Agent skill

Dt Obs Tracing

by Dynatrace in Dynatrace/dynatrace-for-ai

Distributed traces, spans, service dependencies, and request flow analysis.

Apache-2.0Auto-check passedBackend & APIs

Install Dt Obs Tracing

skills CLI
$ npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-tracing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Dynatrace/dynatrace-for-ai dt-obs-tracing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dt-obs-tracing .claude/skills/dt-obs-tracing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dt-obs-tracing
GitHub stars
163
Token cost
~4.5k tokens
SKILL.md length
1,197 words
Files
13 (incl. references)
Skills in repo
33
Repo updated
First seen
Licence
Apache-2.0

At a glance

Distributed traces, spans, service dependencies, and request flow analysis.

  • Works in 3 steps: Investigate Slow Requests → Analyze Request Failures → Map Service Dependencies
  • Investigating span-level details
  • SKILL.md covers Overview, Use Cases, Core Concepts and Common Query Patterns, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Dt Obs Tracing is an agent skill from Dynatrace/dynatrace-for-ai. Distributed traces, spans, service dependencies, and request flow analysis. Use when investigating span-level details, failures, performance bottlenecks, or trace correlation. Trigger: "trace analysis", "slow requests", "failed spans", "service dependencies", "distributed trace", "span details", "HTTP status codes in traces", "database query spans", "messaging spans", "gRPC calls", "Lambda cold starts", "trace ID lookup", "exception analysis", "correlate logs and traces", "request attributes". Do NOT use for…

Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including reference files (for example `references/database-spans.md`, `references/entity-lookups.md` and `references/failure-detection.md`).

It sits in Backend & APIs, covering gRPC and Protobuf, Technical writing and Observability. It works with gRPC. The repository describes itself as: Skills, prompts, and instructions for building AI agents on top of Dynatrace production context. The licence is Apache-2.0.

When your agent uses it

  • Investigating span-level details
  • Performance bottlenecks
  • Trace correlation
  • Explaining existing queries

Example prompts

  • “trace analysis”
  • “slow requests”
  • “failed spans”
  • “/dt-obs-tracing”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Investigate Slow Requests
  2. Analyze Request Failures
  3. Map Service Dependencies

What it can do on your machine

Read from SKILL.md and the folder at commit 4f9aa71. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are dql).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dt Obs Tracing loads about 4.5k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 183 tokens; SKILL.md has 1,197 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~183
When it runs · the whole SKILL.md, loaded when a task matches
~4.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~17k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Dynatrace/dynatrace-for-ai at commit 4f9aa71, republished under its Apache-2.0 licence (© Dynatrace). 1,197 words, ~4,479 tokens.

Download SKILL.mdSave it as .claude/skills/dt-obs-tracing/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
dt-obs-tracing
description
Distributed traces, spans, service dependencies, and request flow analysis. Use when investigating span-level details, failures, performance bottlenecks, or trace correlation. Trigger: "trace analysis", "slow requests", "failed spans", "service dependencies", "distributed trace", "span details", "HTTP status codes in traces", "database query spans", "messaging spans", "gRPC calls", "Lambda cold starts", "trace ID lookup", "exception analysis", "correlate logs and traces", "request attributes". Do NOT use for explaining existing queries, product documentation or configuration questions, service-level RED metrics (use dt-obs-services), log searching (use dt-obs-logs), or problem analysis (use dt-obs-problems).
license
Apache-2.0

Application Tracing Skill

Overview

Distributed traces in Dynatrace consist of spans - building blocks representing units of work. With Traces in Grail, every span is accessible via DQL with full-text searchability on all attributes. This skill covers trace fundamentals, common analysis patterns, and span-type specific queries.


Use Cases

1. Investigate Slow Requests
  • Goal: Find and diagnose requests exceeding a latency threshold
  • Trigger: "slow requests", "high latency", "p99 response time", "find traces over 5 seconds"
  • Done: List of slow traces with duration, endpoint, service, and trace IDs for drilldown
2. Analyze Request Failures
  • Goal: Identify failed requests, failure reasons, and exception patterns
  • Trigger: "failed spans", "HTTP 500 errors", "exception analysis", "failure rate by service"
  • Done: Failure breakdown by reason (HTTP code, exception, gRPC status) with exemplar traces
3. Map Service Dependencies
  • Goal: Understand service-to-service communication patterns and external API calls
  • Trigger: "service dependencies", "what services does X call", "outgoing HTTP calls"
  • Done: Dependency map showing call counts, latency, and error rates between services

Core Concepts

Understanding Traces and Spans

Spans represent logical units of work in distributed traces:

  • HTTP requests, RPC calls, database operations
  • Messaging system interactions
  • Internal function invocations
  • Custom instrumentation points

Span kinds:

  • span.kind: server - Incoming call to a service
  • span.kind: client - Outgoing call from a service
  • span.kind: consumer - Incoming message consumption call to a service
  • span.kind: producer - Outgoing message production call from a service
  • span.kind: internal - Internal operation within a service

Root spans: A request root span (request.is_root_span == true) represents an incoming call to a service. Use this to analyze end-to-end request performance.

Key Trace Attributes

Essential attributes for trace analysis:

AttributeDescription
trace.idUnique trace identifier
span.idUnique span identifier
span.parent_idParent span ID (null for root spans)
request.is_root_spanBoolean, true for request entry points
request.is_failedBoolean, true if request failed
durationSpan duration in nanoseconds
span.timing.cpuOverall CPU time of the span (stable)
span.timing.cpu_selfCPU time excluding child spans (stable)
dt.smartscape.serviceService Smartscape node ID
dt.service.nameDynatrace service name derived from service detection rules. It is equal to the Smartscape service node name.
endpoint.nameEndpoint/route name
Service Context

Spans reference services via Smartscape node IDs and the detected service name dt.service.name which is also present on every span.

dql
fetch spans
| summarize spans=count(), by: { dt.smartscape.service, dt.service.name }

Node functions:

  • getNodeName(dt.smartscape.service) - Adds dt.smartscape.service.name field with the human-readable service name
  • getNodeField(dt.smartscape.service, "attribute_name") - Access specific node attributes

📖 Learn more: See Entity Lookups for advanced entity selectors, infrastructure correlation, and hardware analysis.

Sampling and Extrapolation

One span can represent multiple real operations due to:

  • Aggregation: Multiple operations in one span (aggregation.count)
  • ATM (Adaptive Traffic Management): Head-based sampling by agent
  • ALR (Adaptive Load Reduction): Server-side sampling
  • Read Sampling: Query-time sampling via samplingRatio parameter

When to extrapolate: Always extrapolate when counting actual operations (not just spans). Use the multiplicity factor:

dql
fetch spans
| fieldsAdd sampling.probability = (power(2, 56) - coalesce(sampling.threshold, 0)) * power(2, -56)
| fieldsAdd sampling.multiplicity = 1 / sampling.probability
| fieldsAdd multiplicity = coalesce(sampling.multiplicity, 1)
                         * coalesce(aggregation.count, 1)
                         * dt.system.sampling_ratio
| summarize operation_count = sum(multiplicity)

📖 Learn more: See Sampling and Extrapolation for detailed formulas and examples.

Common Query Patterns

Basic Span Access

Fetch spans and explore by type:

dql
fetch spans | limit 1

Explore spans by function and type:

dql
fetch spans
| summarize count(), by: { span.kind, code.namespace, code.function }
Request Root Filtering

List request root spans (incoming service calls):

dql
fetch spans
| filter request.is_root_span == true
| fields trace.id, span.id, start_time, response_time = duration, endpoint.name
| limit 100
Service Performance Summary

Analyze service performance with error rates:

dql
fetch spans
| filter request.is_root_span == true
| summarize
    total_requests = count(),
    failed_requests = countIf(request.is_failed == true),
    avg_duration = avg(duration),
    p95_duration = percentile(duration, 95),
    by: {dt.service.name}
| fieldsAdd error_rate = (failed_requests * 100.0) / total_requests
| sort error_rate desc
Trace ID Lookup

Find all spans in a specific trace:

dql
fetch spans
| filter trace.id == toUid("abc123def456")
| fields span.name, duration, dt.service.name

Performance Analysis

Response Time Percentiles

Calculate percentiles by endpoint:

dql
fetch spans
| filter request.is_root_span == true
| summarize {
    requests=count(),
    avg_duration=avg(duration),
    p95=percentile(duration, 95),
    p99=percentile(duration, 99)
  }, by: { endpoint.name }
| sort p99 desc

💡 Best practice: Use percentiles (p95, p99) over averages for performance insights.

Slow Trace Detection

Find requests exceeding a threshold:

dql
fetch spans, from:now() - 2h
| filter request.is_root_span == true
| filter duration > 5s
| fields trace.id, span.name, dt.service.name, duration
| sort duration desc
| limit 50
Duration Buckets with Exemplars
dql
fetch spans, from:now() - 24h
| filter http.route == "/api/v1/storage/findByISBN"
| summarize {
    spans=count(),
    trace=takeAny(record(start_time, trace.id))
  }, by: { bin(duration, 10ms) }
| fields `bin(duration, 10ms)`, spans, trace.id=trace[trace.id], start_time=trace[start_time]
Performance Timeseries

Extract response time as timeseries:

dql
fetch spans, from:now() - 24h
| filter request.is_root_span == true
| makeTimeseries {
    requests=count(),
    avg_duration=avg(duration),
    p95=percentile(duration, 95),
    p99=percentile(duration, 99)
  }, by: { endpoint.name }

📖 Learn more: See Performance Analysis for advanced patterns and timeseries techniques.

Failure Investigation

Failed Request Summary

Summarize failures by service:

dql
fetch spans
| filter request.is_root_span == true
| summarize
    total = count(),
    failed = countIf(request.is_failed == true),
  by: { dt.service.name }
| fieldsAdd failure_rate = (failed * 100.0) / total
| sort failure_rate desc
Failure Reason Analysis

Breakdown by failure detection reason:

dql
fetch spans
| filter request.is_failed == true and isNotNull(dt.failure_detection.results)
| expand dt.failure_detection.results
| summarize count(), by: { dt.failure_detection.results[reason] }

Failure reasons:

  • http_code - HTTP response code triggered failure
  • grpc_code - gRPC status code triggered failure
  • exception - Exception caused failure
  • span_status - Span status indicated failure
  • custom_rule - Custom failure detection rule matched
HTTP Code Failures

Find failures by HTTP status code:

dql
fetch spans
| filter request.is_failed == true
| filter iAny(dt.failure_detection.results[][reason] == "http_code")
| summarize count(), by: { http.response.status_code, endpoint.name }
| sort `count()` desc
Recent Failed Requests

List recent failures with details:

dql
fetch spans
| filter request.is_root_span == true and request.is_failed == true
| fields
    start_time,
    trace.id,
    endpoint.name,
    http.response.status_code,
    duration
| sort start_time desc
| limit 100

📖 Learn more: See Failure Detection for exception analysis and custom rule investigation.

Service Dependencies

Service-to-Service Analysis

Analyze service communication patterns:

dql
fetch spans, from:now() - 1h
| filter isNotNull(server.address)
| fieldsAdd
    remote_side = server.address
| summarize
    call_count = count(),
    avg_duration = avg(duration),
    by: {dt.service.name, remote_side}
| sort call_count desc
Outgoing HTTP Calls

Identify external API dependencies:

dql
fetch spans
| filter span.kind == "client" and isNotNull(http.request.method)
| summarize
    calls = count(),
    avg_latency = avg(duration),
    p99_latency = percentile(duration, 99),
  by: { dt.service.name, server.address, server.port }
| sort calls desc

Trace Aggregation

Complete Trace Analysis

Aggregate all spans in a trace to understand full request flow:

dql
fetch spans, from:now() - 30m
| summarize {
    spans = count(),
    client_spans = countIf(span.kind == "client"),

    // Endpoints involved in the trace
    endpoints = toString(arrayRemoveNulls(collectDistinct(endpoint.name))),

