Managing a Longbridge account across multiple sub-accounts, asset classes, and order types is complex. This skill provides structured access to account data, position analysis, and portfolio optimization frameworks through natural language.

Access real-time quotes, K-line charts, order book depth, and IPO calendars for global markets directly in your AI assistant. Eliminates the need to switch between multiple trading terminals for basic market data queries.

Screening stocks, tracking sector rotation, and reading ETF flows manually is slow and fragmented. This skill gives Claude, Codex, and Claude Code a unified Longbridge-backed intelligence hub for screeners, anomalies, catalysts, and thematic research.

Access comprehensive financial data and valuation analysis for global stocks without manual research. This skill orchestrates Longbridge CLI and MCP tools to deliver financial statements, valuation metrics, and company insights in seconds.

Investors need fast, structured earnings analysis but manually reading through filings and transcripts is time-consuming. This skill delivers pre- and post-earnings analysis with beat/miss tracking, segment breakdowns, and valuation models in seconds.

Traders and analysts need fast access to options chains, Greeks, implied volatility, and HK warrant data without leaving their AI assistant. This skill routes natural-language questions to the Longbridge CLI and returns structured results.

Investors need quick access to news, regulatory filings, and community sentiment for any stock. This skill fetches all of that from Longbridge in one prompt, with structured classification and bilingual output.

Get instant access to real-time market data, fundamentals, news, and portfolio analytics across US, HK, CN, and SG markets. This skill enables Claude to query the Longbridge financial platform for quotes, filings, insider trades, and account information.

Managing multi-language translations across large projects is tedious and error-prone. This skill helps AI coding assistants write source strings correctly, follow project naming conventions, and integrate with Localhero.ai translation workflows.

Deep learning debugging often leads to speculative patches that break research reproducibility. This skill provides conservative diagnosis with explicit approval gates before any code modification, keeping debug fixes separate from research contributions.

Deep learning training runs often lack reproducible evidence and clear status reporting. This skill executes a selected training command conservatively and writes standardized training evidence to train_outputs/.

Manually scanning AI repositories for reproduction commands is time-consuming and error-prone. This skill automates README-first analysis to extract documented commands and generate minimal trustworthy reproduction plans.

When reproducing AI research, repository READMEs often leave critical gaps about dataset splits, evaluation protocols, or preprocessing details. This skill resolves those narrow reproduction questions from primary paper sources while preserving README-first guidance.

Running AI paper reproduction experiments requires consistent command execution and standardized reporting. This skill executes smoke tests, inference runs, or evaluation commands while automatically generating structured output bundles for audit trails.

Deep learning researchers need to run quick exploratory trials without overclaiming results. This skill generates budget-aware variant matrices with fair-comparison caveats, keeping exploratory evidence clearly separated from trusted baselines.

Deep learning researchers need to explore code modifications without breaking trusted baselines. This skill creates conservative, auditable change plans with explicit rollback paths for isolated worktrees.

Setting up AI research environments for paper reproduction is complex and error-prone. This skill automates conservative conda-first environment creation and asset path planning to reduce setup friction.

Understanding a new deep learning repository is time-consuming and error-prone. This skill provides read-only static analysis to map model structure, training entrypoints, and suspicious patterns without modifying code or running expensive training jobs.

Reproducing deep learning papers is slow and error-prone because commands, datasets, and assumptions are scattered across READMEs. This skill reads the repository first, selects the smallest documented target, and writes a standardized repro_outputs/ bundle with evidence, deviations, and human decision points.

Creating AI-generated images and videos typically requires navigating complex platforms and writing detailed prompts. This skill connects directly to liblib.tv's API, letting you generate and edit visual content through natural language commands.