「collect」の検索結果

融合单元的范围判定与收益前置评估：判定「哪些计算该被融进同一个融合单元、边界画在哪、 融了值不值」。结构侧按融合单元构造规则切边界——函数边界即候选组；FA（含 BSA 等变种）为 禁融锚点；以 norm / rope / FA 为分界分区，全 Vec 区域优先试融；区域内有 Cube 时以 Cube 为 首算子向后包裹 Vec（Σvec 小于 cube 或遇下一 Cube 封口）；一轮之后纯 Vector 单元之间再迭代； MLP / MoE 以矩阵乘族（MatMul / BatchMatMul / GroupedMatmul）为界；若该段计算已由单个 融合算子承载，按规则 6 先确认载体是否已生效（不重复建单元）。 收益侧按计算单元利用率判型（memory_bound、*_vec|mac|mte2|mte3_ratio、cube_utilization）， 叠加带宽下限 / 区域占比 / 天花板法 / 地板先行 / Amdahl 传导校验给出 go/no-go。 当用户问「这几个算子能不能融成一个」「融合边界画在哪」「融多大范围」「融了值不值得」， 或 profiling-analyze 给出融合机会候选需要定范围与收益时使用；即使用户只说「这条链要不要 合成一个 kernel」「这里能不能省一次读写」也应触发。 **已经做完、实测收益却打平或为负时同样触发**——此时的任务不是接受"融合无收益"， 而是按事后复核做**等功忙计数对照**归因，并在缺陷落在融合核自身时**升级 operator-dev**。 边界：只出「融合单元草案 + 收益判定 + 否决理由」。不做采纳与选点（dit-perf-opt 依收益选 top 并派活）、不做采集与瓶颈定位（profiling-collect / profiling-analyze）、不写 kernel、不写 pattern、 不做使能与并行选型、不定义验收标准（引 accuracy-gate / perf-gate）。 由 profiling-analyze（融合机会候选）与 operator-dev（实现前的收益评估）调用。

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多模态模型自动优化流程总入口（编排层）：把一个三方框架托管的扩散模型从环境准备到无损/有损 优化自动跑完。任务判定后按 S0 环境准备、S1 kernel 融合优化（原 S1/S2 合并）、S3 并行通信、 S4 有损优化、S5 训练感知（预留，少步蒸馏 + SLA/QAT）、闭环复验路由到能力技能（env-install / framework-integration / profiling-collect / profiling-analyze / performance-optimization / dit-parallel-opt / dummy-run 等），检查各阶段产物与验收并回填经验槽位。 当用户需要对具体模型做框架接入、无损/有损加速、并行调优或确认收益时使用本入口；即使用户只提 模型名加"优化/加速/跑通/采profile"而未说框架或阶段，也应由本入口判定路由。仓库代码开发类任务 请走 dev-workflow。流程执行/验收按 workflows/optimization-flow.md，run-state + stage_gate 门禁。

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 human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG. Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow", "user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation design.

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Expert in Jungian analytical psychology, depth psychology, shadow work, archetypal analysis, dream interpretation, active imagination, addiction/recovery through Jungian lens, and the individuation process - grounded in primary sources and clinical frameworks. Activate on 'Jung', 'Jungian', 'shadow work', 'archetypes', 'dream interpretation', 'active imagination', 'individuation', 'anima', 'animus', 'collective unconscious', 'addiction', 'recovery', 'spiritus contra spiritum'. NOT for therapy or diagnosis (only licensed analysts diagnose), active psychosis, severe dissociation, or replacing the relational container of actual Jungian analysis.

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Fetch daily arXiv announcements (cs.RO), filter by research relevance, generate a structured markdown digest, and sync matched papers to a Zotero collection with arXiv PDFs attached.

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。

Collect and submit developer feedback to the LiveAvatar team. Use when: (1) A LiveAvatar integration is done, working, finished, or complete, (2) The user says "it works", "we're done", "that's it", "ship it", (3) The user is frustrated, stuck, or giving up — "this is broken", "this doesn't work", "I'm stuck", "I give up", "this is frustrating", "I want to report an issue", (4) The user explicitly asks to give feedback, report a bug, file an issue, or share a suggestion about LiveAvatar, (5) You've seen 3+ failed attempts at the same integration step, (6) You just finished writing or generating LiveAvatar integration code for the user.

