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Found 30 Skills
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Use when building or reviewing external API integrations in Python — designing client boundaries, defining outbound reliability policy, or structuring contract tests. Also use when provider SDK details leak into domain logic, outbound calls lack timeout/retry policy, or failure paths are untested.
One-click model liberation toolkit for removing refusal behaviors from LLMs via surgical abliteration techniques
MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.
Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError, (3) Managing async exceptions, (4) Preventing stack trace leakage, (5) Designing custom exception hierarchies.
Display kanban board status showing work package progress across lanes (planned/doing/for_review/done).
FastAPI Python async framework with Pydantic and automatic OpenAPI. Use for Python APIs.
Reverse engineer web APIs by capturing browser traffic (HAR files) and generating production-ready Python API clients. Use when the user wants to create an API client for a website, automate web interactions, or understand undocumented APIs. Activate on tasks mentioning "reverse engineer", "API client", "HAR file", "capture traffic", or "automate website".
Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".
Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.
Expert skill for using OpenViking, the open-source context database for AI Agents that manages memory, resources, and skills via a filesystem paradigm.
Guide for Using RQData Data API. Used when you need to query RQData data interfaces and obtain financial data. Supports data queries for markets such as A-shares, Hong Kong stocks, futures, options, indices, funds, and convertible bonds, including HTTP API and Python API documentation.