Topic · Automation and Agents
Agent Tracing and Observability skills for Claude Code and Codex
Skills for seeing what an LLM application or coding agent did: tracing and instrumentation with Arize, Phoenix, LangSmith and NVIDIA NeMo Relay, session audits for Claude Code and Codex, run replays, and usage reports on tokens and cost. They turn a vague complaint about agent behavior into traces and spans an agent can read.
Highest-scoring agent tracing skills in the October 22, 2026 snapshot: bmad-run-inspector from tronghieu/agent-skills (90/100), orca-replay from Continuum-AI-Corp/OrcaReplay (86/100) and hn-summarize from ykdojo/claude-code-tips (80/100). Scores come from the Suede Skill Check.
- 41 listed skills
- 16 repositories
- 8 with full pages
24 from official publishers. 4 bundle scripts. 1 connect to an outside service. Listed in AI Agents and LLM Engineering (31), Productivity and Knowledge Work (3), Testing and QA (2), Product Management (2), Developer Tools and Frameworks (1), Frontend and Design (1), DevOps and Cloud (1).
All 41 agent tracing skills
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| Skill | Repository | What it does |
|---|---|---|
| agenttrace-session-audit 78/100 · C | luoyuctl/agenttrace | Audits local AI coding-agent sessions from tools like Claude Code and Codex CLI with agenttrace, checking cost, tokens, tool failures, latency, and anomalies. |
| bmad-run-inspector 90/100 · A | tronghieu/agent-skills | Inspects and explains bmad-loop run artifacts for live health checks and post-run forensics, such as why a story failed or where tokens went. |
| conversation-analyzer 77/100 · C | mhattingpete/claude-skills-marketplace | Analyzes Claude Code conversation history to identify patterns, common mistakes, and opportunities for workflow improvement and automation. |
| hn-summarize 80/100 · B | ykdojo/claude-code-tips | Fetches and summarizes Hacker News and hckrnews.com top stories, articles, and their comment threads. |
| mine 75/100 · C | ohad6k/emulo | Runs, sets up, updates, re-mines, or deepens Emulo from the user's real local AI coding-session history, only when explicitly asked. |
| orca-replay 86/100 · B | Continuum-AI-Corp/OrcaReplay | Answers questions about a past agent run from its recording rather than memory, and replays or forks that run. |
| skill-miner 76/100 · C | hqhq1025/skill-optimizer | Mines coding-agent session history, archived transcripts, memories, and repeated local work to discover recurring workflows that should become new Agent Skills. |
| summarize 68/100 · D | swarmclawai/swarmclaw | Summarizes or extracts text and transcripts from URLs, podcasts, YouTube videos, and local files using the summarize CLI. |
| arize-ai-provider-integration 82/100 · B | github/awesome-copilot | Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports… |
| arize-annotation 82/100 · B | github/awesome-copilot | Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize… |
| arize-dataset 82/100 · B | github/awesome-copilot | Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation… |
| arize-evaluator 68/100 · D | github/awesome-copilot | Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing… |
| arize-experiment 82/100 · B | github/awesome-copilot | Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing… |
| arize-instrumentation 82/100 · B | github/awesome-copilot | Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement… |
| arize-link 77/100 · C | github/awesome-copilot | Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable… |
| arize-prompt-optimization 82/100 · B | github/awesome-copilot | Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers… |
| arize-trace 79/100 · C | github/awesome-copilot | Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting… |
| ax-narrate 76/100 · C | Necmttn/ax | Write the agent-generated narration of the current session - the reviewable story of what changed, including what never reaches a PR (user… |
| crystallize 71/100 · C | avelikiy/great_cto | Distils repeating patterns from session logs and lessons.md into draft skill files. Run after ≥10 sessions to extract durable knowledge. Output… |
| half-clone 64/100 · D | ykdojo/claude-code-tips | Clone the later half of the current conversation, discarding earlier context to reduce token usage while preserving recent work. |
| langsmith-observability 69/100 · D | Orchestra-Research/AI-Research-SKILLs | LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets… |
| nemo-relay-debug-runtime-integration 78/100 · C | NVIDIA/skills | Use this skill when NeMo Relay is installed or imported but application-side runtime behavior is missing or incorrect, including load failures… |
| nemo-relay-get-started 81/100 · B | NVIDIA/skills | Use this skill when first-time NeMo Relay users want to try Relay, choose the least-complex supported quick start, or verify initial value through… |
| nemo-relay-install 81/100 · B | NVIDIA/skills | Use this skill when choosing or running NeMo Relay installation for the CLI, Python, Node.js, Rust, OpenClaw, Hermes, or maintained framework… |
| nemo-relay-instrument-calls 78/100 · C | NVIDIA/skills | Use this skill when an application owns tool or LLM/provider call sites and needs to wrap them with NeMo Relay scopes and managed execution APIs for… |
| nemo-relay-instrument-context-isolation 75/100 · C | NVIDIA/skills | Use this skill when concurrent requests, async tasks, threads, workers, goroutines, or agents need independent NeMo Relay scope stacks and correct… |
| nemo-relay-instrument-typed-wrappers 75/100 · C | NVIDIA/skills | Use this skill when adding NeMo Relay typed wrappers, domain types, or provider codecs while preserving JSON middleware semantics and caller-visible… |
| nemo-relay-migrate-from-flow 80/100 · B | NVIDIA/skills | Use this skill when migrating applications, examples, integrations, documentation, manifests, or repository code from NeMo Flow to NeMo Relay across… |
| nemo-relay-plugin-adaptive-tuning 81/100 · B | NVIDIA/skills | Use this skill when baseline NeMo Relay instrumentation exists and the user wants to configure or evaluate adaptive plugin behavior, including… |
| nemo-relay-plugin-build 78/100 · C | NVIDIA/skills | Use this skill when building or packaging reusable NeMo Relay runtime behavior as an embedded configuration component or a manifest-backed… |
| nemo-relay-plugin-observability 81/100 · B | NVIDIA/skills | Use this skill when choosing or configuring NeMo Relay 0.6 or 0.7 observability through the built-in plugin, subscribers, or exporters, including… |
| phoenix-cli 85/100 · B | github/awesome-copilot | Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect… |
| phoenix-evals 67/100 · D | github/awesome-copilot | Build and run evaluators for AI/LLM applications using Phoenix. |
| phoenix-observability 69/100 · D | Orchestra-Research/AI-Research-SKILLs | Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running… |
| phoenix-tracing 82/100 · B | github/awesome-copilot | OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or… |
| quarter-clone 64/100 · D | ykdojo/claude-code-tips | Clone the last quarter of the current conversation, discarding earlier context to reduce token usage while preserving recent work. |
| session-report 67/100 · D | anthropics/claude-plugins-official | Generate an explorable HTML report of Claude Code session usage (tokens, cache, subagents, skills, expensive prompts) from ~/.claude/projects… |
| summarize-interview 60/100 · D | infrasity-labs/dev-gtm-claude-skills | Summarize a user interview transcript into structured insights with key themes, quotes, and action items. Use after conducting user interviews to… |
| summarize-interview 75/100 · C | phuryn/pm-skills | Summarize a customer interview transcript into a structured template with JTBD, satisfaction signals, and action items. Use when processing… |
| summarize-meeting 78/100 · C | phuryn/pm-skills | Summarize a meeting transcript into structured notes with date, participants, topic, key decisions, summary points, and action items. Use when… |
| vss-summarize-video 85/100 · B | NVIDIA/skills | Use to summarize a recorded video via the LVS summarization microservice (HITL-gated) with a VLM fallback. Not for report generation or live RTSP… |
How to choose agent tracing skills
For your own LLM app, pick the tracing platform you will keep. Arize, Phoenix and LangSmith each have instrumentation plus trace-reading skills; installing both halves from one vendor gives the agent a full loop from capture to diagnosis.
For coding-agent sessions, local tools such as agenttrace, session-report and conversation-analyzer read history files already on your machine and need no service account.
Hosted tracing sends prompts and outputs to the vendor. Check the environment variables under Before you install and the vendor's data retention settings before you trace production traffic.
Related topics
Summarizer 16Observability and Monitoring 54Codex, Cursor and Coding Agents 23Context Engineering and Agent Memory 31Evals and Benchmarks 27Skill Authoring 37Claude Code Setup and Hooks 31LLM APIs and SDKs 28
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