「metadata」の検索結果

週刊テックニュース動画のYouTubeメタデータ（タイトル・概要欄・ハッシュタグ）を生成するスキル。「週刊ニュースを作成して」「週刊テックニュースを作りたい」などのリクエストで利用。

技術解説系YouTube動画のメタデータ(タイトル・概要欄・ハッシュタグ)を生成するスキル。プロスペクト理論を活かした損失回避の心理を使ったタイトル生成、見やすい概要欄(目次、タイムテーブル、SNSリンク含む)、体系的なハッシュタグ選定を実施。「YouTube用のタイトルを作成」「動画の概要欄を生成」などのリクエストで利用。

Manages project versioning following Semantic Versioning (SemVer) principles. Calculates increments based on changes and synchronizes version across documentation and metadata. Use when the user asks to "bump the version", "prepare a release", or when the `changelog` skill calls for a release.

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。

Insurance-industry App-in-Skill for read-only data governance, backed by an operator-provisioned Busabase workspace (one Drive node for the file drive plus four Bases for QA pairs, featured information, insurer notices, and user feedback) and trusted export/restore/PDF-text-backfill scripts. Use when the user invokes $kelly-insure-data or /kelly-insure-data, wants an insurance data workspace with UI, needs to review insurance files, metadata completeness, QA pairs, featured information, insurer notices, or user feedback, wants to back up or restore a Kelly Insure Data Busabase workspace from local PDFs, or wants Busabase Drive/Base data surfaced for data quality review and ongoing data governance.

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。

Classify financial PDFs by extracting company identity, document type, and date metadata. Validates ticker via Yahoo Finance with multi-step fallback. Uses a Python orchestration script for validation, file modification, and writing markdown.

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。

Execute a single deterministic Python script to harmonize units, create/update metadata, move files to output_data/, and clean up input_data/.

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Execute a single deterministic Python script to compute WACC, DCF assumptions, projected free cash flows, enterprise value, and intrinsic value per share. Updates the metadata markdown.

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Extracts the WACC, assumptions, and DCF projections from the markdown metadata file and converts them into a clean, structured JSON file for the frontend interactive viewer.

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Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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Communicate between existing local Codex App tasks that share one working directory. Use when the user asks to list or inspect same-directory tasks, send one a message or context handoff, ask a task to return a result, wait for that result, continue a request that needs user input, present an inbound task reply, or explicitly broadcast a one-way notice. Also use for Codex delegations that invoke $codex-task-messenger or contain its message-card metadata.

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This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.

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Parse and analyze Windows Amcache.hve registry hive to extract program execution evidence, file metadata, SHA-1 hashes, and device connection history for digital forensics and incident response investigations.

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Analyze Microsoft Outlook PST and OST files for email forensic evidence including message content, headers, attachments, deleted items, and metadata using libpff, pst-utils, and forensic email analysis tools for legal investigations and incident response.

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Analyze the NTFS Master File Table ($MFT) to recover metadata and content of deleted files by examining MFT record entries, $LogFile, $UsnJrnl, and MFT slack space using MFTECmd, analyzeMFT, and X-Ways Forensics.

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Investigates a session recording by gathering metadata, person profile, same-session events, and linked error tracking issues in one pass. Use when a user provides a recording or session ID and wants to understand what happened — who the user was, what they did, what errors occurred, and whether there are related error tracking issues. Replaces the manual chain of session-recording-get, persons-retrieve, execute-sql, and query-error-tracking-issues-list.

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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

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Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.

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Literature: retrieve document-level scientific evidence from publications, clinical-trial registries, and patent sources. Use for PubMed records, reproducible searches, trial registrations, patent evidence, abstracts, study metadata, stable identifiers, and citation provenance.

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。

Preserve user work, scope, metadata, history, and verifiable delivery.

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Extract headline, author, publication date, summary, category, and named entities from a public news article.

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。

Extract clean title, summary, markdown sections, and source metadata from a public documentation page for RAG ingestion.

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Extract title, authors, abstract, and citation info from academic papers.

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Use when writing or auditing the conversational copy layer of a Salesforce bot or Agentforce agent: utterance authoring strategy, fallback message copy, escalation-criteria phrasing, and persona-consistent dialog scripting across channels. NOT for platform configuration (bot builder setup, topic metadata wiring, action mapping, or Agentforce deployment) — see architect/einstein-bot-architecture and agentforce/agentforce-persona-design for those concerns.

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。