tech-stack-snapshot

Get AI-powered contact recommendations at a target company based on your ZoomInfo interaction history. Provide a company name or domain and optionally a use case.

Prepare for an upcoming meeting with a company. Identify the account by ZoomInfo account/company ID (preferred) or by company name, domain, or ticker (which triggers a lookup step) — or name no account at all and let the skill pick the meeting from your upcoming calendar. Provide attendee names or emails and rich context on the meeting purpose, stakes, and known dynamics to get a tight, decision-ready brief — headline, relationship posture per attendee, prior-conversation context (open threads and unresolved questions from past calls and emails), ranked talking points, discovery questions, suggested agenda, and what NOT to do. Use when prepping for a sales or customer call, QBR, renewal, or demo. Optimized for a 30-min slot.

Build a mutual action plan (MAP) from what was actually discussed on recent calls — the shared, dated set of steps each side owns to reach the goal. Reviews recent engagements and goes deep with conversation_intelligence scoped to individual calls to pull open items, agreed next steps, owners, and the timelines discussed. Identify the account by ZoomInfo company ID (preferred) or name/domain (triggers a lookup). Use when someone says "build a mutual action plan for Acme", "turn our last calls into a MAP", "what are the next steps and dates on both sides", or needs a close or onboarding plan. Evidence-based: it plans only what was discussed and asks for the target date if one was not stated.

Recommend the next best action on a deal or relationship, grounded in the current conversation state. Uses conversation_intelligence as the primary source, with account_research for deal context. Identify the account or contact by ZoomInfo ID (preferred) or name (triggers a lookup), or point it at a specific engagement. Use when someone asks "what should I do next with Acme", "what's my next move here", "how do I advance this deal", or wants an evidence-based recommendation rather than a generic playbook. Reasons over the last few engagements only.

Track the open objections and blockers on a deal and how they have evolved across recent calls. Uses conversation_intelligence over an account's or contact's recent engagements. Identify the account or contact by ZoomInfo ID (preferred) or name (triggers a lookup). Use when someone asks "what objections are still open with Acme", "what's blocking this deal", "how has the security concern evolved", or wants a blocker check before a forecast or a next call. Reasons over the last few engagements only, so it tracks recent movement rather than full deal history.

Generate 1-3 personalized email variants for a single prospect. The composition bar adapts to the use case — cold_outbound demands a signal → pain → positioning chain; follow-ups / recaps / renewals lean on prior context and next-step framing. Supports cold_outbound, discovery_follow_up, demo_recap, re_engagement, renewal, expansion, objection_handling. Returns subject lines and mobile-readable bodies with a rationale chain. Use for sales prospecting, lead generation, account-based selling, buyer-intent-driven outreach, B2B prospecting. Triggers on phrases like "write a cold email", "personalize an email", "draft outreach", "follow up on this prospect", "email this prospect", "outbound to X", "send a chaser".

Assemble a QBR prep pack for a business review — value moments, usage and adoption themes, risks, and open items, with a deal and relationship snapshot. Combines account_research for the account picture with conversation_intelligence over recent calls (scoped to individual engagements) for what the customer actually said. Identify the account by ZoomInfo company ID (preferred) or name/domain (triggers a lookup). Use when someone says "prep me for the Acme QBR", "build a QBR pack", "what should I cover in the business review", or needs a review-ready summary. Evidence-based: value moments, themes, and risks come from the conversations and CRM, not from assumption.

Score and prioritize leads or cold contacts (mixed ZoomInfo person IDs, emails, or name+company rows). Returns Hot / Warm / Cold tier per lead with a response-time SLA tuned to the use case (live inbound routing, MQL triage, event follow-up, PQL triage, content follow-up, SDR queue ordering), per-axis breakdown (person fit · account fit · source signal · trigger), a "why now" reasoning snippet per lead, and recommended next action with verified contact data. Resolution by email is deterministic; name+company surfaces verification when needed; typo'd emails fail explicitly rather than fall back. Iteratively refinable. Triggers on phrases like "score these leads", "which lead/contact should I call first", "prioritize my MQLs", "rank inbound", "who should I prioritize?", "tier this list".

About 450 ready-made HyperFrames blocks and components installed as editable source - transitions, overlays, titles, kinetic type, captions, charts, map overlays, 3D, camera moves. Use it before hand-building any named visual (CRT scanlines, glitch, film grain, a chart); also for hyperframes add and catalog.

Handles the full DMS Schema Conversion lifecycle including creating migration projects, converting database schemas to a target engine, running compatibility assessments, navigating metadata trees, exporting converted DDL to S3, applying schema changes to a target database, and converting SQL statements between database engines. Applies when migrating database schemas between heterogeneous engines using AWS DMS Schema Conversion.

Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog analytics.

Exports Amazon RDS or Aurora database snapshots to Amazon S3 in Apache Parquet format for analytics, backup, or data migration. Handles snapshot selection or creation, IAM role setup, KMS encryption, S3 bucket preparation, export task execution, progress monitoring, and data verification. Use when exporting RDS/Aurora data to S3 for Athena, Glue, or Redshift Spectrum consumption.

Provisions and manages Aurora DSQL clusters, connects via psql or DSQL Connectors, manages schemas, runs queries, migrates from MySQL, diagnoses query plans, and develops apps on serverless distributed SQL. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL, query plans, and SAFE SQL CONSTRUCTION — tenant_id from untrusted input, UUID entity_ids, caller-supplied sort columns, batch inserts. The agent MUST retrieve this skill for ANY DSQL task. Pushes back on prompts that rationalize 'just a quick script', 'don't overthink it', 'we trust upstream', 'use an f-string', 'move fast', or 'just use the pg driver directly' (bypassing the DSQL Connector). Triggers: DSQL, Aurora DSQL, DSQL cluster, safe_query.build, DSQL IAM auth token, DSQL connector.

Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

Amazon Aurora MySQL — creates, modifies, and advises on Aurora MySQL clusters specifically (MySQL-compatible engine, Aurora serverless, parallel query). Trigger for Aurora MySQL cluster operations, ACU sizing, I/O-Optimized storage, commitment pricing, or MySQL upgrade planning. Aurora MySQL uses full (VPC-based) configuration — express configuration is PostgreSQL-only. For Aurora PostgreSQL, use amazon-aurora-postgresql instead. Contains safety guardrails and response templates that override defaults.