distributed-tracing

Overview of distributed-tracing skill

The distributed-tracing skill helps an agent design and explain end-to-end request tracing for microservices, with concrete setup patterns around Jaeger and Tempo. It is best for teams working on observability, latency debugging, request-path analysis, and service dependency mapping across distributed systems.

What this distributed-tracing skill is for

Use this distributed-tracing skill when you need more than “add tracing somehow.” It is aimed at practical jobs such as:

• instrumenting services so a request can be followed across hops
• deploying a tracing backend in Kubernetes
• reasoning about traces, spans, context propagation, and filtering
• finding latency hotspots or failure propagation in multi-service flows

Who should install it

This distributed-tracing skill is a strong fit for:

• platform and SRE teams adding tracing to a cluster
• backend engineers debugging microservice latency
• observability owners comparing or implementing Jaeger and Tempo
• agents that need structured guidance instead of a generic observability prompt

If you only need a basic definition of traces and spans, this may be more than you need.

What makes it different from a generic prompt

A normal prompt can explain distributed tracing conceptually. This skill is more useful when you need the model to stay grounded in an implementation workflow: trace structure, core concepts, and deployment-oriented examples for common observability stacks.

The main value is that it narrows the model onto distributed-tracing for Observability rather than broad logging, metrics, or APM advice.

What to know before adopting

This skill is focused and lightweight: the repository evidence shows essentially a single SKILL.md without helper scripts, rules, or reference files. That means adoption is easy, but you should expect guidance rather than automation. It helps the agent think and respond better; it does not ship installers, validators, or environment-specific checks.

How to Use distributed-tracing skill

How to install distributed-tracing

Install the distributed-tracing skill from the repository with:

npx skills add https://github.com/wshobson/agents --skill distributed-tracing

After install, open:

• plugins/observability-monitoring/skills/distributed-tracing/SKILL.md

Because this skill has no extra support files, SKILL.md is the main source of truth.

What input the skill needs

For strong output, give the agent concrete system context. The distributed-tracing skill works best when your prompt includes:

• your services and request path
• runtime or framework per service
• deployment target, especially Kubernetes vs local/dev
• tracing backend preference: Jaeger, Tempo, or undecided
• what problem you are solving: latency, dependency mapping, or error tracing
• any constraints: cost, retention, sampling, existing OpenTelemetry usage

Without that, the output will stay generic.

Turn a rough goal into a usable prompt

Weak prompt:

Help me add distributed tracing.

Better prompt:

Use the distributed-tracing skill. I run 6 Kubernetes microservices behind an API gateway. We already use Prometheus and Grafana, but no tracing yet. I need to trace a checkout request across gateway, auth, cart, payment, and Postgres access. Recommend whether to use Jaeger or Tempo, show the trace flow we should expect, explain context propagation, and give a rollout plan that starts in staging.

Why this is better:

• names the environment
• gives a real request path
• sets the observability baseline
• asks for a decision, not just a definition
• creates output you can review and implement

What the skill actually helps the agent produce

In practice, the distributed-tracing usage pattern is to ask for one of these outputs:

• a tracing architecture recommendation
• a Kubernetes deployment path for Jaeger
• a Tempo-oriented observability plan
• an explanation of trace/span structure for your request flow
• bottleneck analysis logic for existing trace data
• context propagation advice across services

This is most useful when you want the model to connect tracing concepts to a real system design.

Suggested workflow for first use

A reliable workflow is:

1. State the distributed system and request path.
2. Specify your observability stack today.
3. Ask the agent to map traces and spans for one critical request.
4. Ask for backend setup guidance, usually Jaeger or Tempo.
5. Review where context propagation can break.
6. Iterate on sampling, tags, and troubleshooting after the first draft.

This sequence produces better results than asking for full observability architecture in one step.

Repository reading path that saves time

Read SKILL.md in this order:

1. Purpose
2. When to Use
3. Distributed Tracing Concepts
4. Trace Structure
5. backend setup sections such as Jaeger deployment

This gives you the decision context first, then the implementation shape. Since the skill has no extra docs, there is little benefit in hunting for hidden support material.

How to ask for Jaeger vs Tempo guidance

If you already know your backend, say so directly. If not, ask the agent to compare them against your constraints.

Example:

Use the distributed-tracing skill to compare Jaeger and Tempo for a Kubernetes environment where we already use Grafana, need low operational overhead, and mainly want request debugging rather than long-term trace analytics.

That kind of prompt yields a decision-ready answer instead of two shallow tool summaries.

Practical prompt details that improve output quality

Include details the model cannot infer:

• ingress path and async hops
• whether services already propagate headers
• desired tags like tenant, region, or endpoint
• expected traffic volume for sampling decisions
• whether you need dev-only visibility or production tracing

For distributed-tracing usage, these inputs materially change recommendations around span boundaries, storage strategy, and rollout order.

