4,001 件 ・ 関連度順
概要と使いどころ
Use when preparing hardest high-leverage work for a stronger or scarce model, building a ranked execution queue, asking for fable prep, frontier prep, round-collapse, wave queue, or model-upgrade readiness across code, design, copy, marketing, or architecture work.
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Generate advertising images automatically from a product URL + brand profile. ✅ USE WHEN: - User provides a product URL (e-commerce link) - Want automated product scraping + image generation - Have a brand profile to apply (70+ brands available) - Need funnel-stage targeting (awareness/consideration/conversion) - Want AI to auto-select model, scene, lighting based on brand ❌ DON'T USE WHEN: - User provides local product image file → use morpheus-fashion-design - Don't need a person in the image → use nano-banana-pro - Want manual control over model, scene, packs → use morpheus-fashion-design - Already have hero image, need variations → use multishot-ugc - Need video output → use veed-ugc after image generation INPUT: Product URL + brand name (optional) + funnel stage (optional) OUTPUT: PNG advertising image with product + model
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Configure, build and test dynamic-neural-field-composer. Use whenever you need to compile the library, run the GoogleTest suite, verify a change builds, or reproduce a test failure.
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Read the branch diff in dynamic-neural-field-composer and print a PR title and description in chat, filling the repo's pull request template. Use when asked to write, draft or suggest a PR title/description, or "what should the PR say".
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End-to-end workflow for resolving one GitHub issue in dynamic-neural-field-composer - branch, TDD, build, review, docs, PR. Use when asked to fix, implement, or close a specific issue number.
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Read the uncommitted diff in dynamic-neural-field-composer and print conventional commit message(s) for it, grouped by logical change. Use when asked to write, suggest or draft a commit message, or "what should I commit this as".
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Verify documentation keeps up with a code change in dynamic-neural-field-composer - Doxygen on public API, the matching wiki page, and stale references elsewhere. Use before opening a PR, or when asked whether docs need updating.
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Design asynchronous messaging patterns in Azure using Service Bus, Event Hubs, Event Grid, and Storage Queues. Use this skill when users ask for queueing, pub-sub, event streaming, retries, or decoupled system design. Covers delivery semantics, dead-lettering, idempotency, and monitoring.
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Shape reusable library APIs for Compose modules. Design public composables, split sample-app code from reusable library code, review naming and extensibility.
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依据 RGPD 第 28 条、EDPB 07/2020 和 02/2024 号指南、2021 年标准合同条款（CCT）（执行决定 2021/914）、EDPB 01/2020 号建议（Schrems II 之后的补充措施）以及《欧盟条例 (UE) 2024/1689》（《人工智能条例》），对数据处理协议（DPA）进行系统性分析。生成逐条款的 结构化报告（18 个条款：13 项强制 + 5 项补充），附 🟢/🟡/🔴 诊断、可直接插入的 救济条款、国际传输的详细分析、《人工智能条例》核验以及应向供应商提出的问题。 触发词："analyse de DPA"、"audit DPA"、"vérifier un DPA"、"DPA fournisseur"、 "data processing agreement"、"art. 28 RGPD"、"sous-traitant RGPD"、"négociation DPA"、 "review DPA"、"conformité contrat sous-traitance"。
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AI compliance and policy engine — evaluate scan results against OWASP, NIST, SOC 2, ISO 27001, CMMC, EU AI Act, AISVS v1.0, and related frameworks. Generate SBOMs and compliance reports. Use when: "compliance report", "NIST", "SOC 2", "ISO 27001", "OWASP", "EU AI Act", "AISVS", "generate SBOM", "policy check".
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AI compliance analysis for EU AI Act, ISO 42001, NIST AI RMF, GDPR, OECD, financial services regulations (SEC, FCA, FINRA, DORA, MiFID II), and other frameworks. Use when asked to generate a compliance checklist for an AI tool or use case, determine if a risk assessment is required, score an AI tool's risk level, identify where an AI tool or use case could run afoul of regulatory requirements, perform a gap analysis, recommend remediation steps, assess a vendor, draft an acceptable use policy, map training requirements, or review jurisdiction-specific AI rules. Triggers on phrases like "compliance checklist", "risk assessment", "risk score", "does this comply", "EU AI Act", "ISO 42001", "NIST AI RMF", "AI governance", "gap analysis", "vendor assessment", "acceptable use policy", "MNPI", "AI training requirements", or any request to evaluate an AI tool or use case for regulatory compliance or risk.
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Integration of Kahneman's heuristics research with Klein's expertise studies on intuitive decision-making
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Use when stuck on a hard problem, decomposition is unclear, a related problem is needed, or explicit looking-back review will improve transfer. NOT for routine execution with a known procedure, rote checklist work, or domain questions that need specialized subject matter expertise instead of heuristic search.
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Comprehensive reference for inter-process communication mechanisms -- every way processes can talk on a computer. Covers sockets (TCP, UDP, Unix domain), WebSockets, SSE, pipes (named/anonymous), shared memory (mmap, shm_open), message queues, signals, D-Bus, XPC (macOS), gRPC, REST, stdin/stdout, file-based coordination, and clipboard. Performance benchmarks, platform availability, and code examples for each. Special focus on which IPC works best for AI agent coordination. Activate on: "IPC", "inter-process communication", "process communication", "how do I talk between processes", "Unix socket vs TCP", "shared memory", "named pipe", "WebSocket vs SSE", "gRPC vs REST", "agent coordination IPC", "XPC service", "message passing", "stdout pipe", "D-Bus", "mmap". NOT for: distributed systems design (use distributed-systems), network protocol design (use networking), message queue infrastructure like Kafka (use data-pipeline-engineer).
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Use when building Grafana dashboards backed by Prometheus, Loki, or Tempo, designing PromQL/LogQL queries, wiring template variables, setting alert rules, building SLO dashboards, or maintaining dashboards as code. Triggers: rate() vs increase() confusion, irate vs rate, label_replace, recording rules, alerting rule expressions, multi-dimensional template variables, ad-hoc filters, dashboard JSON model, provisioning via Terraform/grafonnet, p99 / histogram_quantile usage. NOT for Datadog/New Relic dashboards (vendor-specific), Grafana plugin development, or Loki ingestion pipeline tuning.
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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.
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Task claiming and file-ownership tracking for parallel execution: agents claim tasks from a shared queue with ownership enforced. Use during parallel phases with multiple simultaneous agents.
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Set up parallel agent team for COMPLEX tasks. Creates worktree structure, generates ownership map, and initializes task queue. Use when orchestrator decomposes a task into parallel subtasks, or when asked to "set up team", "parallel setup", or "configure worktrees".
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Develop, challenge, and document Chisel processor microarchitecture designs before implementation. Use when discussing or writing design documents for processor pipelines, queues, issue logic, rename, ROB, LSU, caches, MSHRs, forwarding, wakeup, replay, flush, privilege, exceptions, or other cycle-accurate hardware mechanisms; when converting design conversations into stable Markdown specifications; or when reviewing a proposed Chisel CPU design for correctness, timing, area, verification cost, and cross-document consistency.
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Look up company/business data through Openapi - Italian companies by VAT/tax code or name, European and worldwide company search, shareholders, UBO, AML, PEC/SDI codes, company monitoring. Use for questions like "find data about company X", "who owns VAT number Y", "search companies in France/EU/worldwide".
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).
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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.
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