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Claude Code usage telemetry and repository CI status are two separate signals: OpenTelemetry can report Claude Code sessions, costs, tokens, and tool activity, while GitHub Actions runs automation and reports workflow checks on repository changes. Configure them independently, then verify each at its destination.

What do “Claude Code usage” and “CI status” mean?

Claude Code telemetry is application data about usage, such as session counts, token and cost metrics, and tool-activity events. Anthropic documents exporting this data through OpenTelemetry (OTel) for organizational monitoring. Anthropic’s Monitoring guide covers the telemetry pipeline.

CI status is the result of a repository workflow: whether a job started, ran, and passed or failed for a particular event or change. GitHub Actions can run Claude Code, but enabling telemetry does not configure a GitHub workflow, and installing the GitHub Action does not automatically send telemetry to your monitoring backend. Treat these as separate setups. Anthropic’s Claude Code GitHub Actions guide documents the workflow integration.

How do I track Claude Code usage across my team?

Enable telemetry and select exporters

Anthropic’s quick start uses environment variables to enable telemetry and choose which signals to export. Set CLAUDE_CODE_ENABLE_TELEMETRY=1, then configure OTEL_METRICS_EXPORTER and/or OTEL_LOGS_EXPORTER. For an OTLP destination, set the exporter protocol and endpoint; configure authentication headers too if your collector requires them.

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export CLAUDE_CODE_ENABLE_TELEMETRY=1
export OTEL_METRICS_EXPORTER=otlp
export OTEL_LOGS_EXPORTER=otlp
export OTEL_EXPORTER_OTLP_PROTOCOL=grpc
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
claude

This is an example configuration, not a universal production endpoint. Replace it with the endpoint and any credentials required by your organization’s collector. Anthropic also documents Prometheus and console exporters; console output is useful for debugging, while a team may prefer an existing OpenTelemetry-compatible collector and observability stack. Choose based on what your backend accepts and how much telemetry you want to retain.

Verify data in the backend

Start a Claude Code session and inspect the configured destination. Anthropic’s guide identifies claude_code.session.count as a metric to look for after session start. If exporting logs only, submit a prompt and look for the claude_code.user_prompt event. Seeing the expected signal in the backend is stronger evidence of delivery than merely seeing exporter variables in a shell.

If data does not arrive, start Claude Code with a debug log and inspect it for instrumentation or exporter errors:

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claude --debug-file /tmp/claude-code-debug.log

Use an appropriate path for your environment and consult Anthropic’s Monitoring documentation for supported settings and exporter configuration.

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How do I see Claude Code costs in our telemetry dashboard?

Anthropic’s OpenTelemetry guidance is intended to help organizations monitor usage, costs, and tool activity. To make dashboards useful across a team, administrators can distribute telemetry configuration through managed settings and attach resource attributes such as department, team, or cost center using OTEL_RESOURCE_ATTRIBUTES.

Follow the documented comma-separated key=value format exactly. In Anthropic’s described setup, each custom attribute becomes a metric label; values with many distinct variants can create high cardinality and increase storage costs in the monitoring backend. Prefer stable, low-cardinality labels such as a fixed team name over per-user or per-session values unless the operational need justifies the tradeoff.

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Choose how much content to export

Telemetry can contain sensitive organizational data. Anthropic documents controls for prompt content, assistant responses, tool details, tool content, managed settings, and raw API-body events. Prompt and response content are disabled by default in the documented settings. Enabling raw API-body events includes the entire conversation history, so assess the data exposure, who can access it, and the backend’s retention and access controls before enabling detailed content export.

When should we use an LLM gateway?

A gateway is a separate architectural choice when an organization needs centralized usage attribution, budgets or rate limits, or audit logs. Anthropic describes these as gateway use cases, but a gateway introduces infrastructure your organization operates and must keep compatible as Claude Code evolves. Anthropic also states that third-party gateways are not endorsed, maintained, or audited by it; evaluate a gateway’s security and capabilities independently rather than assuming them.

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How do I add Claude Code to GitHub Actions?

Use the official action for repository events

The official Claude Code GitHub Action supports workflows that respond to an @claude mention on an issue or pull request, as well as prompt-driven automation attached to a GitHub event. Anthropic’s documented manual setup is to install the Claude GitHub App, make an API key or OAuth token available as a secret, and add a workflow file under .github/workflows/. Teams can use organization-level secrets and reusable workflows for broader rollout. See the official GitHub Actions guide for the current action configuration and examples.

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Choose authentication deliberately. The documented options include an API key, a subscription OAuth token, and workload identity federation configuration, depending on deployment. Anthropic notes that an OAuth token is tied to the subscription of the person who generated it; it should not be treated as an organization-wide shared credential. For a secret intended to be shared across repositories, Anthropic recommends API-key authentication.

Use the CLI for custom non-interactive jobs

For scripts or custom automation outside the official action, Claude Code offers non-interactive prompt mode:

claude -p "Summarize the changes in this pull request"

The -p mode is distinct from launching a regular interactive claude session. The CLI reference documents controls including --allowedTools, --disallowedTools, JSON output formats, and turn limits. Grant only the tools a job needs, and avoid printing secrets or credentials into workflow logs.

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Why doesn’t CI run after Claude pushes a commit?

First check the workflow’s event triggers and token choice. A workflow must listen for the relevant repository event, such as push or pull_request, for the change in question. Anthropic notes that GitHub does not trigger workflows on commits created with the default GITHUB_TOKEN. If a Claude-authored commit does not start downstream CI, inspect how the action authenticates and whether the workflow passes a custom github_token; the action uses GitHub App authentication by default when that input is omitted.

Also review permissions needed for the work. The action checks for write access by the actor on issue and pull-request events, and rejects bot actors unless explicitly allowed. A workflow can therefore be configured correctly yet not proceed for an actor or event that fails those checks. Keep token scope and workflow permissions no broader than the repository task requires.

Why is the @claude workflow not responding?

Work through these checks in order:

  1. Confirm installation: verify the Claude GitHub App is installed for the repository or organization where the issue or pull request lives.
  2. Confirm Actions are enabled: repository or organization policy must permit GitHub Actions to run.
  3. Check the trigger text: use the exact @claude mention expected by the configured workflow, in an event the workflow listens for.
  4. Check the credential: confirm the configured secret exists, is available to that workflow context, and matches the selected authentication method.
  5. Check actor access: for issue and pull-request events, verify the actor has write access; account for the documented bot-actor restriction unless explicitly configured otherwise.
  6. Inspect the workflow run: if a run was created, examine its logs and permissions to distinguish a trigger failure from an authentication or job failure.

Keep telemetry and CI status independently verifiable

Use the monitoring backend to validate telemetry delivery, and GitHub’s workflow run and check result to validate automation. A healthy signal in one system does not prove the other is configured. This separation makes failures easier to diagnose: missing session metrics point toward telemetry configuration or export, while an absent or failed check points toward workflow triggers, permissions, credentials, or job execution.

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