    // Extract the first request root in the trace
    trace_root = takeMin(record(
        root_detection_helper = coalesce(
            if(request.is_root_span, 1),
            if(isNull(span.parent_id), 2),
            3),
        start_time, endpoint.name, duration
      ))
}, by: { trace.id }

| fieldsFlatten trace_root
| fieldsRemove trace_root.root_detection_helper, trace_root

| fields
    start_time = trace_root.start_time,
    endpoint = trace_root.endpoint.name,
    response_time = trace_root.duration,
    spans,
    client_spans,
    endpoints,
    trace.id
| sort start_time
| limit 100

Root detection strategy: Use takeMin(record(...)) with a detection helper to reliably find the root request:

  1. Priority 1: Spans with request.is_root_span == true
  2. Priority 2: Spans without parent (root spans)
  3. Priority 3: All other spans
Multi-Service Traces

Find traces spanning multiple services:

dql
fetch spans, from:now() - 1h
| summarize {
    services = collectDistinct(dt.service.name),
    trace_root = takeMin(record(root_detection_helper = coalesce(if(request.is_root_span, 1), 2), endpoint.name))
}, by: { trace.id }
| fieldsAdd service_count = arraySize(services)
| filter service_count > 1
| fields endpoint = trace_root[endpoint.name], service_count, services = toString(services), trace.id
| sort service_count desc
| limit 50

Request-Level Analysis

Show full SKILL.md (490 more words)Show less
Request Attributes

Access custom request attributes captured by OneAgent on request root spans:

dql
fetch spans
| filter request.is_root_span == true
| filter isNotNull(request_attribute.PaidAmount)
| makeTimeseries sum(request_attribute.PaidAmount)

Field patterns: request_attribute.<name>, captured_attribute.<name> (always arrays)

→ Request Attributes — full patterns for request attributes, captured attributes, and request ID aggregation

Span Types

Span TypeDetectionKey FieldsReference
HTTP server (incoming)span.kind == "server" and isNotNull(http.request.method)http.route, http.request.method, http.response.status_codehttp-spans.md
HTTP client (outgoing)span.kind == "client" and isNotNull(http.request.method)server.address, server.porthttp-spans.md
Databasespan.kind == "client" and isNotNull(db.system)db.system, db.namespace, db.statementdatabase-spans.md
MessagingisNotNull(messaging.system)messaging.system, messaging.destination.name, messaging.operation.typemessaging-spans.md
RPC / gRPCisNotNull(rpc.system)rpc.system, rpc.service, rpc.method, rpc.grpc.status_coderpc-spans.md
Serverless / FaaSisNotNull(faas.name) and span.kind == "server"faas.name, faas.trigger.type, cloud.providerserverless-spans.md

⚠️ Database spans: Can be aggregated (one span = multiple calls). Always use aggregation.count extrapolation for accurate operation counts.

📖 Detailed patterns per span type: See the reference files above.

Advanced Topics

Exception Analysis

Exceptions are stored as span.events within spans:

dql
fetch spans
| filter iAny(span.events[][span_event.name] == "exception")
| expand span.events
| fieldsFlatten span.events, fields: { exception.type }
| summarize {
    count(),
    trace=takeAny(record(start_time, trace.id))
  }, by: { exception.type }
| fields exception.type, `count()`, trace.id=trace[trace.id], start_time=trace[start_time]

💡 Tip: Use iAny() to check conditions within span event arrays.

→ Logs Correlation — joining logs and traces, filtering traces by log content → Network Analysis — client IPs, DNS resolution, subnet analysis

Best Practices

AreaRule
FilteringApply request.is_root_span == true and endpoint filters first
SamplingUse samplingRatio (e.g., 100 = read 1%) for performance
PercentilesUse p95/p99 over averages for performance analysis
Root spansUse request.is_root_span == true for end-to-end analysis
Trace groupingGroup by trace.id for complete trace metrics
Request groupingGroup by request.id for OneAgent-only request metrics
ExtrapolationAlways apply multiplicity for accurate operation counts
ExemplarsUse takeAny(record(start_time, trace.id)) to enable UI drilldown

Troubleshooting

ProblemCauseSolution
Duration values seem wrong (too large)duration is in nanoseconds, not millisecondsDivide by 1000000 or compare with 5s (DQL duration literal)
Span counts don't match expected request volumeSampling or aggregation not accounted forUse multiplicity extrapolation — see Sampling and Extrapolation reference
getNodeName(dt.smartscape.service) returns nullService not yet resolved or OneAgent not monitoringVerify OneAgent monitors the service; entity resolution may have a short delay
request.is_root_span filter returns nothingQuerying OpenTelemetry-only traces without OneAgentUse isNull(span.parent_id) as fallback for root span detection
trace.id filter returns no resultsTrace ID not converted to UID formatUse filter trace.id == toUid("abc123...") for string-based trace IDs
Database span counts are too lowDatabase spans are aggregated (one span = N calls)Always use aggregation.count extrapolation for database operation counts
  • dt-dql-essentials — Core DQL syntax for querying trace data
  • dt-app-dashboards — Embed trace queries in dashboards
  • dt-migration — Smartscape entity model and relationship navigation

References

Detailed documentation for specific topics:

© Dynatrace, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files (references) in skills/dt-obs-tracing of Dynatrace/dynatrace-for-ai.

  • SKILL.md
  • references/database-spans.md
  • references/entity-lookups.md
  • references/failure-detection.md
  • references/http-spans.md
  • references/logs-correlation.md
  • references/messaging-spans.md
  • references/networking-analysis.md
  • references/performance-analysis.md
  • references/request-attributes.md
  • references/rpc-spans.md
  • references/sampling-extrapolation.md
  • references/serverless-spans.md

Open the folder on GitHubat commit 4f9aa71

Compare with similar skills

Dt Obs Tracing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dt Obs Tracing compared with similar skills
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Backend Dev Guidelineslangfuse/langfuse36k—~1.9kAutomated safety check: PassCustom licence
Golang Proantoniopaya22/go-rest-template1723 repos~1.2kAutomated safety check: PassMIT
Debug Grpc ConnectionGetBindu/Bindu10k—~1.2kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Dt Obs Tracing

What does Dt Obs Tracing do?

Distributed traces, spans, service dependencies, and request flow analysis. Dt Obs Tracing is an agent skill from Dynatrace/dynatrace-for-ai. Distributed traces, spans, service dependencies, and request flow analysis.

When should I use Dt Obs Tracing?

Dt Obs Tracing fits situations like: investigating span-level details; performance bottlenecks; trace correlation; explaining existing queries.

How do I install Dt Obs Tracing in Claude Code?

Run `npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-tracing -a claude-code`. Or copy the skill folder (skills/dt-obs-tracing in Dynatrace/dynatrace-for-ai) into .claude/skills/dt-obs-tracing in your project. Claude Code loads it when a task matches its description.

How do I install Dt Obs Tracing in Codex?

Run `npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-tracing -a codex`. Or copy the skill folder (skills/dt-obs-tracing in Dynatrace/dynatrace-for-ai) into .agents/skills/dt-obs-tracing in your project. Codex loads it when a task matches its description.

Can I use Dt Obs Tracing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Dynatrace/dynatrace-for-ai --skill dt-obs-tracing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dt-obs-tracing, .gemini/skills/dt-obs-tracing, .github/skills/dt-obs-tracing and .opencode/skills/dt-obs-tracing in your project.

What does Dt Obs Tracing need to run?

SKILL.md names no scripts, command-line tools or credentials: Dt Obs Tracing is instructions for the agent only.

Does Dt Obs Tracing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Dt Obs Tracing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dt Obs Tracing use?

Dt Obs Tracing is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dt Obs Tracing use?

About 4.5k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.

What are the alternatives to Dt Obs Tracing?

Skills that share tags, products or a category with Dt Obs Tracing: Kratos Development (aide-family/moon, 253 stars), Use Yaak (mountain-loop/yaak, 19k stars), Backend Dev Guidelines (langfuse/langfuse, 36k stars) and Golang Pro (antoniopaya22/go-rest-template, 172 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dt Obs Tracing?

Dynatrace (a GitHub organization) maintains it in Dynatrace/dynatrace-for-ai, which has 163 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 1, 2026.

Source: Dynatrace/dynatrace-for-ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.