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Agent skill

Code Review

by langfuse in langfuse/langfuse

Review Langfuse code changes for correctness, regressions, and best practices.

Custom licenceAuto-check passedAI & LLM Engineering

Install Code Review

skills CLI
$ npx skills add langfuse/langfuse --skill code-review -a claude-code

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

GitHub CLI
$ gh skill install langfuse/langfuse code-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/langfuse/langfuse.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/code-review .claude/skills/code-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
code-review
GitHub stars
35k
Token cost
~591 tokens
SKILL.md length
273 words
Files
2 (incl. references)
Skills in repo
33
Repo updated
First seen
Licence
Custom licence

At a glance

Review Langfuse code changes for correctness, regressions, and best practices.

  • Tasks that involve LLM observability
  • SKILL.md covers Start Here, Review Priorities, Output Expectations and Scope Guidance
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Code review

What it does

Code Review is an agent skill from langfuse/langfuse. Review Langfuse code changes for correctness, regressions, and best practices.

Its SKILL.md is about 590 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/review-checklist.md`).

It sits in AI & LLM Engineering, covering LLM observability and Code review. It works with Langfuse and ClickHouse. The repository describes itself as: 🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.

When your agent uses it

  • Tasks that involve LLM observability
  • Tasks that involve Code review

Example prompts

  • “/code-review”

What it can do on your machine

Read from SKILL.md and the folder at commit 8b58764. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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

Code Review loads about 591 tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 23 tokens; SKILL.md has 273 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~591
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

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 passed

The automated check found no risky patterns in SKILL.md.

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

Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 273 words (~591 tokens).

“Use this skill when the task is to review code changes rather than implement a feature.”

— opening of SKILL.md by langfuse, Custom licence
name
code-review

Read the full SKILL.md on GitHub

Files

SKILL.md and 1 other file (references) in .agents/skills/code-review of langfuse/langfuse.

  • SKILL.md
  • references/review-checklist.md

Open the folder on GitHubat commit 8b58764

Compare with similar skills

Code 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.

Code Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Code Review this skilllangfuse/langfuse35k—~591Automated safety check: PassCustom licence
Phx Triageoliver-kriska/claude-elixir-phoenix564—~948Automated safety check: PassMIT
Langfuseavivsinai/langfuse-mcp1131 repos~580Automated safety check: PassMIT
Langfuse Integration Pagelangfuse/langfuse-docs246—~3.7kAutomated safety check: PassMIT
Langfuselangfuse/skills299—~2.1kAutomated safety check: NotesMIT
Migrate To Langfuselangfuse/skills299—~1.7kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Code Review

What does Code Review do?

Review Langfuse code changes for correctness, regressions, and best practices. Code Review is an agent skill from langfuse/langfuse. Review Langfuse code changes for correctness, regressions, and best practices.

When should I use Code Review?

Code Review fits situations like: tasks that involve LLM observability; tasks that involve Code review.

How do I install Code Review in Claude Code?

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

How do I install Code Review in Codex?

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

Can I use Code 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 langfuse/langfuse --skill code-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/code-review, .gemini/skills/code-review, .github/skills/code-review and .opencode/skills/code-review in your project.

What does Code Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Code Review is instructions for the agent only.

Does Code Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Code Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Code Review use?

Code Review has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.

How many tokens does Code Review use?

About 591 tokens (SKILL.md is roughly 2.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.2k tokens, read only when the agent opens those files.

What are the alternatives to Code Review?

Skills that share tags, products or a category with Code Review: Phx Triage (oliver-kriska/claude-elixir-phoenix, 564 stars), Langfuse (avivsinai/langfuse-mcp, 113 stars), Langfuse Integration Page (langfuse/langfuse-docs, 246 stars) and Langfuse (langfuse/skills, 299 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Review?

langfuse (a GitHub organization) maintains it in langfuse/langfuse, which has 35,460 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 7, 2026.

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