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Found 197 Skills
Converts X (Twitter) tweets and articles to markdown with YAML front matter. Uses reverse-engineered API requiring user consent. Use when user mentions "X to markdown", "tweet to markdown", "save tweet", or provides x.com/twitter.com URLs for conversion.
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
File-based memory system using Tiago Forte's PARA method. Use this skill whenever you need to store, retrieve, update, or organize knowledge across sessions. Covers three memory layers: (1) Knowledge graph in PARA folders with atomic YAML facts, (2) Daily notes as raw timeline, (3) Tacit knowledge about user patterns. Also handles planning files, memory decay, weekly synthesis, and recall via qmd. Trigger on any memory operation: saving facts, writing daily notes, creating entities, running weekly synthesis, recalling past context, or managing plans.
Produces API reference documentation for Next.js APIs: functions, components, file conventions, directives, and config options. **Auto-activation:** User asks to write, create, or draft an API reference page. Also triggers on paths like `docs/01-app/03-api-reference/`, or keywords like "API reference", "props", "parameters", "returns", "signature". **Input sources:** Next.js source code, existing API reference pages, or user-provided specifications. **Output type:** A markdown (.mdx) API reference page with YAML frontmatter, usage example, reference section, behavior notes, and examples.
Generates technical guides that teach real-world use cases through progressive examples. **Auto-activation:** User asks to write, create, or draft a guide or tutorial. Also use when converting feature documentation, API references, or skill knowledge into step-by-step learning content. **Input sources:** Feature skills, API documentation, existing code examples, or user-provided specifications. **Output type:** A markdown guide with YAML frontmatter, introduction, 2-4 progressive steps, and next steps section.
Read/write config files (.env, YAML, TOML, JSON, docker-compose, etc.) safely. Use this instead of Read/Write/Edit tools whenever touching config files that may contain API keys, tokens, passwords, or other secrets — it auto-detects and redacts them.
Deploy project to hosting platform — read stack YAML for exact config, detect local CLI tools (vercel, wrangler, supabase, fly, sst), set up database, push code, verify live deployment. Use when user says "deploy it", "push to production", "set up hosting", or after /build completes. Do NOT use before build is complete.
Provides 3-tier validation approach for Home Assistant dashboards including pre-publish validation (entity checks, config structure), post-publish verification (log analysis), and visual validation (browser console, rendering). Use when validating HA dashboards, checking dashboard configs, verifying entity IDs, debugging rendering issues, or before deploying dashboard changes. Triggers on "validate dashboard", "check HA config", "dashboard errors", "entity not found", or "test dashboard". Works with Home Assistant WebSocket/REST APIs, Chrome extension MCP tools, Python dashboard builders, and YAML dashboard configurations.
Converts agent definitions between Markdown (with YAML frontmatter) and TOML formats. Use when transforming agent configurations for different agent systems — MD format for rich tool restrictions, TOML format for Codex-style agents with sandbox modes.
Optimize agent skills to reduce context bloat while preserving answer coverage. Use when: (1) A skill's SKILL.md body exceeds ~250 lines or duplicates its references/ files (2) A skill's YAML description is verbose or triggers false positives from sibling skills (3) Planning or executing a body/reference split for a skill (4) Auditing skill token efficiency
Create and maintain an Obsidian-style graph memory bank in a code repository: small atomic Markdown nodes with YAML frontmatter, cross-links, explicit backlinks, and release/entity-driven coverage for fast AI-agent context retrieval. Use when asked to build/upgrade a 'memory bank', 'graph memory', 'obsidian docs', 'суперсвязанную графовую документацию', or when you need structured docs under docs/ that let an AI agent pull minimal but precise context.
This skill guides creating autonomous agents for Claude Code plugins using markdown files with YAML frontmatter. Use when building new agents, designing agent system prompts, or configuring agent behavior.