「deep」の検索結果

将deep调研结果汇总为markdown报告，覆盖所有字段，跳过不确定值。

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。

读取调研outline，为每个item启动独立agent进行深度调研。禁用task output。

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。

Summarize deep research results into markdown report, cover all fields, skip uncertain values.

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 the user wants to design, launch, or optimize an in-app referral / invite / share-to-earn program — including reward structure, mechanics, fraud prevention, deep link setup, and viral coefficient measurement. Use when the user mentions "referral program", "invite a friend", "refer and earn", "share to earn", "viral loop", "viral coefficient", "K-factor", "double-sided rewards", "give X get X", "referral rewards", "invite link", "share sheet", "Branch referrals", "in-app invites", or "how to make my app go viral". For deep link infrastructure that referrals depend on, see attribution-setup. For organic content-driven virality (UGC, creator), see creator-ugc-marketing.

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。

使用AI大模型根据文字、首尾帧图片或图片/视频/音频参考素材生成带对白和环境音的视频。适用于文生视频、图生视频、首尾帧过渡和多模态参考视频生成，通过 Deep Code Plus 试算积分、确认后上传素材并生成视频。

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。

Reviews and improves another DeepCode skill's SKILL.md description field, and guides Agent Skill installation into user or project .agents/skills roots. Use when the user asks to digest a skill, install an Agent Skill, install a skill to user/project scope, or says "消化技能" or "安装 agent skill".

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 creating a DeepSeek Harness plugin, choosing public names for a new external DSH plugin, validating a dsh-plugin.naming.json manifest, checking reviewed central registrations for known conflicts, or creating a workspace package inside the deepseek-harness repository. Covers the full workflow from repository-mode and plugin-form selection through separate offline naming validation, optional online registry lookup, and package validation. Routes tool, LLM adapter, hook, service, and configuration forms to their corresponding references while separating upstream-monorepo rules from external-package rules. For an existing plugin crossing Harness versions, use this Skill's built-in version-adaptation workflow before implementing against the exact target contract.

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 writing or reviewing tests for DeepSeek Harness plugins, external DSH plugin packages, or package changes in the deepseek-harness repository. Select the minimum sufficient levels from unit tests, coverage, real-API end-to-end tests, snapshots, Web tests, real compositions, and built-artifact smoke tests. For Harness version migrations, use this Skill's built-in seven-touchpoint test workflow and validate the exact target-version runtime.

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。

Qianfan Deep Research Agent for complex research tasks. Combines information retrieval, multi-source analysis, content synthesis, and report generation. Use when user needs in-depth research, analysis reports, or comprehensive investigation on complex topics.

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 evaluating Seth Klarman-style deep value and distressed opportunities: extreme margin of safety, forced sellers, liquidity discounts, complexity, downside protection, and patience.

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。

结构化深度讨论 Skill，用于与用户进行多轮问题分析和方案设计。当用户描述一个问题现象、故障表现、 技术困惑、方案选择困难，或明确说"讨论一下"、"帮我分析"、"我遇到一个问题"、"你觉得怎么样"、 "帮我想想"、"我在纠结"时，必须使用本 skill。当用户提供了一段描述（可能附带截图）并期望深入分析 而非直接给答案时，也应触发本 skill。即使用户只是抛出一个现象描述没有明确提问，也要使用本 skill 来引导结构化思考。不要在简单的事实查询（"X是什么"）或明确的执行指令（"帮我写个脚本"）上触发。

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。

Implement mHC (Manifold-Constrained Hyper-Connections) for stabilizing deep network training. Use when implementing residual connection improvements with doubly stochastic matrices via Sinkhorn-Knopp algorithm. Based on DeepSeek's 2025 paper (arXiv:2512.24880).

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-agent research orchestration: split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file. Use for systematic web/document research, competitive or industry analysis, batch link/dataset processing, and long-form evidence synthesis. Triggers: "深度调研", "deep research", "wide research", "多 Agent 调研", "系统调研".

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。

Respond to a brand or executive impersonation incident — deepfaked executives, cloned support lines, fake apps, spoofed domains, or AI-generated scam content wearing your name. Use when a deepfake of a leader is circulating, customers report a fake version of your product or support channel, or to prepare the impersonation playbook before it happens. Produces an incident response: verification protocol, takedown sequencing by platform, customer and public communications, and the hardening plan. For general crisis comms use press-release/pm-crisis skills; for security incidents inside your systems use security-incident-response.

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。

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

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。

INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.

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。

INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

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。

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.

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。

Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate 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。

Decides whether and how to polish a draft genome assembly to raise consensus accuracy (QV) with read-type-matched tools - Racon and medaka (ONT consensus), dorado polish, Polypolish and pypolca (Illumina, repeat-aware), Pilon (legacy short-read), NextPolish/NextPolish2, Hapo-G (haplotype-aware), ntEdit, and DeepPolisher/PEPPER-Margin-DeepVariant for human. Covers the do-not-polish-HiFi rule, the medaka basecaller-model footgun, held-out Merqury QV as the only honest stop signal, and the haplotype-collapse trap. Use when correcting homopolymer indels or residual SNPs in a long-read assembly, deciding if a HiFi assembly needs polishing, or choosing an ONT vs hybrid vs short-read polishing chain.

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。

Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.

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。

Work with hyperspectral and multispectral images in MATLAB. Covers reading/writing (ENVI, NITF, TIFF, Sentinel-2, Landsat, ASTER), ECOSTRESS spectral libraries, processing (calibration, atmospheric correction, denoising, sharpening, dimensionality reduction, endmember extraction, unmixing, target/anomaly detection, spectral indices, segmentation), labeling (Spectral Image Labeler app, ground truth objects, automation algorithms), and deep learning (pixel classification CNNs, unmixing autoencoders, transfer learning). Use when reading, writing, processing, analyzing, classifying, labeling, or applying deep learning to hyperspectral or multispectral images.

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。

Write, review, and improve blog posts for the Sentry engineering blog following Sentry's specific writing standards, voice, and quality bar. Use this skill whenever someone asks to write a blog post, draft a technical article, review blog content, improve a draft, write a product announcement, create an engineering deep-dive, or produce any written content destined for the Sentry blog or developer audience. Also trigger when the user mentions "blog post," "blog draft," "write-up," "announcement post," "engineering post," "deep dive," "postmortem," or asks for help with technical writing for Sentry. Even if the user just says "help me write about [feature/topic]" — if it sounds like it could become a Sentry blog post, use this skill.

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。

Deep autonomous diagnosis of a failed flow run. Provide environment, flow, and run IDs. Use when the user asks to diagnose or deeply investigate a specific failed flow run.

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。