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Traiage Review

OrganizationPopular
NeoLabHQ
traiage-review

This skill should be used when need prioritize what changed code in repository human must review.

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill nametraiage-review
Stars
1.8K
Forks
160
Bundled files
Instructions only
LicenseGPL-3.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Traiage Review 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/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/traiage-review
  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/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/traiage-review .claude/skills/traiage-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Traiage Review 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 Traiage Review 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 Traiage Review 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.

Traiage Review

Goal

The goal of this skill is to help human prioritize specific files in repository that require attention over the entire list of changes.

CRITICAL: Human attention and time is limited. Reviewer cannot check all existing changes in repository. Your job is not to find all changes that require attention, you job is to build exhaustive list of files from whole pool of changes, that probably will cause this change to fail review!

Rules

  • You are orcestrator agent, you only launch agents and pass them data. You do not do any other work.
  • You ARE allowed to run the following read-only commands yourself, and ONLY these:
    • The random sample script (see "## Random Sample Script").
    • Read-only git detection commands needed for review-mode and default-branch detection: git status, git diff --name-only, git branch, git symbolic-ref, git rev-parse.
    • Read-only commit commands needed for commit-mode detection and latest-commit resolution: git rev-parse HEAD (resolve the latest commit), git show --name-only, git diff (commit-diff inspection).
  • You MUST NOT do any implementation work, read source files, or run any mutation git command (commit, stash, push, checkout, reset, revert, add, merge, rebase, etc.). If you try to read source files or run any command outside the allowed read-only list above, you will be killed! Your life is at stake!

Process

  1. Determine the review mode BEFORE launching agents:
    • If a commit param was passed to the skill → use the commit workflow directly (this takes precedence over the local-changes/branch auto-detection below):
      • If a concrete commit hash was given (commit <commit-hash>) → use that hash.
      • If no hash was given (commit or latest commit) → resolve the latest commit with git rev-parse HEAD and use the resolved concrete hash.
      • Record the resolved concrete commit hash for use in the commit-mode prompt and the random sample script.
    • Else, if a branch param was passed to the skill → use the branch-diff workflow directly.
    • Else, check for local changes with a read-only command: git status --porcelain (non-empty output means staged, unstaged, or untracked changes exist).
      • If local changes exist → use the local-changes workflow (default behavior).
      • If there are NO local changes → detect the default branch (see below) and check whether the current branch IS the default:
        • Current branch: git rev-parse --abbrev-ref HEAD.
        • If the current branch is NOT the default → use the branch-diff workflow (diff current branch against the detected default branch).
        • If the current branch IS the default → STOP and report to the user that there are no local changes and the current branch is the default branch, so there is nothing to compare against.
    • Default-branch detection (read-only, robust):
      • Primary: git symbolic-ref --short refs/remotes/origin/HEAD → strip the origin/ prefix to get main or master.
      • Fallback (if primary fails): git rev-parse --verify origin/main — if it succeeds, default is main; otherwise try git rev-parse --verify origin/master — if it succeeds, default is master.
      • Record the detected default ref as origin/<default-branch> (e.g. origin/main) for use in the branch-mode prompt and the random sample script.
  2. Launch 4 parallel agents, to build his own list of files that require attention based on specific process for each agent. Pass the mode-appropriate agent prompt (see "## Agents"): the local-changes prompt for the local-changes workflow, the branch-mode prompt (with the detected origin/<default-branch> filled in) for the branch-diff workflow, or the commit-mode prompt (with the resolved concrete <commit-hash> filled in) for the commit workflow.
    • change-story-agent
    • change-impact-agent
    • change-failure-agent
    • change-expectation-agent
  3. Each agent will produce lists of key files by his opinition. On top of that Change Expectation Agent will produce list of declarative files.
  4. Parse all lists of key files, and build final list of files that require attention.
    • Pick top 5 files from each agent. If some of them are the same, pick more from each agent that have higher scores across all criteria and high enough confidency. At this stage your job is to build list until it reach 20 files. (ignore declarative files for this stage)
  5. Run python script to pick 20 random files from whole batch of changed files. Pick 5 files from this list and add it to final list, if some of them are already in final list, pick more from this list.
  6. Report final list of files that require attention, with key fact summary from Change Story agent, list of random sample files and list of declarative files.

Agents

Choose the prompt that matches the review mode determined in the Process step, and pass it EXACTLY to launch change-story-agent, change-impact-agent, change-failure-agent and change-expectation-agent.

