「pip」の検索結果

Drives the Phase 4-5 dev pipeline state machine with sub-agent reviews and bounded autoresearch gates. Use when running /pipeline or executing an approved plan.

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。

Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build systems.

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。

Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.

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。

Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.

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。

Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests involving SOAR enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment pipelines, or automated IOC processing.

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。

Структурированная 6-шаговая процедура для улучшения, реновации или перестройки существующих пайплайнов, отдельных папок проектов, структур документации или стек-технологий. Вызывается как "pipeline optimizer" (для целых тематических пайплайнов, например, пайплайнов разработки ПО, исследований или создания игр) или "project-folder optimizer" (для отдельных папок проектов внутри пайплайна, например, единичного инструмента ПО или проекта статьи). Активируется при выполнении таких задач, как "улучшить пайплайн X", "оптимизировать стек", "перестроить Y", "реновация", "рефакторинг пайплайна", "навести порядок в папке проекта", "улучшить структуру папки", "унифицировать конвенции", "консолидация документации", "интегрировать в существующую систему" или при любом существенном вмешательстве в устоявшиеся структуры. Обеспечивает анализ существующего фонда, уточнение целей, эскиз идеального состояния, план устранения разрывов, эмпирическое выявление проблемных мест и повторное тестирование с помощью свежих субагентов. Предотвращает появление параллельных стандартов, дублирование и сбои в пайплайне.

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。

Standard-Gerüst für rotierende Pipeline-Checks: Pro Lauf genau ein Ziel aus einer Menge (Projekte, Ordner, Repos) wählen — bevorzugt das am längsten ungeprüfte —, den Check durchführen, Ergebnis in einer Check-Registry und einem Verlaufslog festhalten. Nutze diesen Skill, wenn ein wiederkehrender Check über viele Projekte verteilt werden soll („prüfe regelmäßig alle X auf Y"), wenn eine Automatisierung Doppelprüfungen vermeiden muss, wenn eine Check-Registry/CHECKS-LOG-Struktur angelegt oder benutzt wird, oder wenn eine periodische Qualitätsrunde (Quellencheck, Style-Check, Health-Check, Audit) über eine Pipeline fair verteilt werden soll.

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。

Proposes the piping content a P&ID adds to an accepted PFD process model: line inventory with off-page continuity and required ancillary lines, line identification elements per the project line numbering procedure, connection items at nozzles matched to a quoted schedule, per quoted standards, specification break questions and observations. Every proposal cites its source and revision; no source, no proposal; never sizes a line or selects a class. Use when the user asks to "run the piping enrichment", "start the discipline enrichment", "list the lines the P&ID adds to this PFD" or "fill the line identifiers from the numbering procedure" after gate G2. Do not use for instruments, loops or instrument tags, use instrumentation-and-control-enrichment instead; do not use before gate G2, use process-model-check. Drafts for human review; never approves, authorises or signs off.

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。

Triage a lead pipeline and say who needs action today: replied means send the scheduling link, link sent 24h ago without a booking means nudge, awaiting reply means check inbound, going cold means follow up, booked means close out. Ranked and draft-first, so the owner edits before anything goes out. Triggers: "/pipeline", "work my leads", "who is stuck in the funnel".

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。

End-to-end media pipeline automation — from AI generation through optimization to social publishing. Covers workflow design, WordPress integration, Redis job queuing, batch processing, multi-site pipelines, and orchestration. MCP bridge tools are the primary interface for individual operations; the Design Stack media worker handles batch and advanced processing. Use when you need to automate image generation→optimization→publishing chains, set up cross-site media sync, design batch processing pipelines, or orchestrate multi-step media workflows triggered by post publish or cron.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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。

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

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 working with Buildkite — buildkite CI/CD pipeline and agent management. Covers build pipeline status, agent pool health, artifact management, build annotations, and cluster queue monitoring. Use when checking build status, investigating failures, managing agents, or reviewing pipeline configurations.

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。

You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

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。

Operate the Teams meeting summary pipeline via FETIH CLI — summarize meetings, inspect pipeline status, replay jobs, manage Microsoft Graph subscriptions.

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。

完全パイプライン実行 - 調査から設計・コード生成まで全フェーズを一括実行。/full-pipeline [対象パス] で呼び出し。

Generates the monthly revenue-accountable marketing report that B2B SaaS marketing leaders deliver to CEOs, CFOs, executive teams, and boards. Use when preparing the monthly CEO report, drafting the marketing section of a board deck, defending budget decisions, diagnosing pipeline gaps, or transitioning marketing from MQL/SQO reporting to revenue, CAC payback, and forward pipeline coverage. Designed for B2B SaaS demand generation leaders who own pipeline targets across segments, geos, and motions, and need to explain marketing's revenue contribution in executive language.

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 the user wants an end-to-end run from source footage to a rough cut or final packaged video, or asks "素材から動画を作って", "全自動で", "編集して", or "粗編集を作って". Prefer the official full-pipeline entrypoint, then fall back to individual stage Skills for recovery.

Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.

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。

Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.

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。

Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests involving SOAR enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment pipelines, or automated IOC processing.

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。

Focused Signals scout for PostHog projects moving data through pipelines. Watches the three delivery surfaces — CDP destinations and transformations (hog functions), batch exports, and hog flows (workflows/messaging) — for contradictions between configured state and actual delivery: functions the watcher quietly degraded or disabled, failure rates stepping above a pipeline's own baseline, batch export runs failing or stalling (a growing data gap), and active flows failing for the people they trigger on. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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。

End-to-end flow, spectral, and mass cytometry (CyTOF) pipeline from raw FCS files to differentially abundant/expressed cell populations. Orchestrates the read -> compensate/unmix -> transform -> QC -> doublet-removal -> cluster-or-gate -> annotate -> diffcyt DA/DS chain with flowCore/CATALYST/diffcyt, branching on instrument type and on clustering-vs-gating. Use when processing a cytometry experiment end-to-end, deciding the pipeline path for an instrument, or wiring the flow-cytometry component skills into one analysis with valid sample-level statistics.

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。

Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.

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。