「hn」の検索結果

Capture the "why" behind technical choices to prevent architectural drift. Load when the user makes a major technical decision, chooses a library/framework, defines a system boundary, or changes a core architectural pattern. Also triggers on "record a decision", "write an ADR", "why did we do this", "document this architectural choice", or "architectural decision record". Supports `SYNTHESIS=true` mode for retrospective ADR backfill from observed repo state — used by `retroactive-project-setup` and any future backfill skill. Essential for long-term project maintainability and agent alignment.

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Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team 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。

AUTHORIZED USE ONLY: This skill contains dual-use security techniques. Before proceeding with any bypass or analysis: > 1.

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。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

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Expert in developer tool documentation as a product. Activate on: docs site, getting started guide, API reference, developer documentation, Docusaurus, Mintlify, Nextra, ReadMe, migration guide, troubleshooting page, docs information architecture, documentation testing, llms.txt. NOT for: marketing copy (use seo-content-blogging), API design itself (use api-architect), general technical writing (use technical-writer).

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Validates typed DAG dependency declarations, returns Kahn linear extensions or generations and residual cycle witnesses, and identifies changed consumers for revalidation. NOT for constructing graphs, scheduling resource capacity, granting effects, or executing nodes.

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Systematic framework for resurrecting and modernizing legacy codebases through archaeology, resurrection, and rejuvenation phases. Activate on "legacy code", "inherited codebase", "no documentation", "technical debt", "resurrect", "modernize". NOT for greenfield projects or well-documented active codebases.

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Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed.

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Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.

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Personalized academic writing assistant for university assignments in Chinese and English. Use when users need help writing/revising academic reports, project docs, technical analyses, research reviews, or case studies. Produces natural prose avoiding AI markers. Triggers: academic writing, assignment, report, technical analysis, research review, case study. | 个性化学术写作助手，适用于中英文大学作业。触发词：学术写作、作业、报告、技术分析、研究综述、案例研究、项目文档。

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。

Library of accessibility personas representing people with various disabilities, impairments, and situational limitations. Use this skill when users ask about disability types, accessibility personas, user needs for specific conditions, how people with disabilities use technology, assistive technology users, or designing for accessibility. Triggers on requests about blindness, deafness, cognitive disabilities, motor impairments, low vision, screen readers, sign language, autism, ADHD, temporary disabilities, or any question about "how would a person with X use this".

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。

Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team 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。

Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality 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。

AUTHORIZED USE ONLY: This skill contains dual-use security techniques. Before proceeding with any bypass or analysis: > 1.

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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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。

Generate or edit images using AI models (FLUX, Gemini). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that isn't a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.

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。

Systematically evaluate architecture decisions, document trade-offs, and select appropriate patterns. This skill should be used when the user asks about 'architecture decision', 'ADR', 'design pattern selection', 'technology choice', or needs to evaluate architectural trade-offs. Keywords: architecture, ADR, patterns, trade-offs, technical debt, quality attributes, decision record.

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 and maintain Architecture Decision Records (ADRs) following best practices for technical decision documentation. Use when documenting significant technical decisions, reviewing past architect...

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。

Perform structured investment research (投研分析) for a company/stock/ETF/sector using a repeatable framework: fundamentals (basic/财务报表与商业模式), technical analysis (技术指标与关键价位), industry research (行业景气与竞争格局), valuation (估值对比/情景), catalysts and risks, and produce a professional research report + actionable plan. Use when the user asks for: equity/ETF analysis, earnings/financial statement breakdown, peer/industry comparison, valuation ranges, bull/base/bear scenarios, technical trend/support-resistance, or a full research memo.

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Plain-English translation layer for non-technical Copilot CLI users. Translates every approval prompt, error message, and technical output into clear, jargon-free English with color-coded risk indicators.

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Pricing strategy for technical products. Use when choosing usage-based vs seat-based, designing freemium thresholds, structuring enterprise pricing conversations, deciding when to raise prices, or using price as a positioning signal.

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。