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Found 7,117 Skills
Use this skill when you see `/omo`. Multi-agent orchestration for "code analysis / bug investigation / fix planning / implementation". Choose the minimal agent set and order based on task type + risk; recipes below show common patterns.
Expert blueprint for visual novels (Doki Doki Literature Club, Phoenix Wright, Steins;Gate) focusing on branching narratives, dialogue systems, choice consequences, rollback mechanics, and persistent flags. Use when building story-driven, choice-based, or dating sim games. Keywords visual novel, dialogue system, branching narrative, typewriter effect, rollback, bbcode, RichTextLabel.
DAG-based multi-skill orchestration: Discover, Plan, Validate, Execute. Builds execution graphs for tasks requiring multiple skills in sequence or parallel with dependency resolution and context passing. Use when a task requires 2+ skills chained together, parallel skill execution, or conditional branching between skills. Use for "compose skills", "chain workflow", "multi-skill", or "orchestrate skills". Do NOT use when a single skill can handle the request, or for simple sequential invocation that needs no dependency management.
Use FuzzingLabs MCP Security Hub to integrate offensive security tools (Nmap, Nuclei, SQLMap, Ghidra, etc.) with AI assistants via Docker-based MCP servers
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrain), Phase 3 AutoML refinement on the DEFT-augmented dataset. This is the default entry point for any "run the AOI workflow", "fine-tune my PCB AOI model end-to-end", "improve my AOI ChangeNet model", or "AOI workflow with AutoML" request — route here instead of tao-run-deft-aoi directly unless the user explicitly asks for the DEFT loop ONLY (e.g. "run JUST the DEFT loop", "skip AutoML, only DEFT"). Also handles the same three-phase pattern for non-AOI DEFT applications — AutoML baseline then DEFT loop warm-started from AutoML's winning HPs then post-DEFT AutoML refinement on the iteration-augmented dataset. Trigger phrases include "run the AOI workflow", "AOI end-to-end", "AutoML + DEFT", "AutoML then DEFT", "tune hyperparameters then DEFT", "DEFT with AutoML at both ends", "warm-start DEFT", "improve my AOI model".
Implement Role-Based Access Control (RBAC), permissions management, and authorization policies. Use when building secure access control systems with fine-grained permissions.
Guide a user end-to-end through setting up Chrome Web Store API release automation in any repository. Use when asked to walk someone through OAuth/CWS credential setup, refresh token creation, local/CI secret setup, version-based publish automation, and submission status checks.
Gate DEX trading comprehensive skill. Supports MCP and OpenAPI dual modes: MCP mode calls through gate-wallet service (requires authentication), OpenAPI mode calls directly through AK/SK. Use when users mention swap, exchange, buy, sell, quote, trade. Automatically select the most suitable calling method based on environment.
Analyze stocks using Mark Minervini's SEPA (Specific Entry Point Analysis) methodology. Use this skill whenever the user mentions SEPA, Minervini, superperformance, trend template, VCP (Volatility Contraction Pattern), Stage 2 uptrend, stage analysis, pivot point breakout, or asks about growth stock screening criteria. Also triggers when the user wants to evaluate whether a stock meets swing trading entry criteria, check moving average alignment (bullish stacking: price above 50MA above 150MA above 200MA), assess breakout quality with volume confirmation, calculate position sizing based on risk percentage, or identify consolidation patterns like cup-with-handle, flat base, bull flag, or high tight flag. Use this skill even when the user simply asks "should I buy this stock" or "is this a good setup" in the context of growth/momentum trading, or when they share a stock chart and want pattern analysis.
Configure Harness AI-powered operations (AIDA) via MCP. Set up predictive failure analysis with ML models for memory leaks, disk exhaustion, connection pool saturation, and latency degradation. Configure intelligent alert correlation and noise reduction to reduce alert volume. Use when asked to set up predictive failure analysis, configure AI-powered alerting, reduce alert noise, or enable ML-based anomaly detection. Do NOT use for pipeline debugging (use debug-pipeline instead) or SLO management (use manage-slos instead). Trigger phrases: AIDA, predictive failure, alert correlation, noise reduction, anomaly detection, AI ops, predictive analysis, alert fatigue, ML alerting, intelligent alerting.
Orchestrate autonomous AI development with task-based workflow and QA gates
Optimize X/Twitter content for algorithm engagement signals. Based on xai-org/x-algorithm's Grok transformer model that predicts 15 user-specific engagement signals. Activates for tweet optimization, thread strategy, X growth, or algorithm-aligned content.