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概要と使いどころ
Expert guide for Data Pipelines, ETL/ELT, and Analytics Engineering. Covers dbt, Apache Airflow, Dagster, BigQuery, ClickHouse, and DuckDB / Panduan ahli untuk Data Pipelines, ETL/ELT. Mencakup dbt, Airflow, Dagster, BigQuery, ClickHouse, dan DuckDB.
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。
Test and validate MaaFramework Pipeline JSON, recognition nodes, action nodes, CustomAction/CustomRecognition wiring, resource loading, and end-to-end task behavior. Use when running `run_pipeline`, checking OCR/TemplateMatch/ColorMatch ROI, validating `context.run_task()` results, auditing `action: Custom` or `recognition: Custom` registrations, or choosing the right resource-check command.
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。
Add runtime UI options (select/checkbox/switch/input) to MaaFramework option surfaces such as `assets/interface.json` or `assets/resource/tasks/**/*.json`. Use when adding a user-facing toggle, selector, checkbox, or input; wiring options to `pipeline_override`; aligning option paths with Python `context.get_node_data()` or CustomAction params; or reviewing option behavior across Pipeline JSON and Python.
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 MaaFramework Pipeline nodes and recognition snippets from screenshots or observed UI state. Use for OCR node generation, ROI sweep, choosing TemplateMatch/OCR/ColorMatch/CustomRecognition, preserving target-file schema style, and designing `next`/`[JumpBack]` links before merging generated nodes into pipeline JSON.
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 asked to map or analyze flows, pipelines, processes, or sequences of events in this repo — e.g. "mapa el flujo", "flow", "pipeline", "ciclo de vida", "trace the request", "how does the build/release flow work", "diagrama del flujo". Maps data flows, request lifecycles, CI/CD pipelines, and state transitions with verified file:line evidence. Distinct from analyze-code (which maps code structure); this skill maps movement over time.
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 a plug-in-ready SDR operator package for sales development and outbound pipeline work: ICP definition, compliant lead-source plan, enrichment/research workflow, account scoring, outbound assets, CRM handoff, reply triage, meeting prep, approval gates, and loop memory. Use when "SDR", "sales development", "outbound SDR", "operator room", "lead gen pipeline", "prospecting pipeline", "ICP targeting", "Apify", "RapidAPI", "Clay", "Apollo", "agentic SDR workflow", "sales operating loop", "sales dashboard handoff", or any request to package outbound sales workflows that can later connect to data tools, CRMs, or operator surfaces.
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 this skill to start or resume the VeriFlow RTL hardware design pipeline (architect to synth). Trigger this when the user asks to "run the RTL flow", "design hardware", or "start the pipeline". Pass the project directory path as the argument. Optional: append `--benchmark` to auto-generate a benchmark report after the pipeline completes.
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。
Improve router-facing pipeline and reflection narration to reduce noisy status churn and make Step 0/Reflection outcomes explicit. Use when updating Router output contract, reflection reminder wording, or post-pipeline notification batching.
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。
Evaluates completed agent pipelines across 5 scoring dimensions and produces a composite verdict with actionable recommendations
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。
Analyze an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine. Produces an annotation plan that maps detected I/O patterns to source files across 30+ supported languages with 902 auto-detection patterns. Use when onboarding onto an unfamiliar codebase to understand data flow, starting putior integration in a project without existing annotations, auditing a project's data pipeline before documentation, or preparing an annotation plan before running annotate-source-files.
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。
Scaffold a new puzzle type across all 10+ pipeline integration points in jigsawR. Creates the core puzzle module, wires it into the unified pipeline (generation, positioning, rendering, adjacency), adds ggpuzzle geom/stat layers, updates DESCRIPTION and config.yml, extends the Shiny app, and creates a comprehensive test suite. Use when adding a completely new puzzle type to the package or following the 10-point integration checklist to ensure nothing is missed end-to-end.
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。
Three-tier video pipeline: ffmpeg Quick (5s, instant) → ffmpeg Enhanced with Ken Burns (15s, polished) → Remotion Animated (60-90s, production-grade). Takes static slides from Paper MCP or any PNGs and assembles them into platform-ready video. Make sure to use this skill whenever the user has slides, images, or PNGs and wants to turn them into video — even if they just say 'make a video from these', 'animate my slides', 'I have images and need a TikTok', or 'stitch these together'. Also use when they mention ffmpeg video assembly, Remotion-animated slide assembly, Ken Burns effects, or any slides-to-video pipeline. Owns the slides→MP4 assembly pipeline; for writing, debugging, or running `remotion render` on Remotion code itself, see /remotion-best-practices; for screen-recorded product demos, see /marketing-demo. Works with any PNG source — Paper exports, Canva, screenshots, anything.
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。
Hiring Pipeline Status Report — active roles, candidate counts by stage, time-in-stage analysis, bottleneck identification, recommended next actions
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 AI orchestration pipelines with routing. TRIGGERS - Use when user needs help with ai-orchestration-pipeline related tasks.
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 AI image processing pipelines for generation and analysis. TRIGGERS - Use when user needs help with ai-image-pipeline related tasks.
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 ML pipeline orchestration systems. TRIGGERS - Use when user needs help with ai-pipeline-orchestrator related tasks.
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 data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.
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 AI data extraction pipelines. TRIGGERS - Use when user needs help with ai-extraction-pipeline related tasks.
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。
Phase 1 of the feature-pipeline suite — stress-test a CLI command, feature, flag, or module as a developer seeing it for the first time, then file GitHub issues for every distinct finding. Use when someone says "explore X", "test X", "find issues with X", "what's wrong with X", or after /feature-pipeline init. Also triggers standalone as /fp-explore <feature> when you want just the exploration phase without the full pipeline. Reads the target feature from .fp-state.json (or the argument if provided). Completes in one bounded turn and prints the M1 stop hook so the user decides whether to continue to /fp-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。
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, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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。
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
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 and operate CI/CD pipelines: fast feedback, deployment strategies, environments, and pipeline reliability. Use when building, fixing, or scaling build and deploy pipelines.
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。
Full-stack app pipeline — build, test, review, and deploy across one or ALL repos. Scans CLAUDE.md, MEMORY.md, TODOs, open PRs, and issues for shippable work. Supports 'ship all' for multi-repo. Use when: 'build and ship', 'ship it', 'ship all', 'deploy everything', 'full pipeline', 'what needs shipping', 'end to end'.
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。
Full design improvement pipeline — syncs your app to Google Stitch, generates alternatives, presents side-by-side comparison, and adopts cherry-picked improvements. Triggers: 'stitch pipeline', 'improve my design with stitch', 'make my app look better', 'stitch redesign everything'.
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。