Honeycomb Verify Recent Trace logo

Honeycomb Verify Recent Trace

Organization
aws
honeycomb-verify-recent-trace

Load when the operator asks to verify a recent test or deployment in Honeycomb, find the most recent trace, or show the trace a test produced. Finds the traces created since the test started and summarizes the most recent one.

Overview

Publisheraws
Repositorytools-for-devops-agent
Skill namehoneycomb-verify-recent-trace
Stars
120
Forks
97
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Honeycomb Verify Recent Trace AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aws/tools-for-devops-agent/tree/main/skills/honeycomb-verify-recent-trace
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/tools-for-devops-agent.git /tmp/tools-for-devops-agent
mkdir -p .claude/skills
cp -r /tmp/tools-for-devops-agent/skills/honeycomb-verify-recent-trace .claude/skills/honeycomb-verify-recent-trace
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Honeycomb Verify Recent Trace in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Honeycomb Verify Recent Trace on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Honeycomb Verify Recent Trace is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Honeycomb Verify Recent Trace

Requires the Honeycomb connector in the Agent Space, with its read tools enabled.

Purpose

Query Honeycomb to find the traces that were created by a recent test.

When to Use

  • The operator asks to "show me a trace in Honeycomb"
  • A test or release step needs observability verification

Verification Output

Report query results as: Queried [VIZ] where [FILTERS] by [GROUP_BY] over [TIME_RANGE] Results: [link to Honeycomb query]

Query Patterns

  • Time Range: 1200 seconds (20 minutes) - adjustable based on when the test started

Pattern 1: Get a particular trace

What service are you testing? This determines the dataset. Do you know the name of the span you expect to see?

Query for:
- dataset: [dataset]
- filter: name = [expected span name]
- time_range: [calculated time range]
- calculation: COUNT
- include_samples: true

Now take the most recent sample, and use get_trace to get the full trace. Output:

Queried [CALCULATION] where [FILTERS] over [TIME_RANGE] Found [count] results Results: [link to the query]

Most recent trace: [link to the trace] Root span: [root span name] Total spans: [count of spans] Services: [list of services]

Pattern 2: Find all traces since the test started

Query for:
- all datasets
- filter: trace.parent_id does-not-exist
- time_range: [calculated time range]
- calculation: COUNT
- breakdowns: name
- include_samples: true

Output: Queried [CALCULATION] where [FILTERS] by [BREAKDOWNS] over [TIME_RANGE] Found [count] results Results: [link to the query]

For each sample, print:

  • Trace ID
  • Span name
  • A link to the trace, from the query results or the get_trace output

Pattern 3: Find the most recent trace, precisely

Query for:
  calculated_fields: event_time=EVENT_TIMESTAMP()
  calculation: COUNT
  breakdowns: event_time, trace.trace_id
  order: event_time DESC
  limit: 1
  include_samples: false

Print the summary of the trace, as in Pattern 1.

Take the output trace_id and use get_trace to get the full trace.

Pattern 4: When you don't see any traces, get the time range right

Find out when the test or deployment started: from the operator, or from the release or test record you are working from.

Set the time range to the seconds since that start time, plus 20 seconds.

Include that in the query you're using to find traces.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Honeycomb Verify Recent Trace AI skill do?

Load when the operator asks to verify a recent test or deployment in Honeycomb, find the most recent trace, or show the trace a test produced. Finds the traces created since the test started and summarizes the most recent one.

Why use Honeycomb Verify Recent Trace on TypingMind?

Because you install it once and use it with any model. Honeycomb Verify Recent Trace is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Honeycomb Verify Recent Trace in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/tools-for-devops-agent/tree/main/skills/honeycomb-verify-recent-trace. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Honeycomb Verify Recent Trace?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Honeycomb Verify Recent Trace?

As many as you like. As long as a model supports skills, you can use Honeycomb Verify Recent Trace with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Honeycomb Verify Recent Trace AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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