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Twg Bench Lite

Organization
zenobi-us
twg-bench-lite

Run a lightweight single-prompt A/B comparison of free Atlassian/local MCP context vs TWG CLI graph context using paired read-only agent sessions.

Overview

Publisherzenobi-us
Repositorydotfiles
Skill nametwg-bench-lite
Stars
67
Forks
6
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

    Published by zenobi-us on GitHub. Read the source before you install it.

Installation

Install the Twg Bench Lite 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.
  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/zenobi-us/dotfiles.git /tmp/dotfiles
mkdir -p .claude/skills
cp -r /tmp/dotfiles/files/devtools/agent/bundles/developer/skills/atlassian/twg-bench-lite .claude/skills/twg-bench-lite
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Twg Bench Lite 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 Twg Bench Lite 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 Twg Bench Lite 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.

twg-bench-lite

Run scripts/benchmark-lite/runner.ts directly. Use its run, render, and doctor subcommands.

Run one read-only prompt twice: once with free Atlassian/local MCP context and once with paid Atlassian Teamwork Graph context through TWG CLI. This is a skill-first workflow with a thin twg benchmark lite run bridge. The benchmark repo owns reusable runner, judge, and report-template code.

CLI launcher fallback

Run twg <command>. On shell command not found, use $HOME/.local/bin/twg (macOS/Linux) / $env:LOCALAPPDATA\Programs\twg\bin\twg.exe (PowerShell), then tell user to add that directory to PATH. Do not treat auth or command errors as PATH failures.

Runtime Support

V1 supports Codex and Rovo live runs. Claude is V2. Do not require users to install the benchmark CLI locally.

Eligibility

Use a text-only read task both arms can attempt. Do not compare mutations; for writes, compare a read-only plan or dry-run only after user approval. Preserve the underlying prompt byte-for-byte for both arms.

Prerequisite

For the default full lifecycle command, verify the portable runner and selected runtime first:

bash
twg benchmark lite --check

The default check is Codex; use --agent rovo when the run should use Rovo.

For Rovo runs:

bash
twg benchmark lite --check --agent rovo

The quality judge defaults to the selected runtime, so Rovo runs use a Rovo judge by default. If the user explicitly wants a different judge runtime, use:

bash
twg benchmark lite --check --agent rovo --judge-agent codex

The managed custom agents are only required for manual orchestration:

  • twg-bench-lite-control
  • twg-bench-lite-test

If a manual run needs either agent and it is missing, tell the user to run:

bash
twg skills install --yes

Then start a fresh Codex thread so agent discovery refreshes. Do not substitute generic agents for manual orchestration because arm isolation is the point.

Before a live run, verify setup with:

bash
twg benchmark lite --check

This checks the bundled runner artifact and the selected live-run runtime. Codex checks include the Codex executable and Codex home. Rovo checks include the Rovo executable and OAuth status. If --judge-agent differs from --agent, the check verifies both runtimes. It also reports the global twg-bench-lite skill install and managed control/test Codex agent configs as manual-workflow optional checks when relevant.

Arms

  • Control - Free Atlassian with Local MCPs Context: use free Atlassian tools plus user-local MCPs/connectors. Do not use TWG CLI, TWG skills, paid graph tools, prior artifacts, or local files as task evidence unless the prompt is explicitly about local files.
  • Test - Paid Atlassian Teamwork Graph Context: use TWG CLI graph context with bundled TWG guidance as the primary work-data source.

Paired Run

For the default full lifecycle, run:

bash
twg benchmark lite run --prompt "<read-only prompt>" --output-dir <dir>

Use --agent rovo to run the same lifecycle with Rovo. The quality judge uses the same runtime unless --judge-agent codex|rovo is supplied.

For manual orchestration:

  1. Save the exact comparison prompt.
  2. Ask TWG CLI for the test-arm route before launching the paired agents: twg benchmark lite plan --prompt-file <prompt-file> --json.
  3. Spawn both managed agents concurrently with no forked parent context: twg-bench-lite-control and twg-bench-lite-test.
  4. Send the identical prompt as the complete user message to each child. Do not add arm-specific instructions; the managed agent configs own isolation. The test agent may receive the TWG route plan as tool-selection policy only; it is not answer evidence.
  5. Leave model/reasoning overrides unset unless the user asks; apply any override equally to both arms.
  6. Wait for both children to finish and record their agent IDs or session links.
  7. Do not solve the task again in the parent. The parent compares, evaluates, and reports.

Print lifecycle progress in the parent session: start, prompt captured, agents started, each arm finished, outputs read, quality reviewed, report compiled, done.

Quality

Read both answers before interpreting token delta. Classify as equivalent, twg-better, control-better, capability-gain, or not-comparable. not-comparable is a real outcome for missing, unusable, or non-comparable answers, not a placeholder. Claim observed savings only for equivalent or twg-better; for capability-gain, report capability instead.

Evaluate coverage, correctness, evidence quality, directness, and limitations. Do not assign numeric quality scores.

Lite Report

Return a compact report in the parent answer with:

  • prompt text,
  • control/test status and final outputs,
  • observed tool families and TWG command evidence when available,
  • token/tool/duration metrics when the agent runtime exposes them,
  • quality classification and rationale,
  • integrity notes: identical prompt, expected roles, TWG absent from control, TWG used in test, and any measurement gaps.

If token/session metrics are unavailable, say "not measured"; do not estimate. Describe the result as one observed prompt comparison, not a universal efficiency rate.

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 Twg Bench Lite AI skill do?

Run a lightweight single-prompt A/B comparison of free Atlassian/local MCP context vs TWG CLI graph context using paired read-only agent sessions.

Why use Twg Bench Lite on TypingMind?

Because you install it once and use it with any model. Twg Bench Lite 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 Twg Bench Lite in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zenobi-us/dotfiles/tree/master/files/devtools/agent/bundles/developer/skills/atlassian/twg-bench-lite. 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 Twg Bench Lite?

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 Twg Bench Lite?

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

Is the Twg Bench Lite AI skill free?

Yes. It is published on GitHub by zenobi-us 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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