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Found 267 Skills
Use when migrating from Realm to SwiftData - comprehensive migration guide covering pattern equivalents, threading model conversion, schema migration strategies, CloudKit sync transition, and real-world scenarios
A complete skill for E2E testing
Standardized artifact creation via tk tickets. Use whenever a skill needs to persist output — research findings, plans, postmortems, reviews, design specs, decisions. Replaces all bespoke output directories (.oracle/, .plans/, etc.) with a single canonical system.
Persistent local memory for AI agents. Silently capture and retrieve context that survives beyond a single conversation: business requirements, API specs, integration quirks, technical decisions, user preferences, and domain knowledge. Use this skill proactively whenever you encounter information worth preserving or when context from past sessions would help the current task. Also triggered manually by "braindump this" (to store) or "use your brain" (to retrieve).
Coordinates multi-session, delegated, or long-running work with persistent state, recovery checks, and explicit status transitions. Use when a task spans multiple turns, multiple agents, background jobs, or scheduled loops, or when interrupted work must be resumed reliably.
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
Extracts key learnings from conversations, debugging sessions, and failed attempts. Use at session end or after solving complex problems to capture insights. Stores discoveries in memory (via amplihack.memory.discoveries), suggests PATTERNS.md updates, and recommends new agent creation. Ensures knowledge persists across sessions via Kuzu memory backend.
Long-term semantic memory across sessions using Mem0. Use when you need to remember, recall, or forget information across sessions, or when referencing what we discussed last time or in a previous session.
Use when you need to turn a vague idea into a confirmed design spec before implementation (new feature/component/behavior change). First check project context, then ask one question at a time, provide 2-3 options with trade-offs, finally output design in segments (~200-300 words each) with confirmation after each. Triggers: brainstorm, clarify idea, design spec, refine concept, requirement clarification.
Subscribe to AI and tech RSS feeds and persist normalized metadata into SQLite using mature Python tooling (feedparser + sqlite3). Use when adding feed URLs/OPML sources, running incremental sync with deduplication, and storing entry metadata without full-text extraction or summarization.
Clarify the outcome you want - a change in user behavior, not a feature shipped. Use at the start of any work to ground the session in strategic intent.
Do the work. Pre-flight, build, detect drift, salvage if needed. Use when you have a clear aim and are ready to implement.