Skills

investigating-error-issue

Investigates a single PostHog error tracking issue end-to-end. Use when the user provides an issue ID or pastes an issue URL (`/error_tracking/<id>`) and wants to understand the error — who it affects, what triggers it, when it started, whether it correlates with a release, browser, OS, or feature f

querying-posthog-data

Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query anal

signals-scout-apm

Signals scout for PostHog distributed tracing (APM / OpenTelemetry spans). Watches RED metrics per (service, operation) — error rate, p95 latency, request volume — for regressions, new error signatures, and traffic cliffs, and files each validated regression as a report in the inbox.

huggingface-trackio

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output f

triaging-error-issues

Triage PostHog error tracking issues during a daily or on-call review. Use when the user asks "what's broken?", "what new errors do we have?", "show me top errors today", "what should I look at this morning", or wants a prioritized list of active issues to work on. Surfaces new and high-impact issue

amazon-opensearch-service

Amazon OpenSearch Service and Serverless across five capabilities — migration (Solr/ES/self-managed OpenSearch into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, storage tiers, FGAC, monitoring); search (vector / semantic / hybrid / RAG with B

datarobot-model-monitoring

Tools and guidance for monitoring model performance, tracking data drift, managing model health, and detecting prediction anomalies. Use when monitoring deployed models, tracking drift, or investigating prediction anomalies.

logfire-instrumentation

Add Pydantic Logfire observability to applications and send as much useful telemetry as possible. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", "set me up properly", "send as much data as

qdrant-monitoring

Guides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', or needs to set up Prometheus, Grafana, or health checks. Also use when debugging production

domino-jobs

Create, run, and manage Domino Jobs - batch executions for scripts, training, and data processing. Covers job configuration, hardware tiers, scheduled jobs (cron), monitoring status, viewing logs, and API-driven execution. Use when running batch workloads, scheduling recurring tasks, or automating t

create-honeycomb-board

Design and then create a board (dashboard) in Honeycomb with queries and SLOs. Trigger phrases: "create a board", "make a board", "build a dashboard", "create a Honeycomb board", "make a dashboard in Honeycomb", "set up a board", "dashboard for my service", "visualize service health", "golden signal

sentry-setup-ai-monitoring

Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, track conversations, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI/Pydantic AI/Laravel AI. Detects installed AI SDKs and configures appropriate integrations.

observability-fundamentals

First principles behind observability — wide events, high cardinality, the core analysis loop, events vs metrics vs logs, and how instrumentation connects to debugging outcomes. Grounds recommendations in first principles rather than tool-specific how-to. Trigger phrases: "what is observability", "w

qdrant-performance-optimization

Navigation hub linking sub-skills for proactive Qdrant tuning: search speed, indexing performance, and memory usage optimization. Use when planning configuration or capacity changes to improve speed and efficiency. For diagnosing an active production slowdown or analyzing live metrics, use qdrant-mo

read-memories

Search past Claude Code session logs to recall prior decisions, patterns, or unresolved work. Use when user says "do you remember", "what did we do", references past conversations, or you need context from prior sessions.

signals-scout-logs

Signals scout for PostHog logs. Watches for emerging and rate-shifted message patterns (window-over-window deltas), volume bursts, severity-distribution shifts, service silence, and trace-correlated bursts.

aidp-audit

Manage and search AIDP audit logs — enable/disable auditing, set retention, and query audit-log entries for a DataLake. Use when the user asks about audit logging, who did what, compliance/retention of AIDP activity, or wants to search audit events. Self-contained — official `aidp audit` CLI preferr

agentforce-observe

Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; want

sentry-get-started

Guided entry point for using Sentry through your agent. Orients you to your current setup and, for a new project, sets up Sentry end to end with sane defaults — provision a project, install the SDK (errors, tracing, and whatever it enables by default), and confirm real telemetry reaches Sentry. Rout

aidp-migrate-job

Run the full Databricks→AIDP migration against a manifest. Pass-1 walks the %run dep tree and rewrites Databricks APIs in each dep notebook code-only. Pass-2 executes each task cell-by-cell on a live AIDP cluster, runs 4-way verify (exec error / stderr patterns / Spark logs / Opus eval), and re-atte

modeling-pigment-applications

Always use this skill when designing or modifying Pigment applications. Provides the mental model of a Pigment app (Application, Dimensions, Calendars, Metrics, Transaction Lists, Tables), the core concepts (dimension list vs property vs transaction list, metric vs table, sparsity, scope), the canon

signals-scout-error-tracking

Signals scout for PostHog error tracking. Watches `$exception` bursts, stuck loops, multi-fingerprint clusters, and status regressions, and files each validated issue as a report in the inbox.

sentry-instrument

Instrument an application with Sentry — detect the platform, install and initialize the SDK if needed, and wire up any signal — error monitoring, tracing/performance, logging, metrics, profiling, session replay, user feedback, cron check-ins, and AI/LLM monitoring. Use to add Sentry to a project or

aws-observability

Builds, configures, debugs, and optimizes AWS observability with CloudWatch (Log Insights, Metrics, Alarms, Dashboards, EMF), X-Ray, CloudTrail, and ADOT (AWS Distro for OpenTelemetry), AND enables/onboards services to Application Signals using ADOT auto-instrumentation SDKs. Covers Log Insights que