「chi」の検索結果

Creates professional logical flow diagrams and logical system architecture diagrams using draw.io XML format (.drawio files). Use when creating: (1) logical flow diagrams showing data/process flow between system components, (2) logical architecture diagrams representing system structure without cloud provider specifics, (3) BPMN process diagrams, (4) UML diagrams (class, sequence, activity), (5) data flow diagrams (DFD), (6) decision flowcharts, or (7) system interaction diagrams. This skill focuses on generic/abstract representations, not AWS/Azure-specific architectures (use aws-drawio-architecture-diagrams for cloud diagrams).

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When the user needs to design or evaluate system architecture — service boundaries, data models, API contracts, infrastructure topology, database selection, or dependency analysis. Also activate for "design the system", "how should I architect this", "monolith vs microservices", or architecture decision records.

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Selects and routes to the right architecture paradigm. Use when choosing patterns for a new system or comparing trade-offs before making architecture decisions.

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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.

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Review repository architecture using Ontoly Software Graph and MCP capabilities. Use when asked to explain architecture, module boundaries, package topology, service ownership, or architectural risk.

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Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

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Guide for managing consent for children's personal data under GDPR Article 8 and COPPA. Covers parental consent mechanisms, age verification methods, country-specific age thresholds (ranging from 13 to 16), parental authorization workflows, and age-appropriate design per the UK ICO Children's Code.

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Guides compliance with China's Personal Information Protection Law (PIPL, effective 1 November 2021). Covers consent requirements, cross-border transfer mechanisms (CAC security assessment, standard contracts, certification), separate consent triggers, and critical information infrastructure obligations. Keywords: PIPL, China data protection, CAC security assessment, cross-border transfer, separate consent, CIIO.

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Designs and implements privacy notices for children that comply with GDPR Articles 12-14, UK AADC Standard 4, and COPPA Section 312.4. Covers plain language, visual explanations, layered information, age-appropriate vocabulary, and interactive notice elements. Keywords: children privacy notice, transparency, plain language, visual, age-appropriate, layered notice.

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Implements strict data minimization and retention limits for children's personal data under GDPR Art. 5(1)(c), Recital 38, UK AADC Standard 8, and COPPA Section 312.7. Covers strict necessity testing, shorter retention periods, limited profiling, parental dashboard design, and automated deletion. Keywords: data minimization, children, retention, necessity test, parental dashboard.

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Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.

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。

Design architecture foundations before implementation. Use when asked to design or refactor architecture, choose Rust/Go crate, package, module, runtime, workflow, or service boundaries, compare mature project architecture, prevent stacked one-off PRs, audit migration debt in existing repos, or produce a spec before coding.

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Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.

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Fix Wayback Machine CDX API returning empty results or timing out from cloud/datacenter IPs (Cloud Run, AWS Lambda, GCE, etc.) while working fine locally. Use when: (1) CDX queries timeout or return 0 bytes from Cloud Run/cloud functions but work from local machine, (2) urllib.request.urlopen times out for web.archive.org from server, (3) Wayback Machine CDX silently returns empty from datacenter IPs with no HTTP error, (4) Need to run CDX-dependent scripts on Cloud Run or similar cloud compute. Workaround: fetch locally, upload to cloud storage, import on cloud compute.

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Fix JavaScript regex failures when matching CJK (Chinese/Japanese/Korean) text using \b word boundaries. Use when: (1) Regex pattern with \b silently fails to match Japanese, Chinese, or Korean text, (2) Pattern works for Latin/ASCII text but not CJK, (3) hasDriverLicenceCue or similar text-detection function returns false for CJK input despite correct characters, (4) inferIssuerFromTitleText or pattern-matching functions fail on non-Latin scripts. Root cause: JavaScript \b only recognizes [a-zA-Z0-9_] as "word characters" — CJK characters are classified as \W (non-word), so \b before/after CJK always sees a non-word/non-word boundary and fails to match.

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Design scale-adaptive game architecture with engine systems and networking. Use when the user says "game architecture" or "design architecture"

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Game systems architect for technical architecture, engine design, and infrastructure. Use when the user asks to talk to Cloud Dragonborn or requests the Game Architect.

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Think and work like an expert Machine Learning Researcher. Use when a task calls for Machine Learning Researcher judgment. Reasons from population risk, double descent, and inductive bias; enforces sacred test sets, hierarchical ablations, nested CV, and HELM/Dynabench-aware benchmarking; reports with NeurIPS and Pineau reproducibility checklists while treating leakage, meta-overfitting, benchmark contamination, Goodhart gaming, and seed variance as first-class failure modes.

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Think and work like an expert Electric Machines Engineer. Use when a task calls for Electric Machines Engineer judgment. Reasons from magnetic circuit design, dq-frame machine models, FEM flux paths, and drive efficiency maps while treating saturation, cogging, thermal derating, and inverter harmonics as first-class failure modes.

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Provider-independent still-image post-production skill for RAW/rendered intake, nondestructive development, exposure, white balance, tone, color, ICC proofing, masking, repair, compositing, truthful retouching by genre, generative disclosure, sharpening, noise, resampling, variants, metadata, export, QA, rights, and ethical finishing. Use when an agent must plan, perform, direct, or review still photography retouching and finishing for print, web, social, archive, publication, or client delivery; do not use for image-generation APIs, video grading, or campaign-specific production strategy.

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Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.

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End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3 (or HOMER), and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.

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Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.

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Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.

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。