「loops」の検索結果

This skill should be used when a model gets read-write control over its own live context window instead of a harness-scheduled compaction policy: the context exposed as an editable file the model rewrites with code tools, model-driven eviction and in-place updates, the harness invariants that keep self-editing safe (pinned prefix, edit gate, edit receipts, budget readouts, rollback on overflow), the prefix-cache cost of mid-context edits, and steering or training the model's own context-editing strategy. Route note content and fixed-threshold summarization to context-compression, cache-stable layout under harness control to context-optimization, file offloading to filesystem-context, and loop governance to self-improvement-loops.

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。

Automated PR review and CI auto-fix for GitHub and GitLab using the Composio CLI. Pulls diffs, fetches failing job logs, posts review comments, and loops fix commits until checks go green.

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。

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

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Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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。

Scan recent merges to main (and noteworthy direct commits) since a given window (last tag or date in state). Extract titles, labels, types, linked issues, and signals for breaking changes or security. Produces structured input for a release notes drafter. Use in changelog-drafter loops.

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。

Scan package manifests and lockfiles for outdated packages and known CVEs. Groups updates by risk (patch, minor, major). Use in dependency sweeper loops.

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。

An advanced skill for L3 autonomous loops. When the token budget nears exhaustion, the agent analyzes its ROI and autonomously drafts a negotiation request for a budget increase rather than silently failing.

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。

Triage open pull requests for CI status, review comments, and merge readiness. Use in PR babysitter loops. Respects project review norms and required checks.

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。

Scan recent merges to main for follow-up cleanup: TODOs, deprecations, broken doc links, stale flags. Use in post-merge cleanup loops.

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。

Parse CI failures, identify failing job/step, classify as flake, regression, env, or config. Use in CI sweeper loops before any fix attempt.

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 when a tool-calling agent does not call a tool, sends wrong arguments, loops without stopping, or needs a function schema. Guides a four-branch diagnosis and five-step schema repair. Do not use for framework-specific, MCP-server, or production-observability questions.

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Build agents and multi-agent systems with the Swarms framework — the Agent class, tools, autonomous loops, memory, and the 15+ multi-agent architectures (SequentialWorkflow, ConcurrentWorkflow, GraphWorkflow, HierarchicalSwarm, SwarmRouter, and more). Use whenever writing, reviewing, or debugging code that imports `swarms`.

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。

Apply seamless infinite marquee loops using duplicated items.

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。

Profile, diagnose, and improve Three.js or WebGL game performance without regressing gameplay. Use for frame-time drops, CPU/GPU pressure, draw calls, texture and geometry budgets, animation loops, adaptive quality, mobile performance, and browser performance verification.

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Profile, audit, and optimize frontend page performance with emphasis on animation work, memory-leak risks, long-session slowdowns, CSS animations, canvas/WebGL requestAnimationFrame loops, marquees, skeletons, GSAP/Three/Matter effects, timers, listeners, and observers. Use when the user asks to make animations performant, pause offscreen animations, look for memory leaks, profile pages that slow the computer over time, fix janky scrolling, reduce CPU/GPU use, or repeat the "only play in view" optimization on React/Vite/Next/frontend pages using Codex Browser.

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Use this by default for non-trivial multi-step work that needs persistent planning, progress tracking, or durable notes on disk. Trigger when a task will likely span multiple tool calls, research steps, verification loops, or enough context that the plan should not live only in transient chat memory.

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Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including the generic Vector type, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.

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。

Diagnose gh-aw failures using logs and audits; follow the shared strategy for patches and permitted active debug loops.

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Deep consistency audit of the entire repository — launches 4 parallel specialist agents to find factual errors, code bugs, broken references, count mismatches, and cross-document inconsistencies, then fixes all issues and loops until clean. Make sure to use this skill whenever the user wants a comprehensive repository-wide check — not a targeted review of a single file. Triggers include: "audit", "deep audit", "find inconsistencies", "check everything", "run a full audit", "are there any broken references", "check the whole repo", "something feels off", "run the audit loop", or after making broad changes across multiple files.

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 fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool call/result correlation, and common failure patterns.

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。

Run long or failure-prone Claude Code tasks as bounded, resumable loops with a durable state machine, attempt budget, and verification evidence checkpoint.

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 existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.

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Use this skill when the user is hands-on deploying an in-bundle DOCA service container (Argus, DMS, Firefly, or UROM service) on a BlueField — kubelet standalone watching a static-pod manifests directory, YAML pod-spec drop, kubelet status / ENTRYPOINT logs / per-service liveness, smoke-before-bulk, and the layered error taxonomy (pod-spec, scheduling, image pull, runtime, mount, network, version, host). Trigger even when the user does not say "container deployment" — typical implicit phrasings include "how do I run my built service on the BlueField?", "where do I drop the pod-spec YAML?", "pod stuck in Pending / ImagePullBackOff / CrashLoopBackOff", "container Running but service isn't ready", "pod restart-loops after edit", or "DMS and Firefly together". Refuse and route elsewhere for per-service config schemas, DOCA install, library-API questions, external NVIDIA services (BlueMan, HBN, SNAP, Virtio-net), or full Kubernetes-cluster ops — those belong to other skills.

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。