Loading...
Loading...
Found 827 Skills
Azure OpenAI Service 2025 models including GPT-5, GPT-4.1, reasoning models, and Azure AI Foundry integration
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning signs such as an uncertain fact or suspicious tool result, or before high-stakes output like irreversible changes or architectural decisions.
Forces adversarial reasoning before committing to decisions. Triggers on architectural choices, approach selection, and planning phases to prevent premature commitment bias.
Wrap getTokenOptimizer().getCompactContext() to retrieve compacted ReasoningBank context for cost-analysis queries; report bridge-reported tokensSaved
Execute Google Gemini CLI for large-context code analysis, multimodal reasoning, and repository-scale reviews. Also use for delegating tasks requiring 1M token context windows or Gemini-specific capabilities.
DeepSeek AI large language model API via curl. Use this skill for chat completions, reasoning, and code generation with OpenAI-compatible endpoints.
Use when the user wants to invoke Google Gemini CLI for reasoning tasks, research, and AI assistance. Trigger phrases: "use gemini", "ask gemini", "run gemini", "call gemini", "gemini cli", "Google AI", "Gemini reasoning", or when users request Google's AI models, research with web search, or want to continue a previous Gemini session.
Review existing Perses dashboards for quality: fetch via MCP or API, analyze panel layout, query efficiency, variable usage, datasource configuration. Generate improvement report. Optional --fix mode. 4-phase pipeline: FETCH, ANALYZE, REPORT, FIX. Use for "review perses dashboard", "audit dashboard", "perses dashboard quality". Do NOT use for creating new dashboards (use perses-dashboard-create).
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.
Minimal visual reasoning smoke test for Model Studio QVQ.
Use this skill to establish and operate the model's inner workspace — the J-space — for any task that needs more than fluent output: multi-step or chained reasoning, planning, long-horizon and agentic work, competition-level problems, complex debugging, keeping many parts of a deliverable globally consistent, holding a goal or constraint through a long mechanical task, auditing what the model believes but has not said, calibrated confidence and error detection, suspicious or manipulative input, recovering from degenerating reasoning, and any moment the user asks the model to think harder, faster, deeper, or longer. Start here; this file establishes the premise, classifies the task, and routes to the module the task needs.