Handles OTel span and metric instrumentation for the Copilot Chat extension.

Install

mkdir -p .claude/skills/otel && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/17826" && unzip -o skill.zip -d .claude/skills/otel && rm skill.zip

Installs to .claude/skills/otel

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

OpenTelemetry instrumentation for the Copilot Chat extension — covers the four agent execution paths, the IOTelService abstraction, span/metric/event conventions, and the relationship between code and the user/developer monitoring docs. Use when adding/changing OTel spans, metrics, or events; instrumenting a new agent surface; touching the Copilot CLI bridge or Claude span emission; or updating `extensions/copilot/docs/monitoring/agent_monitoring*.md`.
456 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • Add OpenTelemetry spans, metrics, or events.
  • Change existing OpenTelemetry instrumentation.
  • Review OpenTelemetry telemetry in the Copilot Chat extension.
  • Instrument new agent surfaces.
  • Update monitoring documentation to sync with code changes.

How it works

This skill guides OpenTelemetry instrumentation in the Copilot Chat extension by referencing authoritative documents and canonical code locations. It ensures that code changes are synchronized with monitoring documentation and outlines specific strategies for different agent execution paths.

Inputs & outputs

You give it
A request to instrument code, modify telemetry, or update monitoring documentation.
You get back
OpenTelemetry instrumentation integrated into the Copilot Chat extension, with updated documentation.

When to use otel

  • Adding new OTel spans
  • Updating telemetry metrics
  • Syncing monitoring documentation

About this skill

OpenTelemetry Instrumentation Skill

When adding, changing, or reviewing OTel telemetry in the Copilot Chat extension, always read the two source-of-truth docs first and always keep them in sync with the code you change.

1. Authoritative Documents

The extensions/copilot/docs/monitoring/ directory contains the two specs that define the OTel contract for the extension. Treat them like the layout / layer specs in vs/sessions.

DocumentPathAudienceCovers
User-facingextensions/copilot/docs/monitoring/agent_monitoring.mdExtension usersQuick start, settings, env vars, exported spans/metrics/events, backend setup guides
Architectureextensions/copilot/docs/monitoring/agent_monitoring_arch.mdDevelopersMulti-agent strategies, span hierarchies, file structure, instrumentation points, IOTelService, configuration channels
Visual flowextensions/copilot/docs/monitoring/otel-data-flow.htmlDevelopersRenders the bridge data flow for the in-process Copilot CLI agent

If the implementation changes, you must update the relevant doc in the same PR. The arch doc is the most likely to drift; treat divergence as a bug.

2. Architecture at a Glance

The extension has four agent execution paths, each with a different OTel strategy:

AgentProcess ModelStrategyDebug Panel Source
Foreground (toolCallingLoop)Extension hostDirect IOTelService spansExtension spans
Copilot CLI in-processExtension host (same process)Bridge SpanProcessor — SDK creates spans natively; bridge forwards to debug panelSDK native spans via bridge
Copilot CLI terminalSeparate terminal processForward OTel env varsN/A (separate process)
Claude CodeChild process (Node fork)Synthesized from SDK messages — extension intercepts the Claude SDK message stream in claudeMessageDispatch.ts and emits GenAI spans; LLM calls are proxied through claudeLanguageModelServer.ts (which calls chatMLFetcher, producing standard chat spans).Extension spans

Why asymmetric? The CLI SDK runs in-process with full trace hierarchy (subagents, permissions, hooks). A bridge captures this directly. Claude runs as a separate process — internal spans are inaccessible, so the extension synthesizes spans by translating SDK messages and proxying the model API.

