「chi」の検索結果

Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share metrics, investigating low lead flow, or searching for bidding and budget constraints. Don't use for setting up new campaigns, uploading conversion events directly, or general Google Mobile Ads SDK integration issues (use gma-android-integrate instead).

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Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.

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Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.

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Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, generating new YARA-L 2.0 rules to close coverage gaps, and with user approval, deploy them to SecOps. Don't use when asked to perform threat hunting actions, and SOC investigative actions.

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Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).

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Provides foundational Firebase CLI setup, CLI installation, version checks (`firebase-tools@latest --version`), CLI login (including --no-localhost), project creation, project selection (`firebase use`), and app config file downloads (`google-services.json`, `GoogleService-Info.plist`). Use ONLY for CLI login, project creation/switching, or downloading app config files. Don't use for Firebase Hosting deploy, Firestore, Auth, App Hosting, Data Connect, Crashlytics, or Remote Config.

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Discovers golden base images for creating GKE custom node images based on technical specifications or context clues. Use when finding the golden base image for custom GKE node creation, mapping cluster configuration parameters (GKE version, OS, architecture, accelerators, gVisor, cgroups) to image names, or querying GKE base image maps. Don't use for general GKE cluster creation (use gke-cluster-creation) or standard node pool management (use gke-basics).

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Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).

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Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).

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Teaches the fundamentals of Google Cloud Build (GCB). 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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Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

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Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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Scaffolds a structured investigation in scratch/ for empirical research and documentation. Use when the user says "start an investigation" or wants to: trace code paths or data flow ("trace from X to Y", "what touches X", "follow the wiring"), document system architecture comprehensively ("document how the system works", "archeology"), investigate bugs ("figure out why X happens"), explore technical feasibility ("can we do X?"), or explore design options ("explore the API", "gather context", "design alternatives"). Creates dated folder with README. NOT for simple code questions or single-file searches.

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Create a well-structured GitHub PR with proper branching, testing, formatting, and documentation. Use when the user says "create a PR", "make a PR", "open a pull request", or wants to submit changes for review. Handles the full workflow: branch creation, implementation, testing, formatting, committing, and PR creation with comprehensive descriptions.

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Audit feature specs against India's Digital Personal Data Protection (DPDP) Act. Use this skill when: - Writing or reviewing any feature that handles user data (especially children's data) - Auditing a spec's Personal Information section for completeness - Checking a feature against DPDP Act 2023 requirements - Preparing compliance documentation before engineering review - Verifying masking, retention, and consent policies are defined for all PII

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Capture learnings from a campaign, launch, sprint, or initiative using Five Whys (Taiichi Ohno, Toyota Production System) for root cause analysis and Start-Stop-Continue for action planning. Append reusable insights to knowledge/learnings.md so future runs of the OS get smarter. Use when the user asks for a retro, retrospective, post-mortem, "what did we learn", "campaign wrap-up", "lessons learned", or after a campaign or launch ends. Critical feedback loop that makes the OS compound over time. For the numbers review itself, see kpi-review. For planning the next campaign, see campaign-brief.

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Write personalized pitch emails to journalists, podcasters, newsletter writers, and analysts using AIDA adapted for media pitching and Ryan Holiday's journalist psychology from "Trust Me I'm Lying." Different from press releases (use /press-release-writer for those). Use when the user asks to pitch a journalist, pitch a reporter, "get coverage in X", media outreach, podcast pitch, analyst briefing request, or wants to land a story. Reads brand voice and positioning from knowledge/. For the press release itself, see press-release-writer. For the underlying customer story, see case-study-writer.

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Write long-form thought leadership articles, opinion pieces, industry POV essays, and CEO/founder bylines using the Made to Stick SUCCESs framework (Chip and Dan Heath). Use when the user asks for a long-form article, executive byline, opinion piece, industry POV, manifesto, "explain our point of view on X", or wants to publish an authority-building piece (1200-2500 words). Reads brand voice and positioning from knowledge/. For a short-form post, see linkedin-post. For the newsletter, see newsletter-writer. For improving an existing draft, see copy-review.

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Write SEO-optimized long-form articles targeting specific keywords using the They Ask You Answer Big 5 framework (Marcus Sheridan). Articles are categorized by Big 5 type (Cost, Problems, Versus, Best/Reviews, How-To) and structured accordingly. The "answer first" rule applies to every article. Use when the user asks for an SEO article, blog post for ranking, "rank for keyword X", organic content, search-optimized post, pillar page, or content for organic traffic. Includes keyword targeting, search intent matching, internal linking suggestions, and meta tags. For improving an existing article, see copy-review. For deciding what to write about, see social-calendar pillars and icp-research. For repurposing it, see content-repurposer.

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Write press releases for product launches, funding announcements, partnerships, executive hires, and milestones using the Inverted Pyramid (AP journalism) and Ogilvy on Headlines (David Ogilvy, Ogilvy on Advertising). The lede answers Who/What/When/Where/Why in 40 words or fewer. The headline selects the right audience and promises a specific benefit. Use when the user asks for a press release, media release, news announcement, PR for X, launch announcement, funding announcement, or "write a press release". Reads brand voice and positioning from knowledge/. For pitching it to a journalist, see pr-pitch-writer. For the customer story behind it, see case-study-writer.

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Write multi-email nurture sequences for lead-gen, onboarding, re-engagement, sales, and lifecycle marketing using the Hook Model (Nir Eyal) to architect the sequence arc and PAS (Problem-Agitate-Solution) to structure each email body. Use when the user asks for an email sequence, drip campaign, nurture flow, onboarding emails, re-engagement campaign, "write 5 emails for X", welcome series, or any multi-step email program. Reads brand voice and ICP from knowledge/. For a single cold outreach email, see pr-pitch-writer. For the newsletter, see newsletter-writer. For testing subject lines, see ab-copy-writer.

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Write customer case studies and success stories using the Situation-Complication-Resolution (SCR) narrative framework and Barbara Minto's Pyramid Principle (conclusion first). Produces case studies where the headline states the result, the complication makes readers recognize their own situation, and every paragraph advances the story. Use when the user asks for a case study, customer story, customer success story, win story, reference write-up, or "turn this customer into a case study". Reads brand voice and service catalog from knowledge/. For a press announcement, see press-release-writer. For pitching the story to media, see pr-pitch-writer. For repurposing it into social, see content-repurposer.

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Generate, score, and shortlist brand names through a guided interview. Use when the user says "name my brand", "help me name", "come up with a name", "naming ideas", "what should I call this", "brand name", "product name", "company name", "rename", "rebrand", or is stuck choosing between names. Self-contained and brand-agnostic: it does not read knowledge/, so it works for naming a brand-new venture, product, service, feature, or rebrand from scratch, before any brand context exists. Runs on Alexandra Watkins' SMILE and SCRATCH tests to generate and filter, Marty Neumeier's 7 criteria to score, and the Lexicon Branding method for sound symbolism and strategy. Asks the user questions before generating. For the positioning the name has to carry, see positioning-doc. For the message architecture built on top of it, see messaging-framework.

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Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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