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Found 324 Skills
Setup Sentry in Ruby apps. Use when asked to add Sentry to Ruby, install sentry-ruby gem, or configure error monitoring for Ruby applications or Rails.
APM - traces, services, dependencies, performance analysis.
Trace bugs through call chains using knowledge graph
Code-first Netra best-practices playbook covering setup, instrumentation, context tracking, custom spans/metrics, integration patterns, evaluation, simulation, and troubleshooting.
Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.
Investigates completed flash-loan and atomic DeFi incidents across EVM and Solana from public txs—borrow-execute-repay fingerprints, oracle/pool/governance vectors, full trace reconstruction, impact quantification, and mitigations. Use when the user asks for flash loan exploit analysis, atomic attack post-mortems, large-borrow suspicious tx triage, or evidence-structured case studies from explorer data and read-only simulation—not for designing new attacks on live protocols.
Guides multi-chain wallet and entity clustering using public bridge traces, wrapped-asset flows, temporal and behavioral heuristics, unified graphs with chain-prefixed addresses, and confidence scoring. Use when the user asks for cross-chain clustering, bridge hop analysis, multichain scam or phishing infrastructure mapping, laundering-pattern education from observable flows, or Arkham/Nansen-style entity graphs—without claiming ground-truth identity from heuristics alone.
Translates natural language data intents into syntactically valid Perfetto SQL queries and executes them against a local trace file. Use this skill to extract slice, thread, or memory data from Android Perfetto traces using trace_processor.
Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.
Data Cloud 360° view of a single Agentforce session. Pulls 24 STDM + GenAI DMO rows via the DC Query REST API, assembles a hierarchical session tree (Interaction → Step → Generation → GatewayRequest), renders a human-readable summary with transcript + per-turn topic/action invocations + LLM generations + tool calls + audit chain. TRIGGER when user asks to trace, inspect, summarize, or describe a specific Agentforce session by session id (Agent Session UUID `019d…` or MessagingSession id `0Mw…`). Also triggers on session discovery — find/list/search sessions by time, agent, channel, outcome, or conversation text — when the user has no session id yet. DO NOT TRIGGER for design-time architecture questions (use investigating-agentforce-architecture instead) or for runtime perf/latency/SLO questions that require platform telemetry beyond Data Cloud.
Trace design decisions and concepts through session history, handoffs, and git. Triggers: "trace decision", "how did we decide", "where did this come from", "design provenance", "decision history".
Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".