「meta」の検索結果

Next.js App Router 専用 API リファレンス（Pages Router / getStaticProps 等は非対応）。 Server Components, Server Actions, layout/page/loading/error ファイル規約, dynamic routes, parallel/intercepting routes, Metadata API (generateMetadata), use cache, revalidatePath/revalidateTag, next/image・next/font・next/link, cookies/headers, useRouter, next.config.js, proxy（旧 middleware）。 プラットフォーム実行基盤・CDN・ISR インフラは vercel スキル参照。

Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot with 5'UTR / CDS / 3'UTR scaling; MetaPlotR; deepTools `computeMatrix scale-regions`), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (bigWig of log2 IP/input via deepTools `bamCompare`; ggcoverage; pyGenomeTracks; Gviz; IGV / UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), 5'UTR / CDS / 3'UTR stacked-bar feature-distribution summaries, and volcano / MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012 *Nature* 485:201; Meyer 2012 *Cell* 149:1635 — concurrent founding papers from different labs both showed this independently). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features for figure 1, generating DRACH motif logos as sanity checks on the peak set, rendering volcano plots of differential m6A, or reproducing the Dominissini 2012 / Meyer 2012 stop-codon enrichment plot.

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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot with 5'UTR / CDS / 3'UTR scaling; MetaPlotR; deepTools `computeMatrix scale-regions`), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (bigWig of log2 IP/input via deepTools `bamCompare`; ggcoverage; pyGenomeTracks; Gviz; IGV / UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), 5'UTR / CDS / 3'UTR stacked-bar feature-distribution summaries, and volcano / MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012 *Nature* 485:201; Meyer 2012 *Cell* 149:1635 — concurrent founding papers from different labs both showed this independently). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features for figure 1, generating DRACH motif logos as sanity checks on the peak set, rendering volcano plots of differential m6A, or reproducing the Dominissini 2012 / Meyer 2012 stop-codon enrichment plot.

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The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for SSRF reachability, internal route probing, metadata-service access, credential pivoting, and token-to-accepted-privilege chains. Use when the user asks to trace SSRF sources, internal hosts, metadata endpoints, link-local tokens, service-account credentials, or explain how a server-side fetch edge turns into accepted access. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

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App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.

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Analyze ARIS usage logs and propose optimizations to SKILL.md files, reviewer prompts, and workflow defaults. Outer-loop harness optimization inspired by Meta-Harness (Lee et al., 2026). Use when user says "优化技能", "meta optimize", "improve skills", "分析使用记录", or wants to optimize ARIS's own harness components based on accumulated experience.

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MNN Metal 后端 op/kernel 开发与优化入口。索引各份 sub-doc：性能问题诊断流程（op 单测 + 对手基准定位真瓶颈，优化任务第一站）、kernel 开发规范与优化知识库（命名/写法/GEMV/GEMM/attention + 手段方法论）、算子融合全链路（导出图→converter→Metal 单 dispatch + 融合方法论）、运行时调度（fence/content-cache/H2D/replay + 调度方法论）、构建测试基线、env 开关注册表。根据当前任务选择性阅读对应 sub-doc。

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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

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Guide on how to add and propagate new metadata fields in Apache Beam's WindowedValue, extending protos, windmill persistence, and runner interfaces to avoid metadata loss.

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Kick off public shotgun metagenomics QC, host-depletion, taxonomic profiling, and functional profiling workflows using nf-core/taxprofiler, Kraken2, Bracken, MetaPhlAn, and HUMAnN.

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Upload one Kleros-ecosystem file per paid request to IPFS through the Kleros x402 gateway for $0.01 USDC on Base mainnet. Use for dispute evidence, MetaEvidence JSON, court/dispute/arbitrator policies, Curate item metadata, juror justifications, or any artifact a Kleros contract or subgraph will reference by CID. Trigger when the request mentions Kleros, court, arbitrator, dispute, juror, Curate, Proof of Humanity, evidence, meta-evidence, justification, explicitly names this gateway or skill, or asks to test or validate it. Do NOT use for generic IPFS/CID requests without Kleros context; recommend a general-purpose pinning service. Each request accepts exactly one file: different bytes require separate paid uploads, while identical bytes should reuse one CID.

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Metaplex development on Solana — NFTs, tokens, compressed NFTs, candy machines, token launches, autonomous agents. Use when working with Token Metadata, Core, Bubblegum, Candy Machine, Genesis, Agent Registry, or the mplx CLI.

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Fetches Instagram user profile metadata including bio, follower count, following count, post count, verification status and other profile details. Use when user mentions Instagram profile info, user stats, account details, follower count, bio scraping, Instagram user data, IG profile, check Instagram account, Instagram user info, how many followers does someone have on Instagram, get Instagram profile, Instagram account metadata.

