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Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

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Generates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

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Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.

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Develop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.

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Read-only repository explorer. Use PROACTIVELY for cold-start exploration, broad cross-file localization, or when a direct search has failed and you need to find where something lives. Skip it when the issue already names the exact file or symbol, or a previous turn already returned usable file:line evidence. Returns only compact path:line citations; its reads and greps never enter the main conversation.

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Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).

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Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.

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Use when monitoring Agentforce agent sessions, analyzing conversation logs, measuring deflection rates, or diagnosing agent performance issues. Triggers: 'agentforce session analytics', 'how to query agent conversation data', 'monitor agentforce agent effectiveness', 'agent deflection rate', 'utterance analysis agentforce'. NOT for Trust Layer audit logging — use agentforce/einstein-trust-layer. NOT for designing the topic boundaries and guardrails that cause the misrouting you are seeing in the data — use agentforce/agentforce-guardrails.

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Use when creating or managing picklist fields: choosing between Global Value Sets and object-local picklists, configuring controlling and dependent field relationships, managing picklist values, and replacing values in existing data records. NOT for why the API or an integration ignores a dependency matrix - use admin/field-dependency-and-controlling. NOT for cleaning up degraded, unrestricted or orphaned picklist values - use admin/picklist-field-integrity-issues. NOT for record type picklist filtering - use admin/record-types-and-page-layouts. Keywords: GlobalValueSet, StandardValueSet, __gvs suffix, customValue, valueSetName, valueSetDefinition, restricted picklist, GlobalValueSetTranslation, OpportunityStage, CaseStatus.

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Use when picklist data quality has degraded: unrestricted picklists accepting garbage API values, dependent picklist relationships bypassed by integrations, record type picklist value mappings drifting out of sync, or orphaned values appearing in reports. NOT for the value-deactivation runbook or the API-name-vs-label trap — use admin/picklist-data-integrity. NOT for initial picklist creation or Global Value Set design — use admin/picklist-and-value-sets. NOT for record type configuration itself — use admin/record-types-and-page-layouts.

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Use when learning, designing, or explaining Salesforce Reports and Dashboards from first principles — report types, custom report types, groupings, bucket fields, summary formulas, charts, dashboard components, dynamic dashboards, report subscriptions, and folder permissions. Triggers: 'how do I build a report', 'what report type should I use', 'set up a dashboard', 'joined report limits', 'bucket field vs formula', 'dynamic dashboard running user'. NOT for CRM Analytics (use crm-analytics-* skills). NOT for Einstein Discovery predictive analytics. NOT for troubleshooting missing report data caused by sharing model issues (use admin/reports-and-dashboards for that).

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Use when a report or dashboard is slow, timing out, hitting row limits, or consuming excessive system resources on large-volume objects — covers selective filter strategy, custom report type optimization, async report execution via Analytics API, dashboard refresh scheduling, and grouping/aggregation tuning. Triggers: 'report times out', 'report is slow', 'dashboard not refreshing', 'report row limit hit', 'too many report results'. NOT for CRM Analytics / Tableau CRM performance (separate skill). NOT for sharing-model or visibility issues causing missing rows (use admin/sharing-and-visibility). NOT for building or designing reports from scratch (use admin/reports-and-dashboards-fundamentals).

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Picklist governance for Salesforce admins — Restricted vs Unrestricted, Global Value Sets vs local picklists, value deactivation that breaks reports, dependent picklists with deactivated controllers, and the 'value not in picklist' phantom-data problem. Covers the per-record-type value selection model, the API-vs-label mismatch trap, and the mass-replace-values runbook. NOT for auditing an org where invalid values already exist or record-type mappings have drifted — use admin/picklist-field-integrity-issues. NOT for creating a picklist field or wiring up a dependent picklist — use admin/picklist-and-value-sets.

