 chars)
narrativeFlow (string, min 50 chars)
coreIdeas (CoreIdea[], 1-10 items) Each item has:
importantQuotes (Quote[], 0-20 items) Each item has:
context (string, min 20 chars)
relatedConcepts (string[], 0-15 items)
For video/audio content:
0:30 - 30 seconds2:45 - 2 minutes 45 seconds1:30:00 - 1 hour 30 minutes{
"contentId": "abc123",
"headline": "Constitutional AI introduces a novel approach to aligning language models through self-critique",
"tldr": "This video explains Constitutional AI, Anthropic's method for training helpful and harmless AI assistants. The approach uses a set of principles (a 'constitution') to guide the model's self-improvement, reducing the need for human feedback while maintaining safety.",
"bullets": [
"Constitutional AI uses self-critique guided by explicit principles",
"The method reduces reliance on human feedback for safety training",
"Models learn to identify and correct their own harmful outputs"
],
"tags": ["ai", "safety", "alignment", "constitutional-ai", "anthropic", "rlhf"],
"sentiment": "positive",
"category": "video",
"score": { "relevanceToUser": 0.85 },
"overview": "This comprehensive video from Anthropic introduces Constitutional AI...",
"keyThemes": [
"AI Safety and Alignment",
"Self-supervised learning for safety",
"Reducing human feedback requirements",
"Explicit principles for AI behavior"
],
"detailedAnalysis": "The video opens with a clear problem statement...",
"narrativeFlow": "The presentation follows a classic problem-solution structure...",
"coreIdeas": [
{
"concept": "Constitutional AI",
"explanation": "An alignment approach where AI models critique and revise their own outputs based on explicit principles",
"examples": ["A model generating a harmful response, then self-critiquing"]
}
],
"importantQuotes": [
{
"text": "The key insight is that we can use AI to supervise AI",
"timestamp": "12:34",
"context": "Explaining the core mechanism that makes Constitutional AI scalable"
}
],
"context": "This video builds on prior work in RLHF...",
"relatedConcepts": ["RLHF", "red teaming", "scalable oversight", "AI alignment"]
}
Before outputting, verify: