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Found 333 Skills
This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.
APM - traces, services, dependencies, performance analysis.
Deep test, analyze, and audit Claude skills. Use this skill whenever the user wants to test a skill's behavior, analyze how it uses the Claude API, inspect inputs/outputs from scripts, or run security and code review audits against skill scripts. Trigger on: "test my skill", "analyze this skill", "audit skill scripts", "review skill for security issues", "what does this skill actually do when it runs", "inspect API calls from skill", "run a skill through its paces", "check my skill for bugs or vulnerabilities". Also trigger when the user shows you a SKILL.md and asks you to evaluate, critique, or stress-test it.
Trace bugs through call chains using knowledge graph
Supported programming languages in GrepAI. Use this skill to understand which languages can be indexed and traced.
Navigate, search, and understand the Resume Matcher codebase using ripgrep, ack, or grep. Find functions, classes, components, API endpoints, trace data flows, and understand architecture. Use FIRST when exploring code, finding files, or understanding project structure.
Debugging toolkit for AI agents. Diagnose symptoms via memory cache -> behavior cache -> codebase search, trace data flow, git-bisect bad commits, and compare output directories.
Step-by-step wallet investigation workflow using Range AI MCP tools (risk score, sanctions, connections, transfers, funded-by, entities, cross-chain pivots) plus a one-shot prompt template. Use when the user runs investigations inside an MCP-connected client with Range enabled, or needs a structured checklist alongside crypto-investigation-compliance—not as legal advice or a substitute for Range’s live docs and API scopes.
Auditing memory traces and debugging.
Capture a full DevTools-protocol trace of any browser automation — CDP firehose, screenshots, and DOM dumps — then bisect the stream into per-page searchable buckets. Use when the user wants to debug a failed run, audit network/console/DOM activity, attach a trace to an in-progress session, or feed structured per-page summaries back into an agent loop so its next iteration learns from the last one.
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.
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".