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Found 14 Skills
Make AI solve hard problems that need planning and multi-step thinking. Use when your AI fails on complex questions, needs to break down problems, requires multi-step logic, needs to plan before acting, gives wrong answers on math or analysis tasks, or when a simple prompt isn't enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery reasoning patterns in DSPy.
Reflective sleep-and-dream heuristic for learning from recent experience. Use when the user asks to sleep on something, dream about it, reflect overnight, learn from yesterday, or extract lessons after a meaningful task, conversation, or debugging session. Avoid for first-pass analysis, simple factual lookups, direct execution, or tasks that do not benefit from reflection.
Use when facing complex reasoning tasks - multi-step math, logic puzzles, decisions with tradeoffs, problems where direct answers fail, or when you need to show your work. Triggers on arithmetic errors, shallow analysis, or "I'm not sure" hedging.
Sequential reasoning with deep self-reflection and backtracking. Use when problems have step-by-step dependencies, need careful logical reasoning, or require error correction. Each step includes self-reflection, and incorrect steps trigger backtracking. Ideal for debugging, mathematical proofs, sequential planning, or causal analysis where order matters.
AI-powered web search, research, and reasoning via Perplexity
Zero Framework Cognition Principles
Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.
Evaluate complex requests from 3 independent perspectives (Creative, Pragmatic, Comprehensive), reach consensus, then produce complete outputs. Use for architecture decisions, creative content, analysis, and any task where multiple valid approaches exist.
DEPRECATED: Use the model's native extended thinking instead. Structured, reflective problem-solving through sequential chain-of-thought reasoning that replaced the Sequential Thinking MCP server.
Reasons through problems using six cognitive modes. Applies causal (execute goals), abductive (explain observations), inductive (find patterns), analogical (transfer from similar), dialectical (resolve tensions), and counterfactual (evaluate alternatives) thinking. Use when planning, diagnosing, finding patterns, evaluating trade-offs, or exploring what-ifs. Triggers on "why did", "what if", "how should", "analyze this", "figure out".
Apply cognitive bias detection whenever the user (or Claude itself) is making an evaluation, recommendation, or decision that could be silently distorted by systematic thinking errors. Triggers on phrases like "I'm pretty sure", "obviously", "everyone agrees", "we already invested so much", "this has always worked", "just one more try", "I knew it", "the data confirms what we thought", "we can't go back now", or when analysis feels suspiciously aligned with what someone wanted to hear. Also trigger proactively when evaluating high-stakes decisions, plans with significant sunk costs, or conclusions that conveniently support the evaluator's existing position. The goal is not to paralyze — it's to flag where reasoning may be compromised so it can be corrected.
NEVER escalate without investigation first. This is the Iron Law. Use when evaluating whether to escalate models, facing genuine complexity requiring deeper reasoning, novel patterns with no existing solutions, high-stakes decisions requiring capability investment. Do not use when thrashing without investigation - investigate root cause first. DO NOT use when: time pressure alone - urgency doesn't change task complexity. DO NOT use when: "just to be safe" - assess actual complexity instead.