556 件 ・ 関連度順
概要と使いどころ
固件 / IoT 渗透链。从拿到一坨 .bin / .img 开始，闭环走完逆向 → 提取 → 模拟 → 利用。 方法论遵循 OWASP FSTM 九阶段；工具链以 binwalk v3、unblob、EMBA、Firmadyne、AFL++ 为主。 适用场景：路由器/摄像头/智能家居固件审计、固件升级包逆向、IoT CVE 复现、嵌入式 0day 挖掘。 触发关键词：固件、firmware、IoT、binwalk、unblob、UART、JTAG、squashfs、UBI、JFFS2、Firmadyne、QEMU 全系统仿真、EMBA、固件渗透、路由器固件、嵌入式漏洞利用、bootloader、NVRAM、FAT、firmware analysis toolkit。
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。
Discover the active AWS profile, region, account, and caller identity before SageMaker or AWS work. Use when local AWS context is needed or unspecified; do not use for cloud-agnostic model planning.
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 a production SageMaker endpoint with autoscaling, CloudWatch alarms, and tags. Use after the serving image and IAM role are known; use the deployment planner first when architecture is undecided.
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。
Verify or select a SageMaker execution role before creating models, endpoints, or training jobs. Use when a role ARN is missing or IAM access fails; inspect existing roles before proposing role creation.
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。
Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.
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。
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
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。
Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation 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。
Create an isolated Python environment with a compatible Python version and boto3 for SageMaker or AWS automation. Use before executing AWS Python code; do not use for general Python environment 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。
Select and verify the current region-specific serving container URI for a SageMaker model deployment. Use after the deployment pathway is chosen and before endpoint code; never infer an image URI from memory.
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 evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub.
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。
基于ark_sdk的大模型训练任务自动化工具。帮助用户通过自然语言创建并提交方舟模型训练任务，支持用户自备数据的SFT训练、RFT+GRPO和直接GRPO策略，并引导用户完成训练、状态跟踪与评估闭环。使用场景：当用户需要进行大模型SFT监督微调、RLHF训练、GRPO训练、RFT训练，或需要自动化训练流程时触发。
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。
Create distinctive Photo Relic editorial artworks from user-provided photographs: preserve the real photo and pair it with a recognizable, artful abstract relic shaped like memory, modern printmaking, quiet Eastern restraint, and source-derived light. Use when the user asks to turn a photo into minimal art, photographic relic posters, abstract editorial photography, gallery-like photo posters, Douyin-ready art series covers, graphic photo abstraction, afterimage compositions, or similar visual treatments while keeping the original photograph truthful.
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。
Explore image concepts and make faithful edits of accepted references for 3D or creative production, with explicit model choices and visual review.
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。
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
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 Project Manual specification sections from accepted project requirements using CSI MasterFormat identities, three-part sections and review flags. Use for design requirements; use product-cut-sheet for source-faithful product entries and epd-to-spec for EPD requirements.
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。
Create a source-faithful single-product FF&E cut sheet or spec book entry from a supplied PDF or product URL. Preserve selected identity and source limits; use ffe-spec-book for a complete FF&E book and spec-writer for Project Manual requirements.
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。
Forecast construction project cash flow. Project income and expenses, identify funding gaps, and optimize payment timing for improved financial management.
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。
Route Chinese-BERT-wwm model loading, model selection, benchmark data, and troubleshooting workflows for HFL Chinese BERT-family resources.
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。
根据主题、正文或已有标题生成并筛选小红书标题，提供改写和 A/B 方案，同时检查账号调性、真实性以及正文能否兑现标题承诺。用户要起标题、改标题、套用标题模式或检查标题时使用。 English: Generate and filter Xiaohongshu titles from a topic, draft, or existing title, offering rewrites and A/B options while checking voice, truthfulness, and whether the content fulfills the promise. Use to write, improve, pattern-match, or review titles.
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。
Build a Seedance 2.0 brand, product, website, app, shop, or personal-project promo from verified assets and claims. Use when the user needs a truthful 4–15 second reel with a clear hook, product proof, controlled typography, camera, sound, CTA, and provenance-aware asset roles; not for inventing claims or imitating unavailable brand marks.
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。
Turn real product images and a verified brief into a clean Seedance 2.0 minimalist product film. Use for 4–15 second e-commerce or launch ads that require faithful product color/material/geometry, independent anchor-image roles, product-specific actions, restrained camera, readable integrated copy, sound, and a stable closing frame; not for talking-head ads or invented product features.
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 users provide YouTube, Bilibili, or X/Twitter lecture URLs and want reader-first Chinese LaTeX/PDF notes with source-faithful claims, fluent authored prose, and verified teaching figures, especially requests phrased as lecture notes, 视频转PDF, 课程笔记, 讲义, YouTube笔记, B站笔记, X/Twitter lecture notes, or BV号; do not produce transcript dumps, quota-padded prose, or screenshot galleries.
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。
Performs symbolic execution to detect potential errors by exploring execution paths, solving path constraints, and generating test inputs. Use when you need to analyze code for bugs like null dereferences, division by zero, buffer overflows, or assertion violations. Also use to generate test inputs that exercise different code paths, find edge cases, or explore all reachable program states. Supports Python, Java, and C/C++ through manual symbolic execution techniques and integration with tools like KLEE, angr, Z3, and Symbolic PathFinder.
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。