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Use when taking a repo open source, or hardening one that already is. Run /oss-launch and it scans the repo (stack, existing files, git remote, secrets), asks only what it cannot infer, then generates a tailored OSS file collection (README, LICENSE, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, CHANGELOG, .gitignore, .github/ issue + PR templates, CI/CD). Also covers launch (GitHub metadata, badges, Show HN, Reddit, YouTube), release cadence (SemVer + CHANGELOG), and demo-GIF generation. Triggers on "open source this", "OSS checklist", "prep for launch", "make this repo public", "add a license / contributing / security policy", "Show HN", "release workflow", "star growth", "get contributors".

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。

Reddit automation skill collection. Supports authentication, content publishing, search & discovery, social interactions, and compound operations. Triggered when a user asks to operate Reddit (post, search, comment, login, analyze, upvote, save).

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。

Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

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.

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Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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。

Collection of AWS development skills covering CDK infrastructure as code, cost optimization, and serverless/event-driven architectures with integrated MCP servers.

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Use RoutIR — call a running endpoint with the client, stand up a local server that mixes locally-hosted services with services proxied from a remote master server, and extend RoutIR with new bi-encoders, rerankers, and document collections by writing a small config (and optionally one Python file referenced via `file_imports`). No checkout of the routir source is required.

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Use when a request needs external sources, current facts, listings, a supplied URL, papers, or library and API documentation. When using Firecrawl for the request, use firecrawl_search to find sources and firecrawl_scrape to read a supplied or discovered URL. Firecrawl also retrieves documents, collects structured data, and tracks website changes. Respect explicit source and tool preferences.

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Build bilingual, evidence-backed, SIEGER v2 customer intelligence reports for foreign-trade leads. Use when OpenClaw or a sales operator needs claim-level citations, decision gates, an industry-specific analysis lens, a non-fabricated verdict card, and sales angles that require approval before downstream outreach drafting.

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Run or resume a deliverable active-outreach workflow across lead discovery, screening, customer intel, and outreach email. Use when an operator or system pipeline needs durable run state, visible intermediate artifacts, evidence gates, report-bound human approval, and a final editable email draft that is never auto-sent.

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。

OpenClaw-native SIEGER-aligned customer intelligence. Use a structured evidence bundle to produce a bilingual report with claim-level evidence, decision gates, industry-specific analysis, and proposed sales angles that require explicit approval before email drafting.

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Find the first batch of foreign-trade prospect companies from public web, LinkedIn, and user-authorized data sources such as customs exports, trade show lists, association directories, CRM/Excel files, or historical customer data, then output a conservative, structured candidate list ready for lead screening.

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Generate conservative, editable English outreach drafts for foreign-trade sales from structured lead inputs and customer-intel signals. Use when an operator needs a first-touch or follow-up email draft that calls confirmed recent/customer-market signals from the customer-intel Skill, with subject options, review notes, and explicit reminders not to present unconfirmed facts as facts.

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Use when a foreign-trade operator already has a small customer list and needs conservative customer grouping, outreach angles, English subject lines, first-email drafts, follow-up cadence, and human review points before any sending happens.

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。

Normalize an OpenClaw lead bundle into a conservative lead-screening package with missing-field warnings, manual-review reasons, and customer-intel-ready payloads.

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。

Normalize and screen scattered foreign-trade leads into a conservative, structured lead pool that can flow into customer-intel research. Use when an operator has company names, websites, emails, or contact clues from search results and needs a standardized output with missing-field warnings, review flags, next-step suggestions, and customer-intel-ready JSON.

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。

OpenClaw-native version of the foreign-trade outreach email skill. Use structured operator input plus customer-intel source context to generate conservative first-touch or follow-up email drafts without overstating inferred facts or inventing recent customer events.

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。

用少量竞品网址和关注字段，生成适合人工复核的竞品变化汇总。首版默认复用 changedetection.io 做上游监控底座。

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。