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Trustpilot customer reviews scraper for any company listed on trustpilot.com — given a company domain (e.g. shopify.com, apple.com, shopwagandtail.com) plus optional filters (page number, single star rating 1-5, single language ISO code, verified-only flag, has-company-reply flag, date period last30days/last3months/last6months/last12months, free-text keyword search, and reviewer countryCode client-side filter), extracts paginated reviews with 20+ fields per review: reviewId, reviewUrl, reviewTitle, reviewDescription, reviewRatingScore (1-5), reviewDate (ISO publishedDate), reviewDateOfExperience, reviewLabel, isReviewVerified, reviewer (display name), reviewerId, reviewersCountry (ISO Alpha-2), reviewLanguage, reviewCompanyResponse (the company reply text), plus pagination metadata (currentPage, totalPages, totalCount). Optional flags add reviewer profile metadata (reviewerNumberOfReviews, reviewerProfileIsVerified), reply analysis dates (companyReplyPublishedDate, companyReplyUpdatedDate), extended metadata (reviewSource, reviewVerificationSource, reviewLikes), and review photos. Use when user mentions Trustpilot reviews, scrape Trustpilot, get reviews from Trustpilot, Trustpilot review scraper, Trustpilot customer reviews, extract Trustpilot reviews, Trustpilot review data, download Trustpilot reviews, paginate Trustpilot, paginated Trustpilot reviews, filter Trustpilot reviews by star, filter Trustpilot by language, verified reviews Trustpilot, has reply Trustpilot, company response Trustpilot, Trustpilot reply analysis, brand monitoring Trustpilot, sentiment analysis Trustpilot, competitor reviews Trustpilot, bulk reviews from a Trustpilot page, batch download reviews, trustpilot.com reviews scraping, fake review research Trustpilot, Trustpilot dataset, persona research from Trustpilot reviews, Trustpilot keyword search reviews, Trustpilot date range reviews, review export Trustpilot. Also applies to comparing review trends across competitors, training data for LLM sentiment classifiers, monitoring competitor response time, exporting reviews for BI dashboards, lead generation from active reviewers, ML / fake-review detection, and aggregator sites that display third-party reviews.

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Post-access cloud exploitation for AWS, GCP, and Azure — what to do AFTER you obtain credentials or reach a metadata endpoint. Covers IAM enumeration and privilege escalation (iam:PassRole, CreatePolicyVersion, AssumeRole chains, GCP service-account impersonation, Azure managed-identity abuse), IMDSv1/v2 metadata credential theft, STS token abuse, S3/GCS/Blob bucket takeover and object exfil, Lambda/Cloud Functions env-var secrets, cross-account and cross-service pivoting, and turning leaked keys into demonstrated impact (read PII, assume admin, exfil data). Assumes authorized scope only. Use when you have AWS/GCP/Azure keys, an SSRF hitting 169.254.169.254 / metadata.google.internal, a leaked service-account JSON, or a token, and need to escalate and prove impact. For finding buckets/origins first, use cloud-recon; for the SSRF entry point, see web2-vuln-classes. 中文触发词：云渗透、AWS提权、IAM枚举、元数据凭证、云安全审计

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This skill should be used when the user asks to "use Metasploit for penetration testing", "exploit vulnerabilities with msfconsole", "create payloads with msfvenom", "perform post-exploitation", "use auxiliary modules for scanning", or "develop custom exploits". It provides comprehensive guidance for leveraging the Metasploit Framework in security assessments.

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Universal Meta-Analysis Codebook v2.2 - AI-Human collaboration for meta-analysis data extraction. 4-layer design: Identifiers, Statistics, AI Provenance, Human Verification. Integrates with C5/C6/C7 agents and Category I systematic review pipeline. Triggers: meta-analysis, codebook, data extraction, Hedges g, effect size

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VS-Enhanced Literature Review Strategist - Comprehensive support for multiple review methodologies Full VS 5-Phase process: Prevents Mode Collapse and presents creative search strategies Supports: Systematic Review (PRISMA 2020), Scoping Review (JBI/PRISMA-ScR), Meta-Synthesis, Realist Synthesis, Narrative Review, Rapid Review Use when: conducting any type of literature review, systematic reviews, meta-analyses, scoping reviews, finding prior research Triggers: literature review, PRISMA, systematic review, scoping review, meta-synthesis, realist synthesis, narrative review, rapid review

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Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.

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Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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Given your total budget, recommends the optimal split across Google Search, PMax, Meta prospecting, Meta retargeting, and any other active channels based on your last 60-90 days of marginal ROAS and CPA by channel. Tells you where each additional dollar produces the best return. Platform: Google and Meta.

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Turn almost any file into Markdown plus metadata — PDF, Office, HTML, email, archives, images, audio/video, 1000+ formats — powered by Apache Tika, either via the tika-app CLI (zero setup, one file) or a running tika-server (curl, warm process, many calls). Leads with rmeta (structured, embedded-item-aware output) as the default operation rather than flat concatenated text, since you can't tell whether a file has embedded content from its extension. Covers metadata-only triage, type and language detection, OCR, and output-size discipline for agent context. Use whenever a task involves reading the content of a file whose format you don't want to hand-parse.

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