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Found 22 Skills
Use when identifying chart patterns or setups - recognizes candlestick patterns (head and shoulders, double top/bottom, triangles), documents pattern library with entry/exit criteria. Activates when user says "what pattern is this", "is this a flag", "document this setup", mentions pattern names, or uses /trading:pattern command.
Elliott Wave Theory Signal Engine — Detects swing points via Zigzag, matches 5-wave impulse structures (1-2-3-4-5) and 3-wave corrective structures (A-B-C), validates with Fibonacci relationships, and generates wave positions, target prices, and risk levels. Triggers: "艾略特波浪", "波浪理论", "推动浪", "调整浪", "斐波那契", "1浪", "3浪", "5浪", "abc浪", "艾略特", "波浪計數", "推動浪", "調整浪", "斐波那契", "Elliott wave", "wave theory", "impulse wave", "corrective wave", "fibonacci retracement", "wave count", "wave 3", "wave 5".
Guidance for solving ARC-AGI style pattern recognition tasks that involve git operations (fetching bundles, merging branches) and implementing algorithmic transformations. This skill applies when tasks require merging git branches containing different implementations of pattern-based algorithms, analyzing input-output examples to discover transformation rules, and implementing correct solutions. (project)
Teaches learners to extract transferable design lessons from real-world codebases through critical evaluation and systematic exploration. Use when a learner wants to study existing code to learn patterns, architecture, or design decisions—not just understand what it does. Guides through navigation, pattern recognition, critical evaluation (deliberate choice vs. compromise), and lesson extraction. Triggers on phrases like "learn from this codebase", "study how X is implemented", "understand design patterns in Y", or when a learner wants to improve by reading real code.
Build comprehensive, mobile-compatible Obsidian study vaults from academic course materials with checkpoint-based workflow, error pattern recognition, and quality assurance. Battle-tested patterns from 828KB/37-file projects. Works across all subjects - CS, medicine, business, self-study.
Systematically investigate social media claims and viral content. Use when fact-checking complex claims, when decomposing multi-part assertions, or when investigating narratives that mix facts with interpretation.
Enhanced skill navigator that maps conversation history, recommends multi-skill chains, identifies patterns from past usage, and learns from session outcomes. Goes beyond basic scout with deep context analysis and workflow orchestration.
Systematic problem-solving techniques for stuck-ness. Techniques: simplification cascade (complexity spirals), collision-zone thinking (innovation blocks), meta-pattern recognition (recurring issues), inversion exercise (assumption constraints), scale game (uncertainty). Actions: simplify, analyze, recognize patterns, invert assumptions, scale thinking. Keywords: problem solving, complexity spiral, innovation block, stuck, simplification, meta-pattern, assumption inversion, scale uncertainty, breakthrough thinking, root cause, systematic analysis, Microsoft Amplifier, debugging approach, creative solution. Use when: complexity spiraling, hitting innovation blocks, seeing recurring patterns, constrained by assumptions, uncertain about scale, generally stuck on problems.
Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations. Use this skill when the user draws on historical precedent to justify a strategy, needs to evaluate whether a historical comparison is valid, or wants to learn from past events — even if they say 'this is like the dotcom bubble', 'history repeats itself', or 'what can we learn from how X handled this'.
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.
Build and maintain a Stockbee-style daily 20% mover study for US equities by scanning +20%/-20% movers, classifying catalysts and setup context, updating forward outcomes, and summarizing cohort patterns. Use when the user asks to run a daily 20% study, backfill historical 20% movers, find recurring edge patterns, or build a model book of explosive market moves.
Analyze mental health data, identify psychological patterns, assess mental health status, and provide personalized mental health recommendations. Supports correlation analysis with other health data such as sleep, exercise, and nutrition.