Prepare GRPO reinforcement learning configurations for tasks with programmatically checkable answers, such as math, code, and structured output. Design and inspect reward functions, then select training variants when runs show instability or exploit flaws in scoring.
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Turn graded execution traces and production logs into model training examples and preferred-versus-rejected response pairs. Select useful records, remove sensitive information, exclude evaluation examples, and preserve links to the original runs.
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Tune vector indexes, which organize data for similarity searches, to balance speed, memory use, and missed matches. Choose index types, adjust HNSW settings, and assess compression options using benchmarks with real queries.
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Move Python application settings out of code and into environment variables. Use pydantic-settings to check value types and required fields at startup, organize development and production settings, and keep secrets out of version control.
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Implement Python error handling with early input checks, appropriate exception types, and clear messages. Handle individual failures in batch jobs, preserve error context for debugging, and use Pydantic models to validate structured input.
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Design and refactor Python components with clear responsibilities and simple structure. Assess when to split large classes, combine objects instead of extending them, and extract shared code without introducing unnecessary abstractions.
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Help secure payment systems using PCI DSS, the payment card data security standard. Covers encryption, log masking, access controls, and tokenization, which replaces card details with substitute values to reduce direct handling of sensitive data.
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Identify software protections that hinder inspection, such as debugger detection and obfuscation that makes code harder to understand. Support authorized binary analysis and CTF security challenges with protection findings, bypass strategies, reports, and analysis scripts.
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Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.
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Build Grafana dashboards that show application and infrastructure health through request rates, errors, response times, and resource usage. Organize metrics into useful panels, add filters and alerts, and manage dashboard definitions as code.
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Prepare the narrative, sources, and design requirements for an editable PowerPoint deck. Clarify the audience and intended decision, track evidence for factual claims, and record visual choices before slide creation begins.
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Turn Python code into installable libraries or command-line tools. The skill guides project structure, package metadata, dependency settings, building wheel and source archives, and publishing releases to PyPI or private package repositories.
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Add retries and timeouts to Python service calls, with increasing and randomized waits between attempts. Use examples to distinguish temporary failures from permanent errors, limit retry duration, and record retries in logs.
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Investigate security incidents using memory dumps, saved copies of RAM. The skill guides memory acquisition, process and network analysis, suspicious code detection, and evidence extraction with Volatility and related tools, including checks for capture integrity.
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Review Python code for common mistakes in error handling, asynchronous operations, type annotations, and tests. Use a practical checklist and correction examples to guide code reviews, investigate bugs, and improve existing implementations.
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Define service reliability measurements and targets, then turn them into Prometheus monitoring rules and alerts. Calculate error budgets, the amount of failure allowed, and outline Grafana dashboards for tracking reliability and deciding when to prioritize fixes.
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Add Python type hints and configure strict checking with mypy or pyright to catch type errors before execution. Covers reusable classes that preserve type information, interfaces defined by required members, and checks for possibly missing values.
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Integrate PayPal into online checkout flows with one-time payments, subscriptions, refunds, and payment notifications. Includes client-side buttons, server-side payment handling, and sandbox testing to check the purchase flow before using live credentials.
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Move slow Python operations into background jobs so requests can return promptly. Use task queues and workers, the processes that execute queued work, with retry rules, duplicate handling, time limits, and persistent job status.
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Configure supervised fine-tuning for models that process images and text. Define which components remain unchanged, set LoRA adapters for updating selected model weights, choose image resolution limits, and investigate training runs whose evaluation results fail to improve.
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Create and organize Python tests with pytest. Use reusable setup, mock replacements for external services, controlled test time, and coverage reports to check normal behavior, failures, retries, and interactions between application components.
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Validate generated PowerPoint decks for layout problems, accessibility, editable content, source references, and file integrity. Follow a repair and recheck workflow, keeping audit reports and build records together for delivery.
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Design machine learning workflows from data preparation through training, validation, deployment, and monitoring. Use templates and guidance to connect stages, track versions and experiments, handle failures, and plan gradual releases with rollback options.
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Choose a model training method and configuration from paired response preferences or individual positive and negative ratings. Covers DPO, ORPO, KTO, and SimPO, including pair construction, repeated training rounds, and troubleshooting learning settings.
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