Coding Agents Skill Use this skill as the entry point for any investigation or data question about AI Center Coding Agents. It identifies which data source each agent uses and tells you which reference files to load before querying. Supported Agents Agent Data Type Source Identifier Claude Code Metrics claude code metric family Claude Code (session text) Logs source ai sessions claude Codex Logs service.name == 'codex cli rs' or 'codex app server' Codex (latency) Spans $l.serviceName == 'codex cli rs' or 'codex app server' Cursor Spans $l.serviceName == 'cursor agent' Gemini CLI Metrics gemini cli metric family Copilot CLI (direct OTel) Spans $l.serviceName == 'github copilot' or tags['otel.scope.name'] == 'github.copilot' GitHub Copilot Collector Metrics github copilot metric family Loading References Load the agent reference first, then the shared query language files it depends on: Agent Agent Reference Shared References Claude Code (metrics) references/claude code.md references/promql guidelines.md + references/metrics querying.md Claude Code (sessions) references/claude code.md references/dataprime reference.md + references/logs querying.md Codex references/codex.md references/dataprime reference.md + references/logs querying.md + references/spans querying.md Cursor references/cursor.md references/dataprime reference.md + references/spans querying.md Gemini CLI references/gemini cli.md references/promql guidelines.md + references/metrics querying.md Copilot CLI (direct OTel) references/copilot cli.md references/dataprime reference.md + references/spans querying.md GitHub Copilot Collector references/copilot cli.md references/promql guidelines.md + references/metrics querying.md Cross agent comparison All agent references All shared references Safety All query commands ( cx metrics , cx logs , cx spans , cx dataprime ) are read only. They never modify data and can be run freely without yes . CLI Commands Command Purpose When to Use cx metrics search name '<pattern ' Find metrics by name Discover available claude code , gemini cli , github copilot metrics cx metrics query '<expr ' Instant PromQL query Point in time metric snapshot cx metrics query range '<expr ' Range PromQL query (time series) Claude Code, Gemini CLI, GitHub Copilot Collector trends cx logs '<dataprime query ' Log query Claude Code session text, Codex logs cx spans '<dataprime query ' Span query Cursor, Codex latency, Copilot CLI direct OTel cx dataprime query '<query ' Raw DataPrime query When you need an explicit source logs or source spans in the query Output format: append o toon to any command when passing results to an agent or further processing. Multi profile: use p <profile (repeatable) to fan out across profiles simultaneously. First Response Identify the requested agent and analysis goal. If any required scope is missing, ask before querying: Agent: Claude Code, Codex, Cursor, Gemini CLI, or Copilot CLI. Time range. Optional filters: application, subsystem, user, model, session, repository. Output shape: quick answer, table, trend, top N ranking, investigation notes, or query only. If the user says "GitHub Copilot Codex", clarify whether they mean Codex or Copilot CLI. AI Center has separate Codex and Copilot CLI dashboards. Use the same data source as the corresponding dashboard whenever possible: Claude Code metrics: PromQL over claude code metrics. Claude Code session text: DataPrime over ai sessions claude logs. Codex: DataPrime over Codex logs for tokens, sessions, models, users, and tools; spans only for run turn latency. Cursor: DataPrime over Cursor spans. Cursor reports prompt length and file edits, not provider token counts. Gemini CLI: PromQL over gemini cli metrics. Copilot CLI direct OTel: DataPrime over Copilot spans. GitHub Copilot Collector: PromQL over github copilot org/user/billing metrics when that collector data is present. Time and Filters PromQL (Claude Code, Gemini CLI, GitHub Copilot Collector) Use increase(metric[<range ]) for counters over the selected window. Common label filter pattern: Use sum by (...) , count by (...) , or topk(N, ...) for grouping. For chart trends, use an interval window based on the selected range rather than the full range. DataPrime (Codex, Cursor, Copilot CLI direct, Claude Code sessions) Add filter ... clauses for user, model, application, subsystem, session, and operation. Prefer groupby ... aggregate ... for dashboard like tables. For spans: app/subsystem labels are $l.applicationName and $l.subsystemName (mixed case). For logs: app/subsystem labels are $l.applicationname and $l.subsystemname (lowercase). App/subsystem filter pattern: Cross Agent Questions When comparing agents: Normalize units before comparing: cost in USD, tokens as input/output/total, runtime in milliseconds, sessions as distinct conversations. State which signals are not equivalent. Cursor prompt length is not a provider token count. Direct Copilot CLI users are pseudonymous IDs ( enduser.pseudo.id ), not email addresses. GitHub Copilot Collector users may be logins, names, or emails. Prefer per agent sections over a single merged table when dimensions differ. For "which agent is most used": compare sessions and users first, then tokens or span counts with caveats. For "which agent costs the most": Claude Code has first class USD metrics. Copilot direct spans expose github.copilot.cost . Collector billing metrics expose net/gross/discount amounts by organization/SKU. Do not merge Copilot span cost and collector billing without explaining the source and unit difference. Answer Style Always include: The agent and data source used. The query or a compact query excerpt. The time range and filters applied. A plain language interpretation of the result. Caveats about empty data, approximate counts, pseudonymous users, missing labels, or non comparable metrics. Do not invent fields not listed in the agent reference files. If the user asks for data an agent does not emit, say what is available and propose the nearest supported query.

cx-coding-agents