Trace Review App logo

Trace Review App

Organization
Calibre-Labs
trace-review-app

Design and build a custom trace review app for one AI product: a single-page tool where the team reads traces, writes notes, labels pass or fail, tags trace codes and exports the results for other skills. Built around that product's own trace shape, rubric and failure modes, and set up so reviewing becomes a habit (a queue, progress, streaks, quick keys). Use this skill whenever someone asks to "build a trace viewer", "make an annotation tool", "set up a review page for our traces", "make trace review less painful", "build a labeling app", or says the team isn't reading traces because the tool is clunky.

Overview

PublisherCalibre-Labs
Repositoryreforge-ai-evals
Skill nametrace-review-app
Stars
94
Forks
9
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

    Published by Calibre-Labs on GitHub. Read the source before you install it.

Installation

Install the Trace Review App 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/Calibre-Labs/reforge-ai-evals/tree/main/skills/trace-review-app
  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/Calibre-Labs/reforge-ai-evals.git /tmp/reforge-ai-evals
mkdir -p .claude/skills
cp -r /tmp/reforge-ai-evals/skills/trace-review-app .claude/skills/trace-review-app
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trace Review App 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 Trace Review App 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 Trace Review App 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.

Trace review app skill

Generic trace tools show every trace the same way and leave the judging to the reader. Teams open them, spend too long learning them, and stop reviewing. A review app built for one product shows what matters for that product first, asks the questions its rubric asks, and makes a review session short enough to do every day.

This skill designs that app and builds it as one HTML file that opens from a folder, with no hosting and no account. The person still does the reading and the judging. The app makes that faster and keeps the results in a shape the other skills can use.

Inputs

InputRequiredTypical form
Sample tracesYesA folder of trace files (JSON or JSONL), five or more
What the product doesYesThe PRD, or a few sentences from the user
RubricHelpfulThe eval rubric, so labels can be per rubric line
Trace codesHelpfulA trace codes file from trace-codes, used as tags
Who reviewsHelpfulPMs, domain experts, support leads, and how many

Look for these in the working folder before asking. Read at least five traces before designing anything: the shape of the trace decides the layout.

Workflow

Step 1. Learn the product and its traces

Write down, in a few lines each:

  • What one session is: one turn or many, which tools, what the user sees at the end.
  • What the final output looks like (a table, an email, a booking, code, a chart) and how to render it so a reviewer sees what the user saw.
  • Where the failures worth finding sit: in the final answer, in a tool call, in what was retrieved, in cost or latency. Use the rubric and trace codes if they exist.
  • Which parts of a trace a reviewer can skip.

Step 2. Ask three questions

Ask only what the files don't answer:

  1. Who reviews, and how much time do they have? (Five minutes a day and an hour a week need different designs.)
  2. What do the reviews feed? Free-text notes for trace-codes, pass or fail labels for llm-align, new dataset rows for ticket-to-eval, or all three.
  3. Is there anything a reviewer must never see, such as personal data that should be masked?

Step 3. Write the design

Write a one-page design and show it before building. Cover:

  • The queue. What order traces come in (newest, flagged by a judge, random sample, least reviewed) and how a reviewer picks up where they left off.
  • The trace view. What shows first: the user's request and the final output, rendered the way the user saw it. Then the steps, collapsed by default, with the product's own labels (for an agent: model calls, tool calls, retrieved content, tagged by part of the product). Highlight what the rubric cares about, such as sources next to the figures they support.
  • The review panel. A notes box, pass or fail per rubric line or overall, trace-code tags with a way to add a new one, and a "flag for discussion" button. Keyboard shortcuts for every action.
  • Keeping it a habit. A daily goal (for example ten traces), a progress bar for the queue, a streak for days with a review, a short summary at the end of a session (traces read, codes used most, disagreements with the judge), and per-reviewer counts when more than one person reviews. Keep it calm: no points for speed, nothing that rewards labeling without reading, and celebrations small and rare.
  • Codes back in the app. If the app keeps notes in a shared store, add a Codes tab that shows what trace-codes writes to codes/current (see that skill), and the codes on each trace. Then nobody has to export notes to cluster them.
  • Exports. Notes as CSV for trace-codes (trace id, input, reviewer, note), labels for llm-align (trace id, rubric line, label, reviewer), and flagged traces as candidate dataset rows. One click each.
  • Where it keeps data. In the browser for one reviewer, or a shared store if several people review the same traces. Say which and why.

Step 4. Build it

Build one self-contained HTML file that loads trace files the user picks from a folder (or reads them from a path next to the file). Follow the product's own trace format; don't force it into a generic schema. Keep the design plain and readable: the trace is the content.

Step 5. Test it on real traces

Open it with the sample traces and do a short review session yourself: label five traces, tag two codes, export each file and check the exports load in the skills that read them. Fix what gets in the way. Then hand it over with one paragraph on how to use it and the keyboard shortcuts.

Output

  1. The design, as a short markdown file next to the app.
  2. The app, as one HTML file.
  3. A sample export of each kind, from the test session.

Example

The fall 2026 course's Mapper trace review page is a small version of this: ten Mapper sessions, the market map rendered the way the user saw it, the steps tagged by part of the product, a notes box saved under each reviewer's name, a Cluster button that runs the trace-codes method on Claude and fills a Codes tab, and an instructor key only editors see. A full version would add pass or fail labels per rubric line, code tags, a queue and a daily goal.

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 Trace Review App AI skill do?

Design and build a custom trace review app for one AI product: a single-page tool where the team reads traces, writes notes, labels pass or fail, tags trace codes and exports the results for other skills. Built around that product's own trace shape, rubric and failure modes, and set up so reviewing becomes a habit (a queue, progress, streaks, quick keys). Use this skill whenever someone asks to "build a trace viewer", "make an annotation tool", "set up a review page for our traces", "make trace review less painful", "build a labeling app", or says the team isn't reading traces because the t...

Why use Trace Review App on TypingMind?

Because you install it once and use it with any model. Trace Review App 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 Trace Review App in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Calibre-Labs/reforge-ai-evals/tree/main/skills/trace-review-app. 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 Trace Review App?

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 Trace Review App?

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

Is the Trace Review App AI skill free?

Yes. It is published on GitHub by Calibre-Labs under the MIT 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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