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Found 333 Skills
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions. Computes posterior probabilities for causal variants, links variants to genes via L2G predictions, annotates functional consequences, and suggests validation strategies. Use when asked to fine-map GWAS loci, prioritize causal variants, identify credible sets, or link GWAS signals to causal genes.
Transform GWAS signals into actionable drug targets and repurposing opportunities. Performs locus-to-gene mapping, target druggability assessment, existing drug identification, safety profile evaluation, and clinical trial matching. Use when discovering drug targets from GWAS data, finding drug repurposing opportunities from genetic associations, or translating GWAS findings into therapeutic leads.
Expert signal-based selling strategist for B2B outbound teams. Use when the user asks about buying signals, intent data, signal scoring, signal-based selling, website visitor tracking, job change signals, hiring signals, funding signals, competitor signals, tech stack changes, content engagement signals, multi-signal stacking, RB2B setup, Trigify setup, Common Room, Bombora, Koala, Warmly, 6sense, signal-to-action playbooks, or building signal-driven outbound campaigns. Also triggers on "buying signals", "intent data", "signal scoring", "signal-based", "website visitors", "job change", "hiring signal", "funding signal", "competitor signal", "tech change", "content engagement", "RB2B", "Trigify", "Common Room", "Bombora", "intent signals", "warm outbound", "signal stacking", "visitor tracking", "signal tools", "GTM plays". Do NOT use for general list building without signal context (use list-building skill) or email writing (use cold-email skill).
Expert GDScript best practices including static typing (var x: int, func returns void), signal architecture (signal up call down), unique node access (%NodeName, @onready), script structure (extends, class_name, signals, exports, methods), and performance patterns (dict.get with defaults, avoid get_node in loops). Use for code review, refactoring, or establishing project standards. Trigger keywords: static_typing, signal_architecture, unique_nodes, @onready, class_name, signal_up_call_down, gdscript_style_guide.
Perform technical analysis on stock K-line data, calculate indicators such as MA/MACD/RSI, and judge trends and trading signals. Trigger scenarios: (1) "Analyze the technical aspects of Moutai" (2) "Check if this stock is buyable" (3) "Technical analysis 600519" (4) Used when needing to judge stock trends and trading points. Need to use data-collect to obtain data first
Abstract detector tickets and hints into reusable edge concepts with thesis, invalidation signals, and strategy playbooks before strategy design/export.
Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology. Dual-index tracking (S&P 500 + NASDAQ) with state machine for rally attempt, FTD qualification, and post-FTD health monitoring. Use when user asks about market bottom signals, follow-through days, rally attempts, re-entry timing after corrections, or whether it's safe to increase equity exposure. Complementary to market-top-detector (defensive) - this skill is offensive (bottom confirmation).
Detects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100 composite score with risk zone classification. Use when user asks about market top risk, distribution days, defensive rotation, leadership breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections.
Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode B). Use when user asks about PEAD screening, post-earnings drift, earnings gap follow-through, red candle breakout patterns, or weekly earnings momentum setups.
Use when the user needs to inspect Google Cloud (GCP) logs, metrics, and monitoring signals via gcloud for incident triage, debugging, or operational analysis. Supports Cloud Logging queries, Cloud Monitoring time-series reads, and environment checks for a target project.
Knowledge Blacksmith: Used to non-destructively merge and append new practical experience (new pitfalls encountered, new boundaries discovered, better solutions) into existing knowledge cards. The core principle is "extremely conservative minimally invasive surgery" - strictly prohibited from deleting or over-simplifying high-value experience (pain points, mistake records) in the original assets, and only upgrade old assets through Append and structured Merge. Trigger signals: `/asset-evolve`, `/asset-update`
Shell scripting and terminal integration patterns for building tools that integrate with Zsh, Bash, and Fish. Covers completion systems (compdef/compadd, complete/compgen, fish complete), ZLE widgets, hooks (precmd/preexec/chpwd, PROMPT_COMMAND), readline, bindkey, parameter expansion, ZDOTDIR loading order, event systems, abbreviations, POSIX shell scripting, terminal control codes (ANSI/CSI escape sequences), tput, stty, signal handling, process management (job control, traps), and shell plugin distribution patterns. Use when building shell plugins, writing completion scripts, implementing terminal UI with escape sequences, managing dotfiles, creating installation scripts, handling signals and process management, or integrating native binaries with shell wrappers.