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Found 384 Skills
Use this skill for ANY task involving jj or jujutsu version control. ALWAYS trigger when the user mentions jj, jujutsu, revsets, change IDs, bookmarks, or oplog. Also trigger when the user wants to squash, split, or reorder commits in a stack, write a revset query, absorb fixup changes, undo or restore a previous operation, resolve conflicts after rebasing, recover from force-pushes, rewrite protected/immutable commits, view change evolution (evolog), or try parallel approaches. Trigger even if "jj" is not explicitly said — "changes" instead of "commits", "stack" instead of "branch", "absorb", "squash into the right commit", "undo my last operation", "conflict after rebase", or "compare approaches in parallel" are strong jj signals. This skill contains critical non-obvious rules (like always using -m flags) that prevent broken workflows.
End-to-end outbound prospecting: detect intent signals, research companies, find decision-maker contacts, personalize messaging, launch campaign.
Design, weight, and tune a lead scoring model for your sales funnel. Use when building a lead scoring system, defining MQL/SQL criteria, assigning point values to lead attributes, setting up scoring in your CRM or MAP, tuning conversion thresholds, or deciding which signals should trigger sales follow-up. Do NOT use for reading existing buying signals (use /sales-intent), building prospect lists (use /sales-prospect-list), or marketing-to-sales handoff process design (use /revops).
Enrich contacts and companies with verified emails, phones, and firmographic data. Also covers CRM data hygiene, deduplication, and bulk enrichment. Use when enriching leads, finding email addresses, cleaning CRM data, doing bulk enrichment, optimizing enrichment credits, setting up auto-enrichment, or fixing stale contact data. Do NOT use for building new prospect lists from scratch (use /sales-prospect-list), interpreting buying signals (use /sales-intent), or general Apollo platform help (use /sales-apollo).
Signal-gated HODLMM yield allocator. Reads aibtc.news signals and Quantum Readiness Index alongside live HODLMM APR to compute a risk-adjusted yield score, then executes a Bitflow swap to prepare wallet for HODLMM deposit when conditions align.
Smart-money and ownership-flow signals for a single stock via Longbridge Securities — SEC 13F institutional portfolios + position changes (US), funds and ETFs that hold the stock, SEC Form 4 insider trades (US-only), US short-interest history, and HK broker holdings (HK-only). Read-only. Markets vary by subcommand. Triggers: "13F", "机构持仓", "基金持仓", "ETF 持有", "持有这只股票的基金", "内部人交易", "高管买卖", "Form 4", "做空数据", "空头", "卖空", "经纪商持仓", "中央结算", "13F", "機構持倉", "基金持倉", "ETF 持有", "持有這隻股票的基金", "內部人交易", "高管買賣", "做空數據", "空頭", "賣空", "經紀商持倉", "中央結算", "13F holdings", "institutional holders", "fund holders", "ETF holders", "insider trades", "insider buying", "insider selling", "Form 4", "short interest", "days to cover", "short ratio", "broker holding", "CCASS", "AAPL insider sales", "TSLA short interest", "700.HK broker holding".
Chan Theory Pattern Recognition — Automatically detect top/bottom fractals, Bi (upward/downward Bi), Segments, Zhongshu, and generate Buy 1/Buy 2/Buy 3/Sell 1/Sell 2/Sell 3 signals. Depends on the czsc library. Triggers: "缠论", "分型", "笔", "中枢", "线段", "一买", "二买", "三买", "一卖", "二卖", "三卖", "缠中说禅", "缠师", "纏論", "分型", "筆", "中樞", "線段", "一買", "二買", "三買", "一賣", "二賣", "三賣", "chanlun", "chan theory", "bi", "zhongshu", "buy point", "sell point", "fractal top bottom", "Chan theory".
Generates YAML signal configs for agent simulation experiments. Use when the user wants to define what signals to track, how to extract them from run artifacts, and how to aggregate them into experiment-level metrics. Trigger when users say: "generate a signal config", "create signals for my experiment", "I want to track [metric]", "write a signal YAML", "set up extraction for [thing]", "how do I measure [behavior] across runs", "configure signals for [experiment]", "create a signal config", "create signal config file", or "build a signal config".
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Framework for developing, testing, and deploying trading strategies for prediction markets. Use when creating new strategies, implementing signals, or building backtesting logic.
Technical analysis patterns - Elliott Wave, Wyckoff, Fibonacci, Markov Regime, and Turtle Trading with confluence detection. Use when analyzing charts, identifying trading signals, or calculating technical levels.
SolidJS framework development skill for building reactive web applications with fine-grained reactivity. Use when working with SolidJS projects including: (1) Creating components with signals, stores, and effects, (2) Implementing reactive state management, (3) Using control flow components (Show, For, Switch/Match), (4) Setting up routing with Solid Router, (5) Building full-stack apps with SolidStart, (6) Data fetching with createResource, (7) Context API for shared state, (8) SSR/SSG configuration. Triggers: solid, solidjs, solid-js, solid start, solidstart, createSignal, createStore, createEffect.