Local-changes prompt

Default path for local-changes workflow — staged, unstaged, and untracked changes:

md

Review current project staged AND unstaged changes according to your process and provide list of files that require attention.

Branch-mode prompt for branch-diff workflow

Replace origin/<default-branch> with the default ref you detected, e.g. origin/main:

md

Review the diff of the current branch against the default branch `origin/<default-branch>` (use `git diff origin/<default-branch>...HEAD`, three-dot) according to your process and provide list of files that require attention.

Commit-mode prompt for commit workflow

Replace <commit-hash> with the concrete commit hash the orchestrator resolved:

md

Review the diff introduced by commit `<commit-hash>` (use `git show <commit-hash>` or `git diff <commit-hash>^!`, equivalently `git diff <commit-hash>^ <commit-hash>`) according to your process and provide list of files that require attention.

Random Sample Script

Use this script to pick 20 random files from the whole batch of changed files. Use the local mode block for the local-changes workflow (staged + unstaged + untracked), the branch mode block for the branch-diff workflow (files changed between the default branch and the current branch), and the commit mode block for the commit workflow (files changed by the commit). Replace origin/<default-branch> with the default ref you detected (e.g. origin/main), and <commit-hash> with the resolved concrete commit hash.

Local mode (staged + unstaged + untracked):

python

import random
import subprocess

# Tracked changes (staged + unstaged) relative to HEAD
tracked = subprocess.check_output(['git', 'diff', '--name-only', 'HEAD']).decode('utf-8').splitlines()

# Untracked files (new, not yet added)
untracked = subprocess.check_output(['git', 'ls-files', '--others', '--exclude-standard']).decode('utf-8').splitlines()

changed_files = sorted(set(tracked + untracked))

# Pick up to 20 random files (won't crash on small changesets)
random_files = random.sample(changed_files, min(20, len(changed_files)))

print(random_files)

Branch mode (files changed on the current branch vs the default branch):

python

import random
import subprocess

# Files changed between the default branch and the current branch (three-dot: since the merge base)
default_ref = 'origin/<default-branch>'  # e.g. 'origin/main' — set to the detected default ref
changed_files = sorted(set(
    subprocess.check_output(
        ['git', 'diff', '--name-only', f'{default_ref}...HEAD']
    ).decode('utf-8').splitlines()
))

# Pick up to 20 random files (won't crash on small changesets)
random_files = random.sample(changed_files, min(20, len(changed_files)))

print(random_files)

Commit mode (files changed by the commit):

python

import random
import subprocess

# Files changed by the commit
commit = '<commit-hash>'  # set to the resolved concrete commit hash
changed_files = sorted(set(
    line for line in subprocess.check_output(
        ['git', 'show', '--name-only', '--pretty=format:', commit]
    ).decode('utf-8').splitlines()
    if line.strip()
))

# Pick up to 20 random files (won't crash on small changesets)
random_files = random.sample(changed_files, min(20, len(changed_files)))

print(random_files)

In list of random files, pick only files that relate to logic changes, ignore documentation, tests, configuration, etc. Except case when there no files left, that wasn't highlighted by agents key files list.

Output Format

md

### Key Facts

<note>Key facts should be provided by Change Story Agent</note>

- What change trying to achive: <if any>
- Architecture change: <if any>
- Design decisions: <if any>
- Risks: <if any>
- Solutions: <if any>

### Key Files

| File Path        | Changed Lines         | Importance   | Severity   | Detectability   | Confidence |
|------------------|-----------------------|--------------|------------|-----------------|------------|
| <file path>      | <changed lines count> | <importance> | <severity> | <detectability> | <confidence> |

<note>
- include in last column confidence rating that was provided by agent that provided this file (if there multiple, include highest confidence)
- in the rest columns include ratings that were provided by agent that provided this file (if there multiple, include highest rating. If there no rating, mark it as "-")
</note>

### Random Sample

| File Path   | Changed Lines         |
|-------------|-----------------------|
| <file path> | <changed lines count> |


### Declarative Files

| File Path   | Changed Lines         |
|-------------|-----------------------|
| <file path> | <changed lines count> |

Frequently asked questions

What does the Traiage Review AI skill do?

This skill should be used when need prioritize what changed code in repository human must review.

Why use Traiage Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/traiage-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Traiage Review?

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 Traiage Review?

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

Is the Traiage Review AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 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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