timescale

Convert blog posts and Tiger Den content into native long-form articles for LinkedIn, X (Twitter), or both — with matching social posts. Produces platform-appropriate outputs: LinkedIn gets an SEO title, SEO description, article body with closing CTA, and a social post; X gets an article body and a companion post. Handles both Tiger Den content items and raw blog drafts. Use this skill when the user asks to turn a blog post into a LinkedIn article, write an X article, write a Twitter article, create long-form content for social platforms, or says 'social article,' 'LinkedIn article,' 'X article,' or 'repurpose this for LinkedIn/Twitter.' For short social posts only (not articles), use social-post-writer instead.

Enrich event booth-scan lead CSVs with Common Room firmographics, score them by ICP fit and conversation heat, flag Tiger Data customers, and produce a tiered xlsx for SDR follow-up. Use this skill when the user asks to enrich event leads, score booth scans, process a post-event lead list, or run lead enrichment for a conference/trade show. Also trigger when they mention 'GrafanCON leads', 'Hannover Messe leads', 'booth scan enrichment', 'tier the leads', or provide a lead CSV after an event.

Verify factual claims in a finished SEO article draft via web search. Use after de-slop and before publishing. Produces a structured claim-check report file alongside the article — does not modify the article itself. Trigger when the seo-writer pipeline reaches the verification step, or when a user asks to 'fact-check', 'verify claims', or 'check the article' before publishing.

Write SEO-optimized articles from a brief with automated Tiger Docs verification, competitive fact-checking, internal link discovery, and auto-triggered content review. Use this skill when the user says 'SEO article', 'SEO content', 'GEO article', 'AEO article', 'SEO brief', or 'content brief for SEO'. Does NOT trigger for generic blog posts, comparison posts, or roundups unless explicitly labeled SEO/GEO/AEO.

Plan social media campaigns and draft individual posts for LinkedIn and X (Twitter), including repurposing existing content into platform-native social posts. Use this skill when the user asks to create a social media calendar, plan a LinkedIn content series, write social posts, draft a tweet or X thread, repurpose a blog post for social, turn an article into LinkedIn posts, create a social campaign, or plan social content cadence. Also trigger when they mention social media strategy, content calendar, post series, LinkedIn carousel outline, X thread, hashtag strategy, or ask 'what should we post about [topic]?' For voice and tone guidance, this skill defers to brand-voice-writer. For social posts within an event campaign, use event-brief-planner instead.

Plan and write complete event campaign briefs for Tiger Data -- event overview, full campaign copy (landing pages, emails, social posts, ads, banners, newsletters), and KPI tracking. Use this skill when the user asks to plan an event campaign, create an event brief, write event marketing assets, or fill out the event campaign template. Also trigger when they mention field event, tradeshow, conference, webinar, event landing page, pre-event email, post-event email, event social posts, event paid ads, booth marketing, event campaign copy, or 'what marketing do we need for [event]?' For writing individual assets in isolation (not as part of an event brief), use brand-voice-writer instead.

Plan and draft the bi-weekly Tiger Data developer newsletter. Use this skill when the user asks to write a newsletter, plan a newsletter edition, draft newsletter copy, create a newsletter issue, assemble newsletter content, or says 'it's newsletter time.' Sources recent blog posts, tutorials, case studies, and community content from Tiger Den, then drafts each section in brand voice with UTM-tagged links. Also trigger when the user mentions email newsletter, developer newsletter, bi-weekly newsletter, newsletter content, or newsletter draft.

Surface paid placement opportunities — newsletter ad slots and podcast sponsorships — for Tiger Data marketing campaigns. Use this skill when the user asks to find newsletter placements, find podcasts to sponsor, find media opportunities, find sponsorship opportunities, scout newsletters or podcasts, or asks 'where should we advertise [topic]' or 'find dev newsletters [topic].' Operates in two modes: brief-driven (for a specific campaign) and browse (category sweep). Returns a tight chat summary plus a structured markdown doc on disk. Do NOT use for writing ad copy (use newsletter-ad-writer), post-campaign performance evaluation (use newsletter-ad-review), event sponsorship strategy (use event-brief-planner), or earned/guest opportunities, KOL sourcing, and video sponsorships (all v2+, not yet supported).

Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications

Use this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.

Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.

Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.

Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series

Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

Update workflow implementation from its embedded description. Use this after modifying workflow or node descriptions.

Collaborative workflow design - guides users through creating well-structured crayon workflows with embedded descriptions.

Generate integration nodes for external APIs (Salesforce, HubSpot, etc.). Creates typed function nodes with authentication and error handling.

Refine node definitions - determines HOW each node is implemented (SDKs, libraries, input/output structures, tools, guidelines).

How to trigger workflows and nodes. Covers MCP tools (run_workflow, run_node), HTTP webhooks (async/sync), authentication, and cron scheduling.

Run unit tests for one or all collection clients and report results

Static review of collection-client code against the UNS data contract in CLAUDE.md

Run end-to-end pipeline validation — query TimescaleDB after collection and report

Write an integration guide for the Tiger Data Docs site following established conventions. Use when documenting how to connect a third-party tool (database client, BI tool, streaming platform, observability stack, ETL connector, etc.) to Tiger Cloud or self-hosted TimescaleDB.

Write a tutorial for the Tiger Data Docs site following established conventions. Use when creating a new tutorial, cookbook, or step-by-step guide for the /build/examples section — especially for database, search, AI, or data engineering topics.

Validate that a tutorial follows the write-tutorial format and that all code runs correctly. Use when reviewing, finalizing, or QA-ing a cookbook/tutorial before shipping. Checks README structure, file completeness, code runnability, and consistency.

Write a consistent, step-by-step technical tutorial. Use when creating a new cookbook, guide, or walkthrough — especially for database, search, or data engineering topics. Produces a README, supporting scripts, and a setup file following a proven structure.

How to discover, read, write, and search data in a TigerFS-mounted PostgreSQL database using file tools.

Comprehensive OpenTUI skill for building terminal user interfaces. Covers the core imperative API, React reconciler, and Solid reconciler. Use for any TUI development task including components, layout, keyboard handling, animations, and testing.

Information and guidelines for building custom skills for the MCP server.

Convert, evaluate, and deploy Sigma detection rules across SIEM backends. Covers sigma-cli (pySigma) for converting rules to Splunk SPL, Elasticsearch Lucene/ES|QL, Microsoft Sentinel KQL, QRadar AQL, CrowdStrike, and 20+ other backends. Covers rsigma for direct real-time evaluation against JSON events, linting (65 rules with --fix), validation, and running a detection daemon with correlation, hot-reload, and Prometheus metrics. Includes pipeline-to-backend mapping for ECS, Splunk CIM, Sysmon, and other field schemas. Use this skill whenever the user mentions converting Sigma rules, Sigma backends, sigma-cli, rsigma, SIEM queries, SPL, KQL, Lucene, AQL, LEQL, field mapping, ECS mapping, CIM mapping, detection engineering tooling, or asks how to run, test, validate, lint, or deploy Sigma rules -- even if they don't explicitly name a tool.

Author Sigma detection rules, correlation rules, filter rules, and processing pipelines from natural language descriptions. Covers the full Sigma v2.1.0 specification including logsource, detection blocks, field modifiers, condition expressions, multi-document YAML, correlation types (event_count, value_count, temporal, temporal_ordered, value_sum, value_avg, value_percentile, value_median), filter injection, and pySigma-compatible pipelines. Use this skill whenever the user mentions Sigma rules, SIEM detection, detection engineering, detection-as-code, SigmaHQ, correlation rules, Sigma filters, Sigma pipelines, field modifiers, logsource, or asks to write, review, or fix detection rules -- even if they don't explicitly say 'Sigma'.