Discover Data & AI skills, compare source repositories and read the instructions for each workflow.
Analyzes iron condor credit spreads with OTM put and call spreads for range-bound trading. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting sideways price action, want...
Compare bond performance against market benchmarks and indices. Analyzes yield spreads, duration matching, and relative value. Requires numpy>=1.24.0, pandas>=2.0.0. Use when evaluating bonds against peers, mea...
Factor modeling and portfolio construction (Markowitz, Black-Litterman, constraints, turnover).
Comprehensive financial statement analysis including ratio calculation, trend analysis, and peer comparison. Evaluates liquidity, profitability, efficiency, and leverage metrics. Requires numpy>=1.24.0, pandas>...
Price fixed income securities using present value, yield-to-maturity, and market conventions. Handles treasuries, corporates, municipals with various coupon frequencies. Requires numpy>=1.24.0, pandas>=2.0.0, s...
Tail risk, EVT, regularization, validation guardrails, and common pitfalls.
Analyzes long call butterfly spreads with 3 strikes and 4 legs for neutral outlook. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting minimal price movement, want low-c...
Analyzes bear-put-spread debit spreads for bearish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price decline, compar...
Game theory, network/systemic risk views, queueing/order-book, exchange notes.
Analyzes protective collar strategies combining long stock, long put protection, and short call income. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0. Use when protecting stock positions with reduced...
Analyzes bull-call-spread debit spreads for bullish directional plays with defined risk. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting moderate price increase, comp...
Analyzes long strangle volatility plays with OTM call and put at different strikes. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting very large price movement, want lo...
Information theory (entropy/MI/TE) and causal inference for trading signals.
Optimal execution, control theory, queueing/order-book, exchange microstructure.
Expert in writing PEP 257 compliant docstrings using Google-style format with custom parameter separator. Use when writing or updating Python documentation.
Expert in conducting thorough code reviews for Python projects using GitHub MCP, covering test coverage, security practices, PEP 8 compliance, and code organization. Use when reviewing code or PRs.
Expert in Python 3.10+ fundamentals and best practices for the Drift project including import ordering (isort), code organization, and PEP 8 conventions. Use when writing or reviewing Python code.
Expert in creating comprehensive pytest test suites for Drift project with strict 90%+ coverage requirements. Specializes in unit tests, integration tests, mocking external APIs (Anthropic, AWS Bedrock), and co...
Expert in GitHub workflows including issue management, PR operations, and branch conventions. Use when working with GitHub via MCP.
Expert in writing concise, factual git commit messages for Drift project without AI fluff. Use when committing code changes, creating releases, or reviewing commit history before PRs.
Expert in creating well-structured GitHub issues with clear problem statements, specific acceptance criteria, and actionable technical context using GitHub MCP. Specializes in feature requests, bug reports, and...
Expert in creating well-structured pull requests with clear summaries. Use when writing or structuring PRs.
Expert in auditing Sphinx/RST documentation for Drift project. Use when validating code examples and removing subjective language before release.
Expert in maintaining code quality standards using flake8, black, isort, and mypy. Use when fixing linting errors or enforcing code style.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.