perf-reviewer
Review code for performance issues and run benchmarks. Use when user asks to analyze performance, compare AILANG vs Python vs Go, run benchmarks, or review code for optimization opportunities.
Trouvez la capacité idéale pour votre agent.
Review code for performance issues and run benchmarks. Use when user asks to analyze performance, compare AILANG vs Python vs Go, run benchmarks, or review code for optimization opportunities.
Patterns for SQLite databases in Python projects - state management, caching, and async operations. Triggers on: sqlite, sqlite3, aiosqlite, local database, database schema, migration, wal mode.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
CLI application patterns for Python. Triggers on: cli, command line, typer, click, argparse, terminal, rich, console, terminal ui.
Progressive learning methodology for structured onboarding using time-boxed learning paths (Day-1, Week-1, Month-1), validation checkpoints, and scaffolding principles. Use when onboarding new contributors, reducing ramp-up time from weeks to days, creating self-service learning paths, systematizing ad-hoc knowledge sharing, or building institutional knowledge preservation. Provides 3 learning path templates (Day-1: 4-8h setup→contribution, Week-1: 20-40h architecture→feature, Month-1: 40-160h expertise→mentoring), progressive disclosure pattern, validation checkpoint principle, module mastery best practice. Validated with 3-8x onboarding speedup (structured vs. unstructured), 95%+ transferability to any software project (Go, Rust, Python, TypeScript). Learning theory principles applied: progressive disclosure, scaffolding, validation checkpoints, time-boxing.
Systematic technical debt quantification and management using SQALE methodology with value-effort prioritization, phased paydown roadmaps, and prevention strategies. Use when technical debt unmeasured or subjective, need objective prioritization, planning refactoring work, establishing debt prevention practices, or tracking debt trends over time. Provides 6 methodology components (measurement with SQALE index, categorization with code smell taxonomy, prioritization with value-effort matrix, phased paydown roadmap, trend tracking system, prevention guidelines), 3 patterns (SQALE-based quantification, code smell taxonomy mapping, value-effort prioritization), 3 principles (high-value low-effort first, SQALE provides objective baseline, complexity drives maintainability debt). Validated with 4.5x speedup vs manual approach, 85% transferability across languages (Go, Python, JavaScript, Java, Rust), SQALE industry-standard methodology.
SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage
This skill provides comprehensive guidance for SAP Business Application Studio (BAS), the cloud-based IDE on SAP BTP built on Code-OSS. Use when setting up BAS subscriptions, creating dev spaces, connecting to external systems, deploying MTA applications, troubleshooting connectivity issues, managing Git repositories, configuring runtime versions, or using the layout editor. Keywords: SAP Business Application Studio, BAS, SAP BTP, dev space, Cloud Foundry, MTA, multitarget application, SAP Fiori, CAP, HANA, destination, WebIDEEnabled, Cloud Connector, Service Center, Storyboard, Layout Editor, ABAP, OData, subscription, entitlements, role collection, Business_Application_Studio_Developer, Git, clone, push, pull, Gerrit, PAT, OAuth, asdf, runtime, Node.js, Java, Python, Task Explorer, CI/CD, Yeoman, generator, template wizard, mbt, mtar, debugging, breakpoint
Systematic testing methodology for Go projects using TDD, coverage-driven gap closure, fixture patterns, and CLI testing. Use when establishing test strategy from scratch, improving test coverage from 60-75% to 80%+, creating test infrastructure with mocks and fixtures, building CLI test suites, or systematizing ad-hoc testing. Provides 8 documented patterns (table-driven, golden file, fixture, mocking, CLI testing, integration, helper utilities, coverage-driven gap closure), 3 automation tools (coverage analyzer 186x speedup, test generator 200x speedup, methodology guide 7.5x speedup). Validated across 3 project archetypes with 3.1x average speedup, 5.8% adaptation effort, 89% transferability to Python/Rust/TypeScript.
Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or writing tests.
Python SQL toolkit and Object Relational Mapper (ORM). Use when working with databases in Python, defining models, building queries, managing sessions, or interacting with SQL databases using Python objects.
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
Analyzes CSV files and generates comprehensive summary statistics and visualizations using Python and pandas - automatically and immediately without asking what the user wants.
Use when user EXPLICITLY requests "custom integration", "custom component", "Python for HA", "HACS", or "config_flow". NOT for: YAML automations (use home-assistant skill), Node-RED flows (use node-red skill), ESPHome device configs (use esphome skill).
Coding standards, modular architecture, and common execution patterns for the PolyFlup Python backend.
Bootstrap new projects with strong typing, linting, formatting, and testing. Supports Python, TypeScript, and other languages with research fallback.
Generate Python code to solve AppWorld agent tasks using playbook bullet guidance. Use when the AppWorld executor needs executable Python code for tasks involving Spotify, Venmo, Gmail, Calendar, Contacts, or other AppWorld APIs.
Use when aligning with Georgios’s default Git/GitHub conventions—cloning with gh, Conventional Commits, atomic staging, and guarded GitHub Actions for Rust/Python/TypeScript—so work matches his standard workflows.