Data & Analytics

Python backend developer for FastAPI, Django, and Flask. Use when building Python APIs, REST endpoints, or data processing services.

This skill provides instructions for reducing token usage by 30-60% when dealing with structured data (JSON, directory structures, API responses).

Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.

No description provided.

Fetch inverter summary data from the Solax Cloud API using the npm package solax-cloud-api. Use when the user provides (or has configured) a Solax tokenId and inverter serial number (sn) and wants current/summary energy data returned as JSON (typed as SolaxSummary) for dashboards/automation.

Download, process, and backtest ByBit derivatives historical order book data. Use this skill when the user wants to: (1) download historical order book snapshots from ByBit's derivatives history-data page using Selenium automation, (2) process/unzip ob500 JSONL files and filter to depth 50, (3) run any of 10 order-book-based trading strategies (Order Book Imbalance, Breakout, False Breakout, Scalping, Momentum, Reversal, Spoofing Detection, Optimal Execution, Market Making, Latency Arbitrage) against the data, or (4) generate full backtest performance reports with PnL, Sharpe ratio, win rate, max drawdown, and strategy comparison. Triggers on: "bybit order book", "order book backtest", "download bybit data", "ob500", "order book imbalance", "spoofing detection strategy", "market making backtest", "crypto order book", "depth of book backtest", "bybit historical data".

In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.

This skill enables an AI Agent to signin and retrieve data from websites via JS script.

Query workout data from Hevy including workouts, routines, exercises, and history. Use when user asks about their workouts, gym sessions, exercise progress, or fitness routines.

Query Google Analytics 4 (GA4) data via the Analytics Data API. Use when you need to pull website analytics like top pages, traffic sources, user counts, sessions, conversions, or any GA4 metrics/dimensions. Supports custom date ranges and filtering.

Build knowledge graphs from OSINT data and discover hidden patterns using Neo4j graph algorithms.

Generate Redis key patterns and data structure designs. Use when planning your Redis architecture.