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Agent skill

Opencode Gpt Review

by dyoshikawa in dyoshikawa/rulesync

Run the review-full-pr skill on another model (github-copilot/gpt-5.4) via opencode, then thoroughly investigate and vet the resulting findings, and present only the valid ones to the user.

MITAuto-check: notesDevelopment

Install Opencode Gpt Review

skills CLI
$ npx skills add dyoshikawa/rulesync --skill opencode-gpt-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install dyoshikawa/rulesync opencode-gpt-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/dyoshikawa/rulesync.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.rulesync/skills/opencode-gpt-review .claude/skills/opencode-gpt-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
opencode-gpt-review
GitHub stars
1.5k
Token cost
~811 tokens
SKILL.md length
398 words
Files
1
Skills in repo
40
Repo updated
First seen
Licence
MIT

At a glance

Run the review-full-pr skill on another model (github-copilot/gpt-5.4) via opencode, then thoroughly investigate and vet the resulting findings, and present only the valid ones to the user.

  • Works in 4 steps: Variable Definitions → Run review-full-pr via opencode → Investigate and Vet the Review Results → …
  • Development work in your project
  • SKILL.md covers Prerequisites, 0. Variable Definitions, 1. Run review-full-pr via… and 2. Investigate and Vet the…, plus 5 more sections
  • Calls opencode, curl and bash; reaches opencode.ai

What it does

Opencode Gpt Review is an agent skill from dyoshikawa/rulesync. Run the review-full-pr skill on another model (github-copilot/gpt-5.4) via opencode, then thoroughly investigate and vet the resulting findings, and present only the valid ones to the user.

Its SKILL.md is about 810 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. It works with OpenAI. The repository describes itself as: A Utility CLI for AI Coding Agents. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/opencode-gpt-review”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Variable Definitions
  2. Run review-full-pr via opencode
  3. Investigate and Vet the Review Results
  4. Present the Results

What it can do on your machine

Read from SKILL.md and the folder at commit 2c577d0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • opencode
    • curl
    • bash

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • opencode.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Opencode Gpt Review loads about 811 tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 398 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~811

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePipes a well-known installer script into a shellSKILL.md:20
    tell the user it can be installed with `curl -fsSL https://opencode.ai/install | bash`.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from dyoshikawa/rulesync at commit 2c577d0, republished under its MIT licence (© dyoshikawa). 398 words, ~811 tokens.

Download SKILL.mdSave it as .claude/skills/opencode-gpt-review/SKILL.md (or your agent's skills folder).
name
opencode-gpt-review
description
Run the `review-full-pr` skill on another model (github-copilot/gpt-5.4) via opencode, then thoroughly investigate and vet the resulting findings, and present only the valid ones to the user.
targets
*

GPT Review

Run the review-full-pr skill on another model (github-copilot/gpt-5.4) via opencode, then thoroughly investigate and vet the resulting findings, extract only the valid ones, and present them to the user.

Prerequisites

  • OpenCode must be installed.
    • If not installed, tell the user it can be installed with curl -fsSL https://opencode.ai/install | bash.
  • OpenCode must be configured to use GitHub Copilot.
    • If not configured, tell the user to run opencode and use the /connect command to authenticate with GitHub Copilot.

0. Variable Definitions

PR_TARGET = the user's request

If no PR URL or PR number is provided, use the PR associated with the current branch as the review target.

1. Run review-full-pr via opencode

Run the following command to have the github-copilot/gpt-5.4 model execute the review-full-pr skill via opencode.

bash
opencode run \
  --model github-copilot/gpt-5.4 \
  "review-full-pr スキルで ${PR_TARGET} をレビューしてください"
  • --model github-copilot/gpt-5.4: Use the GPT-5.4 model.

Capture all output from the command.

2. Investigate and Vet the Review Results

For each finding in the review results obtained from opencode, investigate thoroughly using the following steps. Consider using subagents when appropriate.

2-1. Verify the Actual Code at the Finding's Location
  • Based on the file path and line number in the finding, read the actual code to verify.
  • Also check related context (related functions, classes, settings, etc.) that underlies the finding.
2-2. Judge the Validity of the Finding

Judge each finding from the following perspectives:

  • Fact check: Does the finding match the actual code (is it not a hallucination)?
  • Impact: Does the reported issue actually have an impact?
  • Context understanding: Is it valid given the project's conventions and architecture?
  • Reproducibility: Can the reported issue actually occur?
Show full SKILL.md (138 more words)Show less
2-3. Classify the Finding
  • Valid: A finding that is confirmed to be a real issue after checking the actual code.
  • Rejected: A hallucination, misunderstanding, or a finding that is not an issue given the project context.

