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Reviews whether a design is SOUND — solving the right problem, derived from its stated goals and constraints — and emits ranked, evidence-backed findings, not edits. Read when asked to 'review this design', 'is this design sound', 'pressure-test this proposal', 'do a design review', 'does this solve the right problem', 'poke holes in this spec', 'should we build this', or to critique a proposal / spec / ADR / architecture or product decision. Do NOT read when the user wants to AUTHOR one of these — routing a new proposal is frame-a-proposal, a spec is write-a-spec, a decision record is record-a-decision, a postmortem is write-a-postmortem. Do NOT read for code review of a diff, or to fact-check individual claims (that is a correctness pass, a different job).

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Queue open microsoft/apm issues and fan them out through an isolated pool (default 2) of autopilot-issue-triage-worker sessions. Advisory only. Never implements, never assigns, never requests reviewers. Activate on a mixed issue list or queue-all when the job is triage. Works in a local session, Copilot App automation, Cloud Agent, Remote Agent, or Agentic Workflow.

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。

Queue open microsoft/apm pull requests (community fixes, PRs without an issue, or PRs that close an issue) and fan them out through an isolated pool (default 2) of autopilot-pr-triage-worker sessions. Advisory only. Never merges, never assigns, never requests reviewers, never runs autopilot-pr-review-worker. Activate on a mixed PR list or queue-open when the job is PR triage. Works in a local session, Copilot App automation, Cloud Agent, Remote Agent, or Agentic Workflow.

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。

Use Marin's fsutil CLI for local object-storage operations across GCS, CoreWeave S3, R2, and local paths. Use whenever a task needs to list, inspect, read, size, find, copy, move, synchronize, hash, browse, or remove data at gs:// or s3:// URLs from a checkout. Do not launch an Iris job solely to access object storage.

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。

Audit, diagnose, plan, and safely operate connected LinkedIn Ads accounts through the NotFair MCP, with an export-based fallback. Use for LinkedIn advertising, sponsored content, lead-generation forms, job-title or company targeting, campaign groups, creatives, conversions, lead quality, budgets, bids, or approved LinkedIn campaign changes.

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。

Use LinkedIn data for profile, company, job, post, feed, inbox, recruiting, and outreach work through the bundled LinkedIn MCP server. Use only for an explicit LinkedIn request.

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。

Codex-only team orchestration: run a named team of cooperating Codex workers with durable, script-managed state. MUST USE when the user asks Codex to create, run, coordinate, inspect, archive, or delete a team of agents/threads/sessions, or to work on something as a team in parallel. FIRST inspects the active tool surface (checking tool_search for deferred tools) and tells the user the route: native MultiAgentV2 agents (flat spawn_agent with task_name) when available, Codex App threads as the fallback, or a plain-subagent split when neither set exists. The main session is always the leader; members are defined by a concrete part, ownership area, or perspective - never a vague job role; a bundled cross-platform script writes the .omo/teams state plus an auto-generated member field manual. Use a team when the work is not perfectly isolated but parallelizing helps; use plain subagents when scope is perfectly isolated or the goal is ambiguous. Triggers: team mode, teammode, make a team, run as a team, team of agents, coordinate threads, parallel Codex threads, archive the team.

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。

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

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。

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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。

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.

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。

Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances

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。

Draft a tailored freelance proposal or platform profile bio from a real job posting/brief and the freelancer's actual background. Use when responding to a listing on Contra/Upwork/etc, or writing/updating a freelance profile. Never invents experience, clients, or credentials that weren't provided.

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。

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.

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。

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.

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。

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

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。

Use for agent-assisted Auto-FL optimization of an existing NVFLARE job in simulation, POC, or production. Do not use for code conversion, diagnosis-only work, or deployment setup.

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。

Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.

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。

Use this skill when integrating NVIDIA NeMo Fabric into a consumer application, service, evaluation harness, or platform through the typed Python SDK — translating the consumer's own application, job, or deployment config into an in-memory FabricConfig, choosing the single-invocation convenience API or an explicitly started runtime, validating with plan and doctor, and consuming normalized results, artifacts, and telemetry.

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。

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.

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。

Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.

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。

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.

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。

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.

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。

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.

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。

Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.

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。