「jobs」の検索結果

Use this skill when administering, operating, or debugging a GoClaw gateway through the GoClaw CLI/runtime package. It covers CLI discovery, safe command inspection, gateway health/config diagnostics, agents, skills, MCP/tools, runtime packages, credentials, traces, sessions, channels, providers, cron/jobs, and troubleshooting. Always inspect the live `goclaw --help` output first because command availability is version-dependent.

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Look up official macroeconomic releases, central-bank policy rates, release calendars and FX rates for 22 currencies through the FXMacroData MCP server. Use for questions about CPI, GDP, jobs data, rate decisions, bond yields or what data is due next, when the answer should come from the published number rather than news coverage. Remote server at https://mcp.fxmacrodata.com/mcp; USD works without a key.

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Automate Klaviyo tasks via Rube MCP (Composio): manage email/SMS campaigns, inspect campaign messages, track tags, and monitor send jobs. Always search tools first for current schemas.

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Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for current schemas.

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Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo-rl-auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.

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Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.

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How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.

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Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI. Covers ephemeral vs long-lived RayCluster modes, iterating on runs, and debugging hung or failed training jobs.

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GitHub Actions CI/CD pipeline configuration for Golang projects — workflow files for test, lint, SAST, coverage and vulnerability-scan jobs, Dependabot and Renovate config files, GoReleaser release pipelines, Docker build/push, repository security settings, and AI-driven PR review. Use when setting up or improving Go project CI, writing or fixing `.github/workflows/*.yml`, adding a linter or security scanner as a pipeline job, wiring automated dependency-update bots, or adding quality gates. Covers wiring tools into a pipeline, not the analysis they perform: do NOT use for choosing or interpreting security findings (→ See `samber/cc-skills-golang@golang-security` skill) or for choosing, upgrading, or auditing dependency versions (→ See `samber/cc-skills-golang@golang-dependency-management` skill).

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Read LinkedIn for financial research through opencli: the user's feed, and job search with location, seniority, job type, remote, company, and date filters. Use this skill whenever the user wants to check their LinkedIn feed, see what professionals or analysts are posting about a company, market, or earnings, gauge professional sentiment, or search finance and trading jobs on LinkedIn. Read-only: it cannot post, like, comment, connect, message, or apply.

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Build research-grounded personas with goals, frustrations, and behavioural patterns. Use when decisions need a consistent user reference. For one session's emotional snapshot use `empathy-map`; for motivation framing use `jobs-to-be-done`.

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Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.

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Runs quantum computing workflows on AWS through Amazon Braket — discovering devices (QPUs and simulators) and their availability, building gate-model circuits and analog Hamiltonian programs, submitting quantum tasks, program sets and hybrid jobs, looking up prices, and capping spend with spending limits. Applies to any request about quantum computing, quantum hardware, quantum simulation, AHS, OpenQASM, or running a quantum algorithm on AWS.

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Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

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Routes serverless requests to a compute form factor and the skill that owns it. Applies first to Lambda, API Gateway, EventBridge, Step Functions and event-driven work while the compute, orchestration or integration choice is open — API backends, webhooks, jobs, cron, queue/stream consumers, durable workflows, event routing, Step Functions vs Durable Functions, and Pipes vs native event source mappings for queues and streams. Decides Event Functions on-demand vs Managed Instances, Web Functions, MicroVMs, EventBridge rules/Scheduler/Pipes/bus, HTTP vs REST API. Owns shared Lambda references (cold starts, SnapStart, concurrency, event sources, function URLs, throttling, troubleshooting). Defers to the specialized skill once a request commits to one form factor or service, or describes its defining feature, even unnamed (durable checkpointing and automatic step retries, Firecracker sandbox, managed instances, Node.js server). Not for EC2, ECS/EKS/Fargate or Amplify hosting.

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Troubleshoots and debugs AWS Clean Rooms collaboration issues related to IAM roles, S3 bucket policies, KMS keys, Lake Formation permissions, and CloudWatch logging for custom ML model training and inference jobs. Use when a customer reports permission failures, access errors, or log publishing issues in Clean Rooms.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze and design an AssemblyAI architecture spanning pre-recorded jobs, Streaming v3, LLM Gateway, callbacks, queues, retention, and audit evidence. Use when performing system design or review. Trigger with "AssemblyAI architecture" or "design AssemblyAI pipeline".

日本語の概要は準備中です。原文の説明を表示しています。

Improve Adobe integration latency and throughput from measurements while preserving correctness, policy, and spend boundaries. Use when the task requires adobe measured performance tuning. Trigger with "tune Adobe performance", "reduce Firefly latency", or "optimize PDF jobs".

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Derive a safe Adobe workload envelope from synthetic load, queue/backpressure behavior, current service constraints, spend, and output correctness. Use when the task requires adobe capacity and load envelope. Trigger with "load test Adobe", "scale Firefly jobs", or "Adobe capacity plan".

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Build deterministic CI gates for Adobe adapters without giving fork jobs credentials or spending against live creative/document APIs. Use when testing integration changes. Trigger with "test Adobe in CI", "Adobe contract tests", or "gate Adobe deploy".

日本語の概要は準備中です。原文の説明を表示しています。

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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Builds the backend of an app clone: auth, database migrations and access rules, payments with Stripe, email, background jobs and third-party integrations through official APIs only, plus a security checklist. Use when the user says "add login", "set up auth", "wire up the database", "add payments", "connect Stripe", "add Google Calendar", "send emails", "backend for my clone", or when /replica-build is running on fake data.

日本語の概要は準備中です。原文の説明を表示しています。

Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", "test assumptions", "innovation accounting", "build-measure-learn", "minimum viable experiment", "should we pivot", "test a business idea cheaply", or "build the smallest version first". Also trigger when deciding what to include in a first version, measuring startup progress, or evaluating whether to change direction on a product bet. Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done.

日本語の概要は準備中です。原文の説明を表示しています。

Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user-research questions, interpreting ambiguous feedback, or designing customer-discovery that avoids false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.

日本語の概要は準備中です。原文の説明を表示しています。