339건 · 관련도순
Compare API performance across versions — regression detection and root cause analysis. Use when asked "did performance regress", "compare API latency across versions", or "why is this slower".
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Power management and performance optimization for Zephyr RTOS. Covers system power states (Idle, Suspend, Off), device-level power management, residency hooks, and code/data relocation for speed efficiency. Trigger when optimizing battery life, reducing latency, or managing memory constraints.
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Testing and debugging for Zephyr RTOS. Covers the Ztest framework for unit and integration tests, the Twister test runner for automated HIL/SIM validation, and advanced tracing/debugging techniques (SystemView, Thread Analyzer, Stack analysis). Trigger when writing unit tests, setting up CI/CD pipelines, or analyzing system performance and latency.
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Investigate an alert, page, or production symptom in an incident channel or a team's oncall / monitoring channel, and report findings a human can verify. Ordinary conversation and chatter are not alerts; leave those alone. Use when an alert or page lands, when error rate, latency or saturation is up, when someone asks "why is X broken", "investigate", "what changed", "root cause this", pastes a monitor, dashboard, trace or error-tracker link, or reports production trouble; also by default when an alert lands in a covered channel unasked. Feed-level asks land here too ("which alerts matter", "triage today's alerts"), plus the scheduled alert-review routine and the ticket path ("customer X can't check out"). Runs a fast first pass posted as a short interim update with a TL;DR, every finding carrying the query or link to verify it, then proposes the fix and — with connector access and confirmation — carries it out, never unattended. Afterwards `incident-postmortem` writes it up.
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Add or maintain CodeBuddy hook observability for projects that need local metrics for tool latency, transcript token usage, session cost, and multi-agent trace attribution. Use when wiring hook-based telemetry, debugging missing usage events, and validating AgentLens state.
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Runs CloudWatch Logs Insights queries via AWS SDK for JavaScript v3. Analyzes Lambda cold starts, API Gateway latency, and ECS container logs. Generates anomaly detection alarms with math expressions.
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Scans AWS CloudWatch Logs using the CloudWatch Logs Insights API and CloudWatch Anomaly Detection API. Identifies unusual error patterns, latency spikes, and log volume anomalies across log groups.
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Instruments Anthropic API calls to log token usage, latency, and cost per request using the Anthropic TypeScript SDK. Wraps the anthropic.messages.create method to capture usage.input_tokens, usage.output_tokens, and timing from the API response. Writes structured logs to CloudWatch Logs via the AWS SDK v3 CloudWatchLogsClient.
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Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.
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Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.
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Systematically identify whether a GPU kernel is compute-bound, memory-bound, or latency-bound using roofline analysis, occupancy calculations, compute/load ratio per tile, and SASS instruction inspection. Produces a decision matrix for optimization strategy selection (cp.async, warp interleaving, tiling, double-buffering, or CuAssembler hand-tuning).
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Intercept, inspect, and modify HTTP/HTTPS traffic via a Rust MITM proxy with Lua scripting hooks. Supports API debugging, LLM call capture for evals, endpoint mocking, CORS/auth header injection, latency simulation, and telemetry blocking. Triggers on 'proxy', 'intercept traffic', 'inspect requests', 'mock API', 'capture API calls', 'MITM', 'network debug', 'HTTP inspection'.
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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.
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Audit recent tool use across local Senpai OpenHands root and recursively delegated conversations without exposing tool arguments. Use for provider comparisons, tool-loop diagnosis, status-poll analysis, error rates, and latency reports.
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Use when writing Cortex-M NVIC ISRs, configuring priorities, handling HardFault, tail-chaining, or measuring interrupt latency. Not for the vector table itself: use baremetal-startup.
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Use when diagnosing scheduler latency, kmalloc vs vmalloc, page cache, meminfo under pressure, or OOM victim choice, or when reading kernel/sched, mm, or fs source.
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Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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Audit Convex performance for hot reads, write contention, subscriptions, function limits, OCC conflicts, latency, read amplification, and expensive reactive queries.
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Analyzes Perfetto traces to find the root cause of latency, memory, or jank issues in Android apps. Use when the user provides a Perfetto trace file and asks any question, ongoing investigation, or open-ended request to analyze its contents.
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Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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Design, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.
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Evaluate LLM agents and tool-using workflows—task success, tool accuracy, latency/cost, safety, and regression suites. Use when shipping agent features, comparing prompts/models, or debugging agent failures.
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High-throughput inference on FriendliAI — optimized serving engine for low-latency, cost-efficient LLMs.
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Evaluate AI agents rigorously — task benchmarks, success criteria, failure taxonomy, cost/latency tracking, and regression testing. Use when you need to know whether an agent actually works, not whether it demos well.
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