3. Where Things Live (canonical map)

extensions/copilot/src/platform/otel/
├── common/
│   ├── otelService.ts          # IOTelService interface + ISpanHandle + injectCompletedSpan
│   ├── otelConfig.ts           # Config resolution (env → settings → defaults), enabledVia, dbSpanExporter
│   ├── noopOtelService.ts      # Zero-cost no-op (used by chatLib / tests)
│   ├── inMemoryOTelService.ts  # ← actually under node/, see below
│   ├── agentOTelEnv.ts         # deriveCopilotCliOTelEnv / deriveClaudeOTelEnv
│   ├── genAiAttributes.ts      # ⚠ Single source of truth for attribute keys & enums
│   ├── genAiEvents.ts          # Event emitter helpers (emit*Event)
│   ├── genAiMetrics.ts         # GenAiMetrics class
│   ├── messageFormatters.ts    # truncateForOTel, normalizeProviderMessages, toSystemInstructions, …
│   ├── workspaceOTelMetadata.ts
│   ├── sessionUtils.ts
│   └── index.ts                # ⚠ Public barrel — re-export new helpers/constants here
└── node/
    ├── otelServiceImpl.ts      # NodeOTelService + DiagnosticSpanExporter + FilteredSpanExporter + EXPORTABLE_OPERATION_NAMES
    ├── inMemoryOTelService.ts  # InMemoryOTelService (used when OTel is disabled — feeds debug panel only)
    ├── fileExporters.ts        # File-based span/log/metric exporters
    └── sqlite/                 # OTelSqliteStore + SqliteSpanExporter (dbSpanExporter pipeline)

extensions/copilot/src/extension/
├── chatSessions/
│   ├── copilotcli/node/
│   │   ├── copilotCliBridgeSpanProcessor.ts  # Bridge: SDK spans → IOTelService (+ hook span enrichment)
│   │   ├── copilotcliSession.ts              # Root invoke_agent copilotcli span + traceparent + hook stash
│   │   └── copilotcliSessionService.ts       # Bridge installation + env var setup
│   └── claude/
│       ├── common/claudeMessageDispatch.ts   # execute_tool / execute_hook spans + subagent context wiring
│       └── node/
│           ├── claudeOTelTracker.ts          # invoke_agent claude span + per-session token/cost rollup
│           └── claudeLanguageModelServer.ts  # Local HTTP proxy → chatMLFetcher (chat spans)
├── chat/vscode-node/
│   └── chatHookService.ts                    # execute_hook spans for foreground agent hooks
├── intents/node/toolCallingLoop.ts           # invoke_agent spans for foreground agent
├── tools/vscode-node/toolsService.ts         # execute_tool spans for foreground tools
├── prompt/node/chatMLFetcher.ts              # chat spans for all LLM calls
├── byok/vscode-node/                         # BYOK provider chat spans (anthropicProvider, geminiNativeProvider, …)
└── trajectory/vscode-node/
    ├── otelChatDebugLogProvider.ts           # Debug panel data provider
    ├── otelSpanToChatDebugEvent.ts           # Span → ChatDebugEvent conversion
    └── otlpFormatConversion.ts               # OTLP ↔ in-memory span format

4. Service Layer & Selection

IOTelService (otelService.ts) is the only abstraction consumers should depend on — never import the OTel SDK directly outside node/otelServiceImpl.ts. Three implementations:

ClassWhen Used
NoopOTelServicechatLib and tests where no telemetry pipeline is needed — zero cost
NodeOTelServiceOTel enabled — full SDK, OTLP/file/console export, optional SQLite span exporter
InMemoryOTelServiceRegistered when OTel is disabled — no SDK is loaded, but spans/metrics/logs are still captured in-memory so the Agent Debug Log panel keeps working

Selection happens in src/extension/extension/vscode-node/services.ts: exactly one of NodeOTelService or InMemoryOTelService is bound to IOTelService per extension host based on resolveOTelConfig().enabled.

5. Span / Metric / Event Conventions

Follow the OTel GenAI semantic conventions. Always use the constants from genAiAttributes.ts — never raw string literals.

OperationSpan NameKindConstant
Agent orchestrationinvoke_agent {agent_name}INTERNALGenAiOperationName.INVOKE_AGENT
LLM API callchat {model}CLIENTGenAiOperationName.CHAT
Tool executionexecute_tool {tool_name}INTERNALGenAiOperationName.EXECUTE_TOOL
Hook executionexecute_hook {hook_type}INTERNALGenAiOperationName.EXECUTE_HOOK

Attribute namespaces:

NamespaceConstant moduleExamples
gen_ai.*GenAiAttrgen_ai.operation.name, gen_ai.usage.input_tokens
copilot_chat.*CopilotChatAttrcopilot_chat.session_id, copilot_chat.chat_session_id, copilot_chat.hook_*
github.copilot.*CopilotCliSdkAttrSDK-emitted hook attributes (read-only — bridge & debug panel)
claude_code.*(raw)Claude subprocess SDK attributes — only ever observed in OTLP, not produced by the extension

Standard span pattern

return this._otelService.startActiveSpan(
    `execute_tool ${name}`,
    {
        kind: SpanKind.INTERNAL,
        attributes: {
            [GenAiAttr.OPERATION_NAME]: GenAiOperationName.EXECUTE_TOOL,
            [GenAiAttr.TOOL_NAME]: name,
            // …
        },
    },
    async (span) => {
        try {
            const result = await this._actualWork();
            span.setStatus(SpanStatusCode.OK);
            return result;
        } catch (err) {
            span.setStatus(SpanStatusCode.ERROR, err instanceof Error ? err.message : String(err));
            span.setAttribute(StdAttr.ERROR_TYPE, err instanceof Error ? err.constructor.name : 'Error');
            throw err;
        }
    },
);

Cross-boundary trace propagation

// Parent: store context keyed by something the child knows
const ctx = this._otelService.getActiveTraceContext();
if (ctx) { this._otelService.storeTraceContext(`subagent:invocation:${id}`, ctx); }

// Child: retrieve and use as parent
const parentCtx = this._otelService.getStoredTraceContext(`subagent:invocation:${id}`);
return this._otelService.startActiveSpan('invoke_agent child', { parentTraceContext: parentCtx, … }, fn);

Content capture

The extension uses two conventions side-by-side; pick the right one for the attribute you're adding.

  1. Always emit (truncated) — used for inputs/outputs that the Agent Debug Log panel needs to be useful even when OTel export is off (e.g. gen_ai.tool.call.arguments in toolsService.ts, and copilot_chat.hook_input / hook_output in chatHookService.ts). The attribute is captured unconditionally but always passed through truncateForOTel. Use this for moderate-sized, generally-non-secret arguments / results.
  2. Gate on config.captureContent — used for full prompt / response / system-instruction bodies (e.g. gen_ai.input.messages, gen_ai.output.messages, gen_ai.system_instructions, gen_ai.tool.definitions in chatMLFetcher.ts and the BYOK providers). These are larger and more likely to contain user secrets.
// Pattern 1 — always emit, always truncate
span.setAttribute(GenAiAttr.TOOL_CALL_ARGUMENTS, tru

---

*Content truncated.*

When not to use it

  • When the task does not involve OpenTelemetry instrumentation.
  • When working outside the Copilot Chat extension context.

Limitations

  • Requires updating relevant documentation in the same PR as code changes.
  • Hard-coded attribute keys are not allowed.
  • Hard-coded provider strings are not allowed.

How it compares

This skill enforces a strict synchronization between OpenTelemetry code and dedicated monitoring documentation, ensuring consistency and clarity for the Copilot Chat extension's specific instrumentation strategies, unlike general telemetry

Compared to similar skills

otel side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
otel (this skill)02moReviewAdvanced
distributed-tracing52moNo flagsIntermediate
service-mesh-observability52moNo flagsAdvanced
observability-engineer123moNo flagsAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

distributed-tracing

wshobson

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

577

service-mesh-observability

wshobson

Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

574

observability-engineer

sickn33

Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.

1242

prometheus-configuration

wshobson

Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.

645

langfuse

davila7

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

743

slo-implementation

wshobson

Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

338

Search skills

Search the agent skills registry