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Use this skill when configuring Data Manager in CRM Analytics: enabling objects for sync, scheduling data sync runs, setting up remote connections to external databases, monitoring sync status and error logs, or troubleshooting connected object issues. Trigger keywords: data sync, connected objects, Data Manager, sync schedule, remote connection, Snowflake connector, sync error, incremental sync, CRM Analytics data pipeline. NOT for pushing external data in programmatically via the External Data API — use data/analytics-external-data. NOT for authoring dataflow nodes — use admin/analytics-dataflow-development.

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Detects review-blocking configuration issues including sub-1.0 version numbers, beta in app name, staging endpoints, and missing demo credentials that prevent reviewers from evaluating the app, violating Guidelines 2.1 and 2.3.

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Detects screenshot submission issues including launch-screen-only assets and cross-platform UI references, enforcing Guideline 2.3.3 which requires screenshots to show the app in actual use.

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Run linters and formatters to fix code style issues. Use when cleaning up style violations, formatting code, or after implementation to ensure code meets project standards.

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Scan code for security vulnerabilities, misconfigurations, and exposed secrets. Use when a user asks to audit security, find vulnerabilities, check for OWASP issues, scan for secrets, review dependencies for CVEs, detect SQL injection, find XSS vulnerabilities, or harden an application. Covers OWASP Top 10, dependency auditing, secrets detection, and generates fix recommendations with severity ratings.

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Audit web pages and components for WCAG 2.2 accessibility compliance. Use when a user asks to check accessibility, find a11y issues, audit for WCAG compliance, fix screen reader problems, check color contrast, ensure keyboard navigation works, or prepare for accessibility regulations like the European Accessibility Act or ADA.

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Perform thorough code reviews with actionable, prioritized feedback. Use when a user asks to review code, check code quality, find bugs, review a pull request, audit code for issues, or get feedback on implementation. Covers correctness, security, performance, readability, and best practices across languages.

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Fully autonomous bug hunting pipeline — discover bugs in a scoped area using parallel subagents, independently triage each finding, fix confirmed issues with subagents, then audit all fixes against repo constraints and target platforms. Runs end-to-end without user interaction.

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Master debugger for Python code with expertise in common errors, performance issues, and debugging tools

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Use the Skmtc CLI to scaffold projects, install or clone generators from JSR, configure schema sources and enrichments, and produce code artifacts from an OpenAPI v3 or GraphQL SDL schema. Teaches the workspace mental model (`<root>/.skmtc/<project>/`, client.json, bundle, manifest) and the agent contract (strict text / strict JSON modes, exit codes, recipe errors, `agent-context` + `doctor`); the command surface itself is discovered from the binary — `skmtc --help`, `skmtc <cmd> -h` — rather than carried in this skill. Use this skill when the user asks to "run skmtc", "generate code from an OpenAPI schema", "install a skmtc generator", "scaffold a skmtc project", "watch a skmtc project", "configure enrichments", "publish a stack", "deploy to skmtc-hub" (the command is `publish`; there is no `deploy`), "skmtc in CI", or invokes any CLI subcommand. For *authoring* a generator package (Projections, Snippets, transform functions), defer to `skmtc-generator`. When something is broken (no output, wrong output, error messages, a failed bundle build), verify before proposing a fix: read the manifest and the parse issues, and reproduce the failure first.

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Aggregate friction log files across a time period to identify recurring patterns, classify each cluster by intervention type, produce a prioritized action plan with success criteria, and calculate convergence metrics. Complements `skmtc-retro` (which captures per-session signal) by acting as the system's actuator: turning accumulated observations into decisions. The primary output is a review document that makes the "is it getting better?" question answerable. Use this skill when the user asks to "review retros", "review friction", "run a retro review", "what's the pattern across sessions", "what should we fix next", or on a periodic cadence (monthly, pre-release). Also run after a cluster of sessions on the same feature area to surface systemic issues before they compound. Distinct from `skmtc-retro` — that skill captures per-session observations; this skill synthesizes them into decisions. Do not run this skill as a substitute for a per-session retro.

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。