Create upgrade guides for changes that make existing code or settings stop working. The skill checks differences and history, then provides specific steps, copyable before-and-after examples, and any available options for postponing the upgrade.
Review code or changes for exposed secrets, injection risks, weak encryption, and missing permission checks. Produce a structured report with severity, location, recommendations, and confidence, with a separate reviewer checking findings for false positives.
Investigate a bug's root cause, implement a focused repair, and verify the same behavior before and after the change. Select lasting regression tests, preserve reproduction evidence, and record the corrected user journey when possible.
Look up how to configure and organize Omnigent agents. Use examples to choose an executor, the agent's runtime, arrange instructions and skills, add local tools or external services, and define specialized child agents.
Investigate a reported bug before choosing a fix. Trace the actual failure, test competing explanations, and examine design history so the proposed change preserves intended behavior and clearly separates verified findings from unresolved assumptions.
Inspect imports and agent patterns in existing Python code to identify its framework and recommend an Omnigent executor, the component that runs the agent. Explain unsupported frameworks and suggest available next steps.
Run concurrent Omnigent conversations with real hosts and runners while replacing the LLM with a zero-latency mock. Read the generated report to explain response-time distributions, throughput, and failures, helping assess Omnigent's own processing overhead.
Plan and review new Omnigent harness integrations, the adapters connecting agent runtimes to the platform. Compare SDK and subprocess approaches with native interface wrappers, using capability matrices and checklists for implementation and testing.
Have Claude and GPT critique each other's answers, revise their positions, and work toward a combined response. Choose the number of rounds and see what changed, where they agree, and which disagreements remain.
Review code changes with an independent agent from a different model provider. After tests pass, check a fixed diff against acceptance criteria, send blocking issues back to the implementer, and repeat until review passes.
Split debugging, audits, searches, and code questions into read-only tasks for sub-agents. Combine their structured reports into an evidence-based answer, requesting follow-up investigations when findings conflict or leave important gaps.
Prepare a structured JSON handoff for bug fixes or pull request reviews. Capture outcomes, test evidence, change impact, publication status, and remaining work so the next person or workflow can understand what is complete and what remains.
Research questions on the web and produce a report with source links. Read full pages, check important claims against independent sources, and clearly distinguish supported findings from uncertain evidence or disagreements.
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.
Verify Omnigent behavior through real user actions in an isolated environment. Use a feature map to cover relevant web, terminal, and CLI entry points, preserving recordings and other evidence for bug reproductions and fixes.
Move an open pull request, a proposed code change, toward maintainer review by coordinating automated checks, two independent reviews, preview preparation, and live validation instructions, while tracking fixes and unresolved work.
Test Omnigent’s native Antigravity integration with a live local server and the real agy CLI. Verify messages traveling between the web interface and terminal, mirrored replies, permission requests, interruption, session resumption, and process cleanup.
Test Omnigent’s native Pi integration using the real terminal interface and a local server. Verify messages sent through the web or API, recorded replies, interruption, session resumption, authentication, and model settings to investigate integration problems.
Test polly's coordination of coding agents using a scripted mock language model and live command-line tools. Check startup, task dispatch, safety rules, parallel delegation, and whether real agents make appropriate orchestration decisions.
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