「fuse」の検索結果

LangFuse LLM observability integration for tracing, analytics, and cost tracking

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 Scenario generation is blocked, refused, or returns a moderation or sensitive-content error, when a video is rejected after rendering or its audio track is flagged, when an output is flagged for likeness to a real person or for IP and copyright detection, when the same prompt passes on one model and fails on another, or when a team's own characters, weapons, or props get flagged. Keywords: blocked prompt, content moderation, refused generation, provider filter, false positive.

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。

When your bar comes asking "show me how you billed AI-assisted work" — and ABA 512, Florida 24-1, California, New York, and DC all have opinions out — you need an artifact that survives review. billable-time produces it. From your Claude Code session logs, it drafts reviewable time entries plus a printable HTML audit packet with: SHA-256 chain of evidence (source files + matter.yml + active disclosure pack + verifiable artifact self-hash), attorney identity and signature block, a bar-opinion disclosure pack with starter language for five jurisdictions, and content-aware deterministic narratives derived from filename and tool shape — never from prompt text by default. The tool refuses to bill on its own. --strict mode refuses to ship the artifact if any audit invariant fails (broad routes, missing attorney, missing/unverified disclosure). Comes as a Node CLI and a self-contained browser version (no backend; JSONL never leaves the page). 15 invariant tests verify the contract. AGPL-3.0.

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 writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline injection.

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。

Weekly, browser only when a source refuses to be fetched. Counts unpublished calendar entries per property by folding the published ledger against the calendar, and where every property is above its runway threshold it records the counts and exits, which is the normal run. Where a property is low it researches the next block from rank evidence, the topic map, and live result sets, refuses any keyword already targeted, and appends fully specified entries after the last existing block. It is the only writer of the calendar and it never modifies, reorders, or renumbers an existing entry.

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 for Hugging Face Diffusers tasks: pipeline inference, schedulers, adapters/loaders, training recipes, modular pipelines, conversion helpers, CLI checks, and repo maintenance.

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。

Implement comprehensive observability for LLM applications including tracing (Langfuse/Helicone), cost tracking, token optimization, RAG evaluation metrics (RAGAS), hallucination detection, and production monitoring. Essential for debugging, optimizing costs, and ensuring AI output quality. Use when ", llm-monitoring, tracing, langfuse, helicone, cost-tracking, ragas, evaluation, hallucination-detection, prompt-caching" mentioned.

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 the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over writing ad-hoc Python scripts for generation tasks.

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。

AgentCore の Observability ナレッジ。OpenTelemetry 必須4点セット（OTEL_EXPORTER設定・StrandsTelemetry・traceable デコレータ・Langfuse連携）、EMF メトリクス、Jaeger/コンソールエクスポーターでのトレース確認、トレースが出ない・繋がらない場合のデバッグ手順等

LLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production 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。

Geração de imagens com tecnologia de ponta usando Stable Diffusion via HuggingFace Diffusers. Use quando gerar imagens a partir de prompts em texto, realizar tradução de imagem para imagem, inpainting ou construir pipelines de difusão customizados.

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。

Reimagine a supplied photo as a tactile fuse-bead mosaic keepsake on a spacious editorial canvas. Use for 拼豆纪念画、照片拼豆效果、fuse bead photo art or perler-style visual mockups, not exact craft patterns or bead-count charts.

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。

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

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。

Query aggregate Langfuse cost, volume, latency, and model telemetry efficiently with Metrics API v2. Use for dashboards, rollout checks, usage summaries, and cost or latency comparisons; use Observations API v2 instead when individual rows are actually required.

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。

Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

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。

Socratic mentoring for junior developers and AI newcomers. Guides through questions, never answers. Triggers: "help me understand", "explain this code", "I'm stuck", "Im stuck", "I'm confused", "Im confused", "I don't understand", "I dont understand", "can you teach me", "teach me", "mentor me", "guide me", "what does this error mean", "why doesn't this work", "why does not this work", "I'm a beginner", "Im a beginner", "I'm learning", "Im learning", "I'm new to this", "Im new to this", "walk me through", "how does this work", "what's wrong with my code", "what's wrong", "can you break this down", "ELI5", "step by step", "where do I start", "what am I missing", "newbie here", "junior dev", "first time using", "how do I", "what is", "is this right", "not sure", "need help", "struggling", "show me", "help me debug", "best practice", "too complex", "overwhelmed", "lost", "debug this", "/socratic", "/hint", "/concept", "/pseudocode". Progressive clue systems, teaching techniques, and success metrics.

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。

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

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。

Time-of-Check / Time-of-Use (TOCTOU) race condition exploitation methodology across binary, kernel, filesystem, web, and container layers. Covers symbolic-link races (open/access/stat split), file-descriptor races, fopen/realpath traversal races, /proc and procfs races, FUSE-backed slow-fs races to widen the window, ptrace and signal races, kernel double-fetch / userspace pointer races, container/runc/symlink escape primitives, kubernetes admission/authz TOCTOU, web auth-vs-authz TOCTOU, JWT-claim TOCTOU at gateway vs service, payment/idempotency races, and modern race-amplification techniques (single-packet attack, slow loris, FUSE pause, cgroup freeze, scheduler shaping). Use when you've identified a 'check then act' pattern in code, when fuzzing for race conditions, or when exploiting concurrency bugs in privileged binaries / kernel / orchestrators.

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。

Set up and run RuView mmWave / FMCW radar sensing — ESP32-C6 + Seeed MR60BHA2 (60 GHz, heart rate / breathing rate / presence) and HLK-LD2410 (24 GHz, presence + distance), plus mmWave↔WiFi-CSI sensor fusion (48-byte fused vitals, MR60BHA2/LD2410 auto-detect, v0.5.0+). Use when the deployment includes a millimetre-wave radar alongside or instead of WiFi CSI.

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。

Scaffold a custom AI agent harness via `metaharness new {name} --template {id} --host {id}`. Defaults to DRY-RUN (no writes) unless --confirm is passed. Refuses to write to the calling repo root or anywhere inside it. Honors ADR-150 architectural constraint + ruflo's "destructive-action confirmation" pattern.

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。

.NET / C# 二进制逆向。当目标是 .NET assembly（PE 头含 CLR、.exe/.dll 托管程序）、C# 编译产物（含 NativeAOT）、红队 Sharp* 工具（Rubeus / SharpHound / SharpHound 等）、.NET 混淆程序（ConfuserEx / SmartAssembly / Babel / Eazfuscator）、.NET loader / info-stealer / 套壳 malware 时使用。优先用 dnSpyEx + de4dot，需要 AI 直接操作时联动 dnSpy MCP。不用于纯 native 二进制（走 reverse-engineering / ida-reverse）。

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。

Infuse learning strategies and metacognition into a concept-centered OpenMAIC classroom as a parallel goal. Use when the user wants students to learn how to learn while studying a subject: retrieval practice, self-explanation, prediction before feedback, monitoring understanding, deliberate review, or productive failure. Do not use when learning science or study skills should be the standalone subject of the lesson.

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。

Infuse social-emotional learning (SEL) into a concept-centered OpenMAIC classroom as a parallel learning goal. Use when the user asks to add emotional awareness, self-management, empathy, collaboration, relationship skills, resilience, or responsible decision-making to a subject lesson. Do not use when SEL itself should be the standalone content of a counseling, advisory, psychology, or homeroom lesson.

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。

Unified LLM torch-profiler triage skill for `sglang`, `vllm`, `TensorRT-LLM`, and `TokenSpeed`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server when supported and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.

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。