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Review

Organization
entireio
review

Review code changes on the current branch using checkpoint transcript context to understand developer intent before auditing the diff. Use when the user asks for a code review, wants to review branch changes, or asks to audit recent work before merging.

Overview

Publisherentireio
Repositoryskills
Skill namereview
Stars
226
Forks
24
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 entireio on GitHub. Read the source before you install it.

Installation

Install the 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/entireio/skills/tree/main/skills/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/entireio/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/review .claude/skills/review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Review

Review code changes on the current branch by first reading checkpoint transcripts to understand the intent behind each change, then auditing the actual diff for issues. This produces an intent-aware review that catches mismatches between what the developer wanted and what the code actually does.

Response Format

Begin the first response to this skill invocation with the line:

Entire Review:

followed by a blank line, then the content.

  • Apply the header to the first response of the invocation only. Do not re-print it on follow-up turns within the same invocation.
  • Do not include the header on error or early-exit responses (e.g. "not a git repository", "no commits found between branch and base").

Rules

  1. This is a read-only audit. Do not modify any files during the review.
  2. Always show a scope banner before findings so the user knows what is being reviewed.
  3. If checkpoint context is unavailable, still perform the review using only the diff and commit messages. Clearly state that the review lacks intent context.
  4. Group findings by file. Within each file, order by severity (Critical > High > Medium > Low).
  5. Every finding must include a concrete suggestion or explanation, not just flag the issue.
  6. After presenting findings, ask the user if they want a fix plan. Do not auto-fix.
  7. Do not review generated files, lock files, or binary files unless the user explicitly asks for them.

Process

1. Verify environment

Run entire version to check if the CLI is available. If it is not installed, note that the review will proceed without checkpoint intent context (degraded mode).

2. Detect base ref

Find the mainline branch to diff against. Try these refs in order and use the first that exists:

bash
git rev-parse --verify origin/HEAD 2>/dev/null
git rev-parse --verify origin/main 2>/dev/null
git rev-parse --verify origin/master 2>/dev/null
git rev-parse --verify main 2>/dev/null
git rev-parse --verify master 2>/dev/null

If none exist, tell the user no base ref was found and ask them to specify one.

3. Compute scope

bash
git rev-list --count <base>..HEAD
git diff --stat <base>..HEAD

Record the number of commits and files changed. This forms the scope banner.

4. Gather checkpoint intent (skip in degraded mode)

bash
git log --format='%H%x00%s%x00%b' <base>..HEAD

Extract Entire-Checkpoint: trailer values from commit bodies. Deduplicate checkpoint IDs.

For each unique checkpoint ID (up to 20):

bash
entire checkpoint explain --checkpoint <checkpoint-id> --json --no-pager

Parse the JSON to extract session metadata. For each non-review session in the checkpoint, prefer the summary (intent + outcome). If no summary is available, fall back to the latest user prompt. Truncate each checkpoint detail to 320 characters.

If --json fails for a checkpoint, fall back to the bare human-readable output:

bash
entire checkpoint explain --checkpoint <checkpoint-id> --no-pager

Do not use --full in the review workflow — it produces long narrative text harder for agents to parse in bulk. If that also fails, skip the checkpoint and note it was unavailable.

Additionally, read the current in-progress session (if any) for uncommitted work context:

bash
entire session current --json

If this returns valid JSON, extract last_prompt and files_touched — these represent the intent behind uncommitted changes that are not yet captured in any checkpoint.

Build an intent map: for each file in the diff (committed + uncommitted), collect the relevant intent context from all checkpoints and the active session that touched it.

5. Read the diff

bash
git diff <base>..HEAD

Also include uncommitted changes:

bash
git diff HEAD

For large diffs (>5000 lines total), focus on the most critical files:

  • Files with the most changes
  • Files mentioned in checkpoint intents
  • Source files over generated/config files

Read individual file diffs as needed rather than loading the entire diff at once.

6. Perform the review

Read the review rules:

references/review-rules.md

For each file in the diff, review it considering:

With checkpoint context:

  • Does the implementation match the stated intent?
  • Were the constraints from the transcript respected?
  • Did the agent miss edge cases it discussed?
  • Are there leftover debugging artifacts or TODOs?

Always (with or without context):

  • Logic errors, off-by-one, null safety
  • Security issues (injection, auth bypass, data exposure)
  • Performance concerns (N+1 queries, unnecessary allocations)
  • Error handling gaps
  • API contract violations
  • Dead code or unreachable branches

7. Output findings

Present findings using severity prefixes that align with the entire review --fix parser:

Entire Review:

Reviewing <branch> vs <base>: N commits, M files changed
[Intent context: available from K checkpoints | unavailable — reviewing diff only]

## path/to/file.ts

Critical: <issue description>
  Intent: <what the checkpoint said this should do, if available>
  Actual: <what the code actually does>
  Fix: <concrete suggestion>

High: <issue description>
  Intent: <what the checkpoint said this should do, if available>
  Actual: <what the code actually does>
  Fix: <concrete suggestion>

Medium: <improvement opportunity>
  Issue: <what is wrong>
  Fix: <concrete suggestion>

## path/to/other-file.ts

Low: <observation that is not a bug but worth noting>

---

Found N issues (X critical, Y high, Z medium, W low).
Would you like me to create a fix plan for the critical and high issues?

When checkpoint context is unavailable for a finding, omit the Intent: line and use Issue: / Fix: instead.

8. Offer follow-up

After presenting findings, wait for the user's response:

  • If they say yes to a fix plan, outline specific steps to address each critical/high issue.
  • If they ask to fix directly, switch to implementation mode and address the issues.
  • If they decline, the review is complete.

Degraded Mode (no Entire CLI or no checkpoints)

When checkpoint context is unavailable:

  1. Skip step 4 entirely
  2. In the scope banner, state: "Intent context unavailable — reviewing diff only"
  3. Omit the "Intent:" line from findings
  4. Still perform the full code-level review from step 6
  5. Note at the end: "This review was performed without Entire checkpoint context. With checkpoints enabled, the review would also verify that implementation matches the original developer intent."

Failure Modes

  • No commits between base and HEAD: Tell the user the branch has no changes relative to the base. Do not output a review.
  • Cannot detect base ref: Ask the user to specify the base branch or commit.
  • Diff too large (>100 files): Ask the user if they want to review all files or focus on a subset. Suggest reviewing only source files or files with checkpoint context.
  • entire checkpoint explain fails: Note the specific checkpoint was unavailable, continue reviewing remaining files without that context.

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 Review AI skill do?

Review code changes on the current branch using checkpoint transcript context to understand developer intent before auditing the diff. Use when the user asks for a code review, wants to review branch changes, or asks to audit recent work before merging.

Why use Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/entireio/skills/tree/main/skills/review. 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 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 Review?

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

Is the Review AI skill free?

Yes. It is published on GitHub by entireio 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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