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概要と使いどころ
Audit server-side paid-media measurement including server-side tag management, platform conversion APIs, event taxonomy, browser/server deduplication, consent, hashing, data quality, observability, and privacy. Use for server-side tracking, sGTM, server-side tagging, CAPI, Events API, event_id, pixel debugging, first-party measurement, or conversion data loss.
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。
Account-based marketing for B2B SaaS. Use this skill to build a target account list, tier it 1:1 / 1:few / 1:many, size it against sales capacity, turn intent and engagement signals into plays, write the multi-channel orchestration contract each tier runs on, and measure the program in account coverage, penetration and pipeline instead of MQLs. Also triggers on: ABM, account-based marketing, target account list, TAL, named accounts, account tiering, 1:1 ABM, one-to-few, programmatic ABM, account selection, ideal customer profile fit, intent data, buying signals, 6sense, Bombora, Demandbase, account penetration, account coverage, sales and marketing alignment on accounts, enterprise account program.
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。
Ensure design docs explicitly capture risks, assumptions, and open questions with owners, deadlines, and mitigation plans.
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。
Ensure ADRs capture context, decision, alternatives, tradeoffs, and follow-ups in a way that prevents future drift.
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。
Checks whether AI-assisted work defines review criteria, accessible context, explicit review loop, human judgment boundary, and feedback capture before delegating to an agent.
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。
Turn an approved meme idea, image, or short source into a concise humorous video draft with a clear hook, caption plan, and platform variants.
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。
Turn an approved topic into a narrated, non-presenter video using a script, voice asset, visual coverage, assembly, and captions.
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。
Find useful moments in long-form video, create ranked short clips, reframe them, and generate captions without losing source provenance.
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。
Turn approved video clips, audio, captions, and timing decisions into an ordered, reviewable draft while preserving a reproducible timeline.
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。
Turn one approved topic into a curiosity-led 3D educational short with character sheets, ordered keyframes, motion clips, narration, captions, and an auditable assembly 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。
Turn an authorized long-form video into a ranked set of YouTube Shorts candidates with vertical framing, captions, timestamps, and source provenance.
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。
Create a narrated editorial explainer from a topic, presenter, or photo using high-contrast paper-collage keyframes, motion, audio, and captions.
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。
Capture the creator's idea in their exact words. Use at the braindump stage, or when the user says "braindump", "let me talk this through", or "here is my idea".
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。
Check task processing statistics to see how much data and how many docs are moving. Use when checking throughput, diagnosing a stalled task, verifying data movement, or reporting on task activity. Use when user says "is data flowing", "how much data", "check throughput", "how many documents", "task not processing", "data not arriving", "stats", "bytes processed", "stalled capture", "no data in destination", "data volume", "task report", "usage report", or "how much data did we process".
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。
Create a BigQuery materialization to stream Estuary collections into BigQuery tables. Use when setting up BigQuery as a destination for captured data. Use when user says "send to BigQuery", "load into BigQuery", "materialize to BigQuery", or "BigQuery destination".
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。
Create a ClickHouse materialization to stream Estuary collections into ClickHouse tables. Use when setting up ClickHouse as a destination for captured data. Use when user says "send to ClickHouse", "materialize to ClickHouse", "ClickHouse destination", or "load into ClickHouse".
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。
Diagnose and fix SSH tunnel connection issues for Estuary captures and materializations. Use when seeing tunnel failures, connection timeouts, or permission denied errors. Use when user says "SSH tunnel failed", "connection timeout via tunnel", "permission denied SSH", "tunnel not working", "network-tunnel failed", "no pubkey loaded", "bastion host not connecting", or "RSA key format".
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。
Create a Redshift materialization to stream Estuary collections into Amazon Redshift tables. Use when setting up Redshift as a destination for captured data. Use when user says "send to Redshift", "materialize to Redshift", "Redshift destination", or "load into Redshift".
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。
Create a HubSpot materialization to write Estuary collections back into HubSpot CRM objects (Contacts, Companies, Deals, Tickets, etc). Use when setting up HubSpot as a destination for captured data. Use when user says "send to HubSpot", "materialize to HubSpot", "write back to HubSpot", or "HubSpot destination".
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。
Create a Snowflake materialization to stream Estuary collections into Snowflake tables. Use when setting up Snowflake as a destination for captured data. Use when user says "send to Snowflake", "materialize to Snowflake", "Snowflake destination", or "load into Snowflake".
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。
View publication history for catalog resources — who changed what and when. Use when investigating timeline of changes, finding who modified a connector, or correlating changes across capture/collection/materialization chains. Use when user says "what changed", "who changed", "publication history", "catalog history", "when was it published", "timeline of changes", or "what happened to my connector".
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。
Create a PostgreSQL materialization to stream Estuary collections into PostgreSQL tables. Use when setting up PostgreSQL as a destination for captured data. Use when user says "send to Postgres", "materialize to PostgreSQL", "Postgres destination", or "load into Postgres".
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。
Create a Databricks materialization to stream Estuary collections into Databricks tables via Unity Catalog. Use when setting up Databricks as a destination for captured data. Use when user says "send to Databricks", "materialize to Databricks", "Databricks destination", or "load into Databricks".
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。