Skip to content
Development
Skill

/archestra-dev-observability

Use when changing Archestra tracing, metrics, OpenTelemetry, Tempo, Grafana, Prometheus, LLM/MCP spans, observability labels, or local observability setup.

From plugin
archestra
4.1k17 skills2 commands
Install
$ npx -y skills add archestra-ai/archestra --skill archestra-dev-observability --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/archestra-dev-observability

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when changing Archestra tracing, metrics, OpenTelemetry, Tempo, Grafana, Prometheus, LLM/MCP spans, observability labels, or local observability setup.

SKILL.md

archestra-dev-observability.SKILL.md
name: archestra-dev-observability
description: Use when changing Archestra tracing, metrics, OpenTelemetry, Tempo, Grafana, Prometheus, LLM/MCP spans, observability labels, or local observability setup.

Archestra Observability

Use this skill before changing tracing, metrics, span naming, metric labels, or local observability setup.

Run commands from `platform/` unless specifically instructed otherwise.

Naming new attributes and metrics

Before introducing any new span attribute or metric name, look it up — do not coin a name from intuition.

  • **Span attributes**: search the OTEL semantic-convention registry and use the existing attribute verbatim if one fits. Registry: https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/ (wider set: https://opentelemetry.io/docs/specs/semconv/registry/attributes/). Example: prompt-cache tokens are `gen_ai.usage.cache_read.input_tokens` and `gen_ai.usage.cache_creation.input_tokens`, not a custom `archestra.usage.*`. Only use an `archestra.*` name when nothing in the registry fits, and say why in a comment.
  • **"Not yet stable" is not a reason to avoid a standard name.** The whole `gen_ai.*` namespace is Development-stability, including the `gen_ai.usage.*` attributes already emitted here — match that bar, don't custom-namespace to dodge it.
  • **Metrics**: match the existing `llm_*` / prom-client family and label names in `metrics/`; don't introduce a new metric style. Add a label value to an existing metric only if it won't change what current aggregates mean — otherwise add a dedicated metric (cache tokens use a separate `llm_cache_tokens_total`, not new `type` values on `llm_tokens_total`).

Local setup

tilt trigger observability
docker compose -f dev/docker-compose.observability.yml up -d

Both commands are equivalent — the tilt resource wraps the same compose file — and start the full observability stack with pre-configured datasources: Tempo, Loki, OTEL Collector, Prometheus, and Grafana.

Local URLs

  • Tempo API: `http://localhost:3200/`.
  • Grafana: `http://localhost:3002/`.
  • Prometheus: `http://localhost:9090/`.
  • Backend metrics: `http://localhost:9050/metrics`.

Tracing

  • Follow OTEL GenAI Semantic Conventions (see "Naming new attributes and metrics" — check the registry before adding any attribute): https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-agent-spans/.
  • LLM spans use `gen_ai.agent.id`, `gen_ai.agent.name`, `gen_ai.provider.name`, `gen_ai.request.model`, `gen_ai.operation.name`, and `archestra.agent.label.<key>` for dynamic agent labels.
  • MCP spans use `gen_ai.tool.name` and `mcp.server.name`.
  • Team metadata uses the custom `archestra.<scope>.team.*` namespace (no OTEL registry equivalent), where scope is the principal the teams belong to — `agent` (the executing agent's teams) or `user` (the requesting user's teams). `archestra.<scope>.team.ids` / `.names` are array-valued (a principal can belong to multiple teams), and `archestra.<scope>.team.label.<key>` carries team labels merged per key across the principal's teams. Set via `setTeamAttributes(span, teams, scope)` in `observability/tracing/attributes.ts`; agent teams come from `AgentTeamModel.getTeamLabelInfoForAgent` and user teams from `TeamModel.getTeamLabelInfoForUser`, resolved once per request.
  • Session tracking uses `gen_ai.conversation.id` from the `X-Archestra-Session-Id` header.
  • Span names are `chat {model}`, `generate_content {model}`, and `execute_tool {tool_name}`.
  • Agent label keys are fetched from the database on startup and used as dynamic Prometheus metric label dimensions (see Metrics); the tracing SDK's resource carries only `service.name`/`service.version`. On spans, agent labels are set per-request via `setAgentAttributes`.
  • Traces are stored in Grafana Tempo.
  • User identity is tracked with `archestra.user.id`, `archestra.user.email`, and `archestra.user.name` when available.
  • LLM spans include `archestra.cost` in USD and `gen_ai.usage.total_tokens`.

Metrics

  • Prometheus metrics `llm_request_duration_seconds` and `llm_tokens_total` include `provider`, `model`, `agent_id`, `agent_name`, `agent_type`, `source`, and dynamic agent labels as dimensions — deliberately NOT `external_agent_id`, which is client-supplied and unbounded and would explode series cardinality. Do not add it back.
  • `agent_id` is internal.
  • `external_agent_id` comes from the client-provided `X-Archestra-Agent-Id` header and is a label only on `agent_executions_total`.
  • MCP metrics include `agent_id`, `agent_name`, and `agent_type`.
  • Metrics are reinitialized on startup with current label keys from the database.
Read more
Ships witharchestra

Enterprise AI Platform with guardrails, MCP registry, gateway & orchestrator

Get the whole plugin