「pipe flow」の検索結果

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

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Build automated AWS GuardDuty finding response pipelines using EventBridge and Lambda to trigger real-time incident response, automatically quarantine compromised resources, and route security notifications. Use when designing automated remediation playbooks for GuardDuty findings across VPC Flow Logs, CloudTrail, DNS, EKS, or S3 data events, or when reducing mean time to respond to cloud threats.

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Design and deploy a Threat Intelligence Platform (TIP) by integrating open-source CTI tools (MISP, OpenCTI, TheHive, Cortex) into a unified system with feed ingestion pipelines, enrichment workflows, STIX/TAXII interoperability, and analyst dashboards. Use when architecting or standing up a centralized CTI platform to collect, analyze, and disseminate threat intelligence across a security team.

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Automated Android CI/CD pipeline to Google Play — supports TWA, React Native, Flutter, and native Android. Run npx android-cicd to set up keystore generation, GitHub Secrets, and a multi-stage workflow (internal/alpha/beta/production) with auto-bump versionCode.

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Render clean blueprint-style SVG diagrams from JSON specs. Use when users ask to draw, sketch, or diagram a request flow, neural net, transformer block, system architecture, state machine, data pipeline, or any node-and-edge technical visual they want as an SVG for docs, READMEs, posts, or slides.

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Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

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Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.

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Reference skill for Zoom AI Services Scribe. Use after routing to a transcription workflow when handling uploaded or stored media, Build-platform JWT auth, fast mode transcription, batch jobs, or transcript pipeline design.

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Creates demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs. Use when planning demand gen strategy, growth marketing, advertising campaigns, PPC optimization, lead generation, pipeline generation, or marketing budgets. Covers multi-channel acquisition (Google Ads, LinkedIn Ads, Meta Ads), CAC analysis, MQL/SQL workflows, attribution modeling, technical SEO, and co-marketing partnerships. Default calibration profile is a Series A+ B2B SaaS scaling internationally (EU/US/Canada, hybrid PLG/Sales-Led) — adapt benchmarks for other stages and motions rather than skipping the skill.

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World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

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Release pipeline for CLI, mobile, web, and server. Guides through version bumping, building, testing, publishing, and deploying. Replaces the old interactive release-it flow with a Claude Code-native experience. Use when user types /release or asks to release, publish, deploy, or ship any component.

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Use when adding, modifying, or debugging Handsontable performance test scenarios in performance-tests/ - covers the CDP trace-based measurement system, scenario structure (fixture + config + spec), trace-parser integration, hook timing for filtering/sorting, golden snapshot workflow on GitHub Pages, and the CI comparison pipeline. Trigger whenever work touches performance-tests/ files, when asked to benchmark a Handsontable feature, or when adding a new performance scenario.

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Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

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Creates Cloud Storage on Google Cloud (also known colloquially as GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.

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Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

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Teaches the fundamentals of Cloud Build on Google Cloud. Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.

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AI audio editing pipeline: Whisper word-level transcription → Claude segment classification (KEEP/CUT_FILLER/CUT_FALSE_START/CUT_STUTTER/CUT_DEAD_AIR) → ffmpeg with 40ms qsin crossfades and room-tone fill → optional Cleanvoice cloud polish; plus GateScan/GateRepair for noise-gate ticking artifacts. Modes: --preview, --aggressive, --polish. Workflow: Clean. USE WHEN clean audio, edit audio, remove filler words, clean podcast, remove ums, cut dead air, polish audio, trim recording, cut stutters, ticking audio, clicking audio, audio clicks, gate artifacts, popping audio. NOT FOR video composition (use Remotion).

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Workflow 4: Submission rebuttal pipeline. Parses external reviews, enforces coverage and grounding, drafts a safe text-only rebuttal under venue limits, and manages follow-up rounds. Use when user says "rebuttal", "reply to reviewers", "ICML rebuttal", "OpenReview response", or wants to answer external reviews safely.

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Writes and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and integrations on the Salesforce platform. Use when developing Salesforce applications, customizing CRM workflows, managing governor limits, bulk processing, or setting up Salesforce DX and CI/CD pipelines.

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Guides understanding and working with Apache Beam runners (Direct, Dataflow, Flink, Spark, etc.). Use when configuring pipelines for different execution environments or debugging runner-specific issues.

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Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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Technology-agnostic guidance for modular systems: bounded contexts, clear boundaries, composability, state isolation, explicit contracts, failure containment, scaffolding workflows, split/merge criteria, sub-units inside a context, and compliance review signals. Use when designing or reviewing module structure, service boundaries, package layout, cross-cutting dependencies, "how should we split this?", modularity assessments, coupling between domains, greenfield context design, or architecture discussions without assuming a specific framework, language, or repository layout. Do NOT use for executing the full Patterns 1–5 repo decomposition pipeline or per-pattern inventories (use modular-decomposition), phased extraction roadmaps as the main deliverable (use decomposition-planning-roadmap), or end-to-end legacy migration strategy (use legacy-migration-planner).

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Ship every change the way the user requires, with the guardrails. Take screenshots of the start, the key moment and the result; give the change its own changelog version with pictures; verify with tests; commit only your own files; merge with the latest main and push as a fast-forward; publish that exact main live; confirm each step; and report measured sizes, asking before any upload over 50 MB. Use at the end of every change (feature, fix, tweak, art, balance, polish) and whenever the user says "ship it", "commit", "push", "merge", "publish", "deploy", "go live" or "update the changelog". Project specifics (hosting, the changelog pipeline, merge conflicts) go in an optional local references/<project>.md next to this skill.

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End-to-end onboarding of a new entity, customer, or account into TRES Finance. Orchestrates 8 steps in order: wallet upload, data collection (commit), balance validation, reconciliation, cost basis, export unidentified addresses, import contacts, and rollup rules. Trigger ONLY when the user explicitly wants to onboard a new entity, customer, or account — e.g. "onboard a new entity", "onboard new customer", "start onboarding for [name]", "set up a new account in TRES", "run the full onboarding flow", "I need to onboard [company name]", "new entity onboarding". Do NOT trigger for individual tasks — those have their own dedicated skills. If the user asks to "add wallets" without mentioning onboarding, use tres-wallets-upload. "Validate balances" alone is tres-asset-balance-validation. "Run a commit" alone is tres-data-collection-commit. Only trigger when the user wants the full pipeline.

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。