3. Present the Results

Output in the following format.

<Format>
# GPT Review Result

Overview

  • Review target PR: (PR URL or number)
  • opencode model: github-copilot/gpt-5.4
  • Total findings: N
  • Valid findings: M

Valid Findings

(List only findings judged as valid in the format below. If none, write "None".)

Finding 1: (Title)
  • File: path/to/file.ext:line
  • Problem: (What is the problem)
  • Reason: (Why it is a problem, based on the actual code you verified)
  • Severity: "Must fix before merge" / "Can be deferred"
  • Recommended fix: (Specific fix approach)

Rejected Findings

(List rejected findings and the reasons concisely. If none, write "None".)

  • Finding: Reason for rejection
    </Format>

References

© dyoshikawa, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .rulesync/skills/opencode-gpt-review of dyoshikawa/rulesync.

Open the folder on GitHubat commit 2c577d0

Compare with similar skills

Opencode Gpt Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Opencode Gpt Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Opencode Gpt Review this skilldyoshikawa/rulesync1.5k—~811Automated safety check: NotesMIT
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Open Code Review CLIalibaba/open-code-review45k—~3.1kAutomated safety check: PassApache-2.0
Codexskills-directory/skill-codex1.5k3 repos~1.8kAutomated safety check: PassMIT
Get API Docs with chubandrewyng/context-hub14k1 repos~775Automated safety check: PassMIT
Implementation Final Reviewopenai/openai-agents-python30k—~2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Opencode Gpt Review

What does Opencode Gpt Review do?

Run the review-full-pr skill on another model (github-copilot/gpt-5.4) via opencode, then thoroughly investigate and vet the resulting findings, and present only the valid ones to the user. Opencode Gpt Review is an agent skill from dyoshikawa/rulesync.4) via opencode, then thoroughly investigate and vet the resulting findings, and present only the valid ones to the user.

When should I use Opencode Gpt Review?

Opencode Gpt Review fits situations like: development work in your project.

How do I install Opencode Gpt Review in Claude Code?

Run `npx skills add dyoshikawa/rulesync --skill opencode-gpt-review -a claude-code`. Or copy the skill folder (.rulesync/skills/opencode-gpt-review in dyoshikawa/rulesync) into .claude/skills/opencode-gpt-review in your project. Claude Code loads it when a task matches its description.

How do I install Opencode Gpt Review in Codex?

Run `npx skills add dyoshikawa/rulesync --skill opencode-gpt-review -a codex`. Or copy the skill folder (.rulesync/skills/opencode-gpt-review in dyoshikawa/rulesync) into .agents/skills/opencode-gpt-review in your project. Codex loads it when a task matches its description.

Can I use Opencode Gpt Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add dyoshikawa/rulesync --skill opencode-gpt-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/opencode-gpt-review, .gemini/skills/opencode-gpt-review, .github/skills/opencode-gpt-review and .opencode/skills/opencode-gpt-review in your project.

What does Opencode Gpt Review need to run?

Going by SKILL.md and its folder, Opencode Gpt Review needs the command-line tools its instructions call (opencode, curl and bash).

Does Opencode Gpt Review access the network?

SKILL.md names 1 domain. In commands or code: opencode.ai; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Opencode Gpt Review safe to install?

Our automated static check of SKILL.md found notes only (pipes a well-known installer script into a shell), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Opencode Gpt Review use?

Opencode Gpt Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Opencode Gpt Review use?

About 811 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Opencode Gpt Review?

Skills that share tags, products or a category with Opencode Gpt Review: PR Design Doc (OpenHands/OpenHands, 90k stars), Open Code Review CLI (alibaba/open-code-review, 45k stars), Codex (skills-directory/skill-codex, 1.5k stars) and Get API Docs with chub (andrewyng/context-hub, 14k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Opencode Gpt Review?

dyoshikawa (a GitHub user) maintains it in dyoshikawa/rulesync, which has 1,508 GitHub stars. The repository holds 40 skills in this directory. The repository was last updated on October 9, 2026.

Source: dyoshikawa/rulesync on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.