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Coordinate multiple agents on a shared task list to complete development work. Break tasks into clear responsibilities, manage dependencies, and move through planning, implementation, verification, and bounded repair cycles with recorded handoffs.
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Carry an agreed development task through implementation and verification. Make small changes that follow existing patterns, divide independent work when useful, and report changed files, evidence that the code works, and any remaining issues.
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Turn an approved campaign brief into a posting calendar, Canva social designs, captions, and plain-text emails. With owner approval at each stage, schedule social posts in HubSpot and hand over email drafts for sending through the owner's chosen tool.
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Execute development subtasks under a supervising agent, starting with an implementation plan and impact analysis. Deliver code and a three-question self-review in a defined completion format for the supervisor to assess.
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Plan and run ablation studies, experiments that remove or replace parts of a method to test their contribution. Organize reviewer questions, priorities, expected outcomes, and compute costs, then record results and findings.
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Find why MNN CPU inference is slow by examining threads, scheduling, memory layout, and the processor instruction path actually used. Operator-level measurements and comparisons with other inference frameworks help identify which layer needs improvement across ARM, x86_64, and RISC-V.
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Automate browser tasks through BrowserWing's HTTP API, from navigation and form filling to extracting search results. Inspect page structure to identify controls, capture screenshots, and run multiple actions in sequence.
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Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.
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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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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 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 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.
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Create an Omnigent agent directory using validated templates for configuration and instructions. Choose how the agent runs, then add web search, shell access, external tool connections, or subagents only when the task requires them.
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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.
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Build a custom fuzzer, a tool that tests code with many generated inputs, using LibAFL in Rust. Combine input mutation, execution feedback, scheduling, and target execution components when standard fuzzers offer insufficient control.
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Guide development of a LangChain or LangGraph project created with agents-cli. It explains tool additions, local runs, evaluation, and serving while identifying the required graph structure and commands that differ from ADK projects.
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[omh] Which tools and credentials a workflow needs: inventory which MCP servers, CLIs, APIs, credentials, and connectors a workflow needs; use external-connector-readiness to assess one named integration and executor-runtime-readiness to choose the coding owner. Use when the user says: toolbelt-readiness, mcp readiness, tool readiness, plugin readiness, connector readiness, needed mcp, api credential, missing cli.
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[omh] Coding owner already chosen, handoff pending: prepares the handoff for the coding agent you already chose, composing its prompt from that agent's own installed skills; never selects the owner and never executes the work itself. Use when the user says: ulw-maestro, coding handoff, prepare the handoff, prepare a coding handoff, hand off the coding work, external executor handoff, handoff prompt, delegation prompt.
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[omh] Native program crashes or corrupts memory: prepare hypothesis-driven debugging of native binaries and instruct the executor to drive a DAP debugger instead of printf. Use when the user says: native-debugging, native debugging, native binary, segfault, segmentation fault, core dump, stack corruption, memory corruption.
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[omh] Entire product design problem to delegate: prepare a bounded design direction, existing-lane composition, and executor-neutral handoff. Use when the user says: design-orchestration, design orchestration, design ownership, handle this product design, take on the design, デザインを任せる, デザイン全体を任せ, プロダクトデザインを任せ.
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[omh] Evaluating a plugin, connector, or API for adoption: external connector readiness - assess whether a named plugin, connector, API, data provider, or multimodal route is safe, affordable, fresh, and observable; use executor-runtime-readiness for coding-owner choice and toolbelt-readiness for missing capability inventory. Use when the user says: external-connector-readiness, external connector readiness, connector readiness matrix, plugin readiness matrix, provider readiness, api readiness, connector adoption, external plugin adoption.
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Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.
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Work with Rust async runtimes — Tokio, async-std, executor internals, pinning, wakers, and building high-performance async Rust services.
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**WORKFLOW SKILL** - Evaluate AI agent skills using structured benchmarks with YAML specs, fixture isolation, and pluggable validators. USE FOR: run waza, waza help, run eval, run benchmark, evaluate skill, test agent, generate eval suite, init eval, compare results, score agent, agent evaluation, skill testing, cross-model comparison. DO NOT USE FOR: improving skill frontmatter (use waza dev), creating new skills from scratch (use skill-creator), token counting or budget checks (use waza tokens). INVOKES: Copilot SDK executor, mock engine, code/regex validators. FOR SINGLE OPERATIONS: use waza run directly for a single benchmark.
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