create-honeycomb-board

Migrating from Honeycomb Beelines (End of Life) to OpenTelemetry. Trigger phrases: "migrate from Beelines", "upgrade from Beeline to OpenTelemetry", "migrate to OTel", "replace Beelines", "Beeline end of life", "Beeline EOL", "switch from Beeline to OTel", "migrate Go/Python/Node/Java/Ruby Beeline", "W3C trace headers", "W3C propagation", "incremental migration to OpenTelemetry".

Querying OpenTelemetry metrics datasets in Honeycomb, whose rules differ from trace/event datasets: bare COUNT, RATE_SUM, RATE_AVG, RATE_MAX and CONCURRENCY are forbidden, temporal aggregation is automatic, and each metric has its own attributes. Use for gauges, counters, histograms and sums; temporal aggregation (RATE, INCREASE, SUMMARIZE, LAST); finding the metrics dataset, metric names and attributes; unexpected metrics results; infrastructure metrics (CPU, memory, disk I/O, network). Not for instrumenting metrics (use otel-instrumentation), event datasets with "metrics" in their name, or conceptual questions (use observability-fundamentals).

First principles of observability: wide events, high cardinality, the core analysis loop, events vs metrics vs logs, and how instrumentation connects to debugging. Trigger phrases: "what is observability", "why observability", "why Honeycomb", "events vs metrics vs logs", "why wide events", "what is high cardinality", "core analysis loop", "observability vs monitoring", "what is dimensionality", "explain observability".

Instrumenting GenAI/LLM applications with OpenTelemetry for Honeycomb, including content capture and agent failure detection. Trigger phrases: "instrument my GenAI app", "add tracing to LLM calls", "trace AI agent", "instrument OpenAI", "instrument Anthropic", "GenAI observability", "trace tool calling", "LLM token usage", "instrument embeddings", "trace MCP", "GenAI metrics", "instrument LangChain", "add GenAI spans", "capture prompts", "capture LLM responses", "enable GenAI content capture", "streaming tracing", "trace streaming responses".

OpenTelemetry SDK setup, custom instrumentation, and sending data to Honeycomb. Trigger phrases: "instrument my app", "add tracing", "set up OpenTelemetry", "configure OTel", "add custom spans", "add attributes to spans", "send traces to Honeycomb", "set up OTLP", "configure sampling", "add span events", "add span links", "set up tracing for [any language]", "configure the OTel Collector".

Retrofitting OpenTelemetry into an existing, uninstrumented application. Trigger phrases: "migrate existing app to OTel", "add OpenTelemetry to existing project", "retrofit OTel into my codebase", "thread context through my code", "context propagation", "bridge Prometheus metrics to OTel", "logging bridge", "migrate logging to OTel", "slog bridge", "logback bridge", "verify my instrumentation", "traces are disconnected", "orphaned spans", "migrate to OpenTelemetry", "OTel migration plan", "how do I sequence an OTel migration", "add tracing to existing code", "refactor for context propagation", "Fiber context gotcha", "keep existing logging working with OTel", "add OTel without breaking Prometheus", "bridge existing metrics", "coexist with existing monitoring". For greenfield instrumentation use otel-instrumentation; for Beelines use beeline-migration.

Workflows for investigating production issues in Honeycomb: context priming, broad query, BubbleUp, trace analysis, verification, and chaining results between steps to a root cause. Trigger phrases: "investigate production issue", "debug latency spike", "find root cause", "use BubbleUp", "analyze traces", "debug an outage", "why is my API slow", "errors are increasing", "health check", "SLO burning".

Constructing and interpreting Honeycomb queries on trace, log and event datasets: operation selection (percentiles not AVG, HEATMAP for distributions), relational fields (root., parent., any., none.), calculated fields, query math, and reading results (P99/P50 ratios, heatmap bands, TOTAL/OTHER rows, raw JSON via query_result_json). Trigger phrases: "show me latency", "error rate", "find slow requests", "find outliers", "interpret results", "relational fields", "calculated fields", "download raw results". For metrics datasets (dataset_type=metrics) use metrics-queries.

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.

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.

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.

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.

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.