Common adoption blockers

The main blockers are usually not installation but ambiguity:

• not knowing which request flow to trace first
• not knowing whether tracing is needed versus logs/metrics
• missing context propagation between services
• asking for “observability” too broadly and getting diluted advice

This distributed-tracing guide is most useful when you narrow the scope to one request journey and one backend decision.

distributed-tracing skill FAQ

Is this distributed-tracing skill beginner friendly?

Yes, if you already understand your service topology. The skill explains core concepts like traces, spans, tags, logs, and context propagation, but it is more implementation-oriented than tutorial-first. Beginners with a simple monolith may find it excessive.

When should I use this instead of a normal observability prompt?

Use the distributed-tracing skill when you specifically need request-flow visibility across services. A generic observability prompt often mixes logs, metrics, alerts, dashboards, and tracing into one broad answer. This skill keeps the model centered on tracing decisions and workflows.

Does the skill install tracing in my cluster automatically?

No. The distributed-tracing install adds guidance for the agent, not an operator or deployment script. The skill includes setup examples, but you still need to apply manifests, instrument services, and validate results in your own environment.

Is this only for Jaeger?

No. Jaeger is explicitly covered, and Tempo is part of the skill’s positioning. The skill is best viewed as a distributed-tracing for Observability guide that uses those backends as practical implementation targets.

When is this skill a poor fit?

Skip it or use it lightly if:

• you run a single process or simple monolith
• you only need application logs
• you need vendor-specific instrumentation instructions for one framework
• you expect automated environment discovery from the skill alone

In those cases, a narrower framework doc or vendor integration guide may be faster.

What does good distributed-tracing usage look like?

Good distributed-tracing usage starts with one real transaction, such as login or checkout, then defines expected spans, propagation boundaries, and backend setup. Teams that start with a concrete flow get better results than teams asking for “full tracing strategy” without system detail.

How to Improve distributed-tracing skill

Give the skill a request path, not a vague objective

The single highest-leverage improvement is input specificity. Instead of “help with latency,” say:

Use the distributed-tracing skill for this path: frontend → gateway → auth-service → order-service → payment-service → database. We see p95 latency spikes during checkout and want to know where to place spans and what tags to capture.

That lets the agent produce a useful trace model rather than generic observability advice.

Ask for output in implementation order

Better results come from staged requests:

1. map the trace
2. define span boundaries
3. choose backend
4. outline deployment
5. identify troubleshooting checks

If you ask for everything at once, the answer is usually broader and less actionable.

Surface your constraints early

The skill improves sharply when you include operational limits such as:

• existing Grafana stack
• storage budget
• retention needs
• traffic volume
• production sampling concerns
• Kubernetes-only deployment requirements

These constraints affect whether the model leans toward Jaeger, Tempo, or a lighter rollout plan.

Watch for common failure modes

The most common weak outputs are:

• tracing advice that ignores context propagation
• backend recommendations without ecosystem fit
• too many spans with no sampling discussion
• abstract diagrams that do not match your actual services

If you see these, refine the prompt with service names, expected call sequence, and current telemetry stack.

Ask the model to validate assumptions

A strong follow-up prompt is:

Using the distributed-tracing skill, list the assumptions in your design and mark which ones I should verify before rollout.

This is useful because the source skill is guidance-heavy and does not include automatic checks or scripts.

Improve outputs by requesting comparison and tradeoffs

If you are making an adoption decision, do not ask only for a recommended tool. Ask for tradeoffs.

Example:

Use the distributed-tracing skill to recommend Jaeger or Tempo for our platform, then give the top 3 reasons against the recommendation so we can review the tradeoffs.

This usually produces a more trustworthy answer than a one-sided recommendation.

Iterate after the first answer with real trace goals

After the first draft, add one of these refinement prompts:

• “Now optimize for debugging error propagation.”
• “Now optimize for low-overhead production sampling.”
• “Now revise for a team already using Grafana.”
• “Now focus on the minimum viable rollout for staging.”

That kind of iteration improves decision quality more than asking the model to “be more detailed.”

What would make the distributed-tracing skill itself better

From an adoption perspective, this skill would be stronger with:

• explicit decision criteria for Jaeger vs Tempo
• a sample prompt library for common observability scenarios
• clearer rollout sequencing from proof of concept to production
• troubleshooting checks for broken context propagation
• framework-specific examples or OpenTelemetry mapping

As it stands, the distributed-tracing skill is useful and easy to install, but it depends on the user providing enough system context to turn the guidance into a deployable plan.