Skip to content
TrustedSkillsbeta
Back to all skills

Review Feedback Schema

Checked
existential-birds·vlatest·database·Apache-2.0

Helps with code review, schema design as part of agent workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add existential-birds/beagle --skill review-feedback-schema -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

existential-birds

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned existential-birds/beagle at d1a74899fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
d1a74899fb
Manifest
plugins/beagle-core/skills/review-feedback-schema/SKILL.md
Scanned
1 file, 187 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

review-feedback-schema

What it does

This skill enables AI agents to generate structured feedback schemas for reviewing specific outputs or processes. It defines clear evaluation criteria and formats results consistently, ensuring that reviews are actionable and standardized across different tasks.

When to use it

  • Automated Code Reviews: Create a checklist for an agent to evaluate pull requests against style guides and security standards.
  • Content Quality Assurance: Define specific metrics (e.g., tone, clarity, fact-checking) for reviewing blog posts or marketing copy before publication.
  • Project Status Updates: Structure feedback on sprint reports to ensure all team members address blockers and completed tasks uniformly.
  • Data Validation: Establish a schema for agents to verify the accuracy and completeness of generated datasets or API responses.

Key capabilities

  • Generates predefined evaluation rubrics tailored to specific domains.
  • Structures unstructured review notes into consistent, machine-readable formats.
  • Enforces standardized output patterns for feedback loops.
  • Facilitates clear communication between AI agents and human reviewers.

Example prompts

  • "Create a review-feedback-schema for evaluating Python code against PEP 8 standards."
  • