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Helps write and improve Python code with type hints, tests, and organized package settings. Covers dependency management, asynchronous tasks that wait on external operations, and profiling to identify performance bottlenecks before making changes.
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Guide SQLite database design and performance tuning for applications. Covers concurrent reads and writes, indexes that speed up queries, full-text search, JSON processing, analytical queries, and versioned changes to database structure.
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Write and improve Go code with clear error handling, interface design, and tests. Build concurrent workflows, where tasks run independently, with controlled worker counts, cancellation, and explicit shutdown paths for background work.
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Run the current working copy locally with a real language model and Postgres database. Choose web, Slack, or both to test a branch end to end, with separate Slack apps for concurrent instances.
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Analyze and tune PostgreSQL databases using query execution plans, which show how queries run. This skill guides index design, JSONB searches, replication setup, maintenance, and monitoring, with checks to compare behavior before and after changes.
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Write and review Gradio demos for Hugging Face ZeroGPU. Configure GPU time limits and dependencies, and troubleshoot errors caused by separate worker processes, shared state, concurrent requests, or CUDA package installation.
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Test Omnigent’s GitHub Copilot SDK integration on a live local server. Build test agents, run real conversations, and inspect authentication, model selection, tool connections, policy enforcement, and leftover processes to investigate integration failures.
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Test Omnigent’s setup, help, and terminal interactions by driving the real CLI in an isolated environment. Capture rendered screens and compare checks before and after a change to establish whether a reported interface problem was fixed.
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Test Omnigent’s Cursor SDK integration against a live local server. Build test agents, run real conversations, and check authentication, model selection, and tool connections while investigating empty responses or bridge processes left after shutdown.
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Test Omnigent’s Antigravity SDK integration against a live local server. Build test agents and run real conversations to check Gemini authentication, model selection, tool connections, and failures across the full execution path.
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Investigate errors and health in a running Convex app using plain-language questions. It summarizes relevant logs with counts and representative evidence, checks deployment timing, and provides dashboard links when possible for verification.
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Investigate independent bugs or test failures concurrently by assigning each problem area to a separate agent. Define clear boundaries and expected reports, then review the fixes, check for conflicts, and run the full test suite before integration.
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Reviews Rust applications and libraries for security issues involving unsafe code, memory safety, concurrency, calls across language boundaries, panics, and async runtimes. Coordinates specialized reviewers and produces Markdown and SARIF reports, a format for analysis tools.
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Review native C/C++ applications and services for memory errors, integer overflows, and race conditions, where concurrent operations interfere. Coordinate multiple reviewers and produce findings with severity ratings, alongside reports of missing or unverified review coverage.
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Assess API business workflows for flaws that automated scanners can miss. The skill examines skipped steps, simultaneous transactions, authentication tokens, GraphQL, and webhooks, helping testers document weaknesses and their financial or operational impact during authorized assessments.
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Investigate TOCTOU flaws: race conditions where a checked property changes before an action uses it. The skill covers filesystem, kernel, web, and container testing, helping identify vulnerable sequences, reproduce timing issues, and document observed impact.
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Build and debug Python agents with Swarms, a framework for coordinating multiple AI agents. Covers tool connections, saved memory, autonomous execution, and workflow choices such as sequential steps, parallel work, specialist delegation, and combined answers.
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Estimate cross-server traffic, network bottlenecks, and synchronization waits for AI training or inference. Map communication onto physical network paths and assess how topology or communication changes affect the time needed to complete the job.
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Plan when an agent’s tool environments are created, kept available, released, and restored. Estimate peak concurrency, resource use during work and waiting, total task duration, and cost across model calls, tool execution, validation, and retries.
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Review Apache Pinot changes for concurrency and state safety, including locks, shared callbacks, lifecycle changes, and distributed metadata. Use it to examine how changes affect shared state when multiple operations run at the same time.
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Set up one mirrord-up.yaml file to debug several related services locally at the same time. Define each application’s command, Kubernetes target, environment settings, and traffic mode, then manage the sessions together with mirrord up.
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Track changes during authorized red-team testing by comparing response times, headers, and account lockouts. Preserve evidence before and after changes, investigate possible defensive actions or concurrent attacks, and turn observations into timestamped findings.
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Design studies that combine quantitative data, such as surveys, with qualitative evidence, such as interviews. It recommends a suitable design and outlines study phases, participant selection, timing, and ways to combine findings into an integrated interpretation.
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Runs HTTP load tests with gradually increasing concurrent connections. It measures response-time percentiles, throughput, and errors, identifies where performance starts to deteriorate, and recommends a concurrency level based on the results.
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