Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
Inline workflow executor for proxy providers (ZenMux, custom API providers). Executes workflow steps directly in the main context WITHOUT spawning Task subagents. Use this skill when: - Running looplia workflows via ZenMux or other proxy providers - Task subagents fail with "invalid_model" errors - You need inline execution without context isolation Architecture: Each workflow step is executed INLINE (no Task tool) - read skill, execute mission, write output, then proceed to next step. All steps share the main context. v0.6.6: Created for cross-provider compatibility with ZenMux.
Execute the forged prompt exactly as written. Requires explicit consent and a ready artifact. Deletes artifact after successful execution.
Orchestrates the full Ralph autonomous agent pipeline from requirements gathering to execution. Use when building new features, platforms, or complex tasks that need structured development through spec-interview, PRD generation, and autonomous implementation.
This skill should be used when the user asks to "create a CLAUDE.md", "write a CLAUDE.md", "set up CLAUDE.md", "configure Claude for this project", "add project instructions for Claude", "initialize Claude context", or mentions needing project-specific Claude instructions.
ユーザーに質問や確認をする際に毎回発動してください。自由回答形式ではなく、明確な選択肢（1質問あたり2-4個）を持つAskUserQuestionツールを使用し、ユーザーの入力負担を軽減して意思決定を迅速化します。柔軟性のためmultiSelect trueをデフォルトにしてください。
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
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.
This skill should be used when the user asks to "create a bootstrap prompt", "handoff", "save session state", "continue in new session", "create handoff", "session summary for continuation", "bootstrap for fresh session", or wants to capture the current session state for resumption in a new Claude Code session.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Aggregate a writing session into a readable log + a reusable author-intent prompt.
Generate images using Google Gemini with customizable options
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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).
An example skill demonstrating the Agent Skills structure with bundled resources for specialized tasks.
Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
Standardized templates and patterns for integrating skills into agent prompts. Reduces token overhead through reusable skill reference syntax, action verbs, and progressive disclosure usage guidelines.
Create engagement through strategic information gaps that drive user action. Use when designing notifications, writing headlines, planning onboarding flows, or creating content that needs to capture and hold attention.
Plan the v1→v2→v3 agency progression for AI features. Walk through mapping how autonomy increases over time, define promotion criteria, and generate artifacts for stakeholder alignment. Based on CC/CD framework.
Master the Effect AI LanguageModel service for text generation, structured output, streaming, and tool calling. Use when working with LLM interactions, schema-validated responses, or building conversational AI systems.
Multimodal AI processing via Google Gemini API (2M tokens context). Capabilities: audio (transcription, 9.5hr max, summarization, music analysis), images (captioning, OCR, object detection, segmentation, visual Q&A), video (scene detection, 6hr max, YouTube URLs, temporal analysis), documents (PDF extraction, tables, forms, charts), image generation (text-to-image, editing). Actions: transcribe, analyze, extract, caption, detect, segment, generate from media. Keywords: Gemini API, audio transcription, image captioning, OCR, object detection, video analysis, PDF extraction, text-to-image, multimodal, speech recognition, visual Q&A, scene detection, YouTube transcription, table extraction, form processing, image generation, Imagen. Use when: transcribing audio/video, analyzing images/screenshots, extracting data from PDFs, processing YouTube videos, generating images from text, implementing multimodal AI features.
Write LinkedIn posts in Kasper Junge's distinctive Danish style. Use when asked to write LinkedIn content, social media posts, or project announcements in Kasper's voice. Triggers include "skriv i min stil", "LinkedIn post", "del dette på LinkedIn", or when user wants casual Danish tech communication.
