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Found 799 Skills
On-chain intelligence for DOG•GO•TO•THE•MOON rune — forensic analysis, LTH vs STH metrics, multi-chain whale tracking, multi-exchange markets, cross-chain data, and airdrop analytics powered by DOG DATA's Bitcoin full node.
A comprehensive stock deep analysis tool that combines real-time quotes, fundamental metrics, technical indicators, and growth analysis into a single professional report. Supports A-share, US stocks, HK stocks. Generates detailed investment recommendations with risk assessment and actionable trading strategies.
Opinionated guidance for constructing and interpreting Honeycomb queries on trace and event datasets — operation selection (percentiles not AVG, HEATMAP for distributions), relational field patterns (root., parent., any., none.), calculated fields, query math, and result interpretation (P99/P50 ratios, heatmap bands, TOTAL/OTHER rows, raw JSON via query_result_json). Use this skill when the user wants to query spans, traces, or log/event data in Honeycomb — requests like "show me latency", "error rate", "find slow requests", "find outliers", "interpret results", "relational fields", "calculated fields", or "download raw results". This skill covers all dataset types except metrics datasets (dataset_type=metrics) — for those, use metrics-queries instead.
Execute PromQL instant and range queries against Oodle metrics using the Prometheus-compatible query API.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
Gate 1: Business requirements document - defines WHAT/WHY before HOW. Creates PRD with problem definition, user stories, success metrics.
Query Oodle metrics, discover labels and values, and build PromQL expressions using the label discovery workflow.
Use ktx to build a self-improving context layer that teaches AI agents how to query data warehouses accurately with approved metrics, semantic layers, and business knowledge
When the user wants to set up, improve, or audit sales metrics and pipeline tracking. Also use when the user mentions "sales metrics," "pipeline tracking," "CRM setup," "sales dashboard," "activity tracking," "conversion tracking," "win rate," or "sales reporting." For testing sales approaches, see ab-test-setup.
Consistent Nova resources—fields, actions, metrics, lenses, filters, authorization—and how to evolve resources alongside schema changes
Analyze mindshare, sentiment, and broader social metrics for a particular entity using Kaito MCP tools. Use this skill when the user asks about the social pulse of a particular entity, wants mindshare or sentiment trends, or wants a deeper anomaly-based explanation.
Generates a comprehensive milestone progress review including feature completeness, quality metrics, risk assessment, and go/no-go recommendation. Use at milestone checkpoints or when evaluating readiness for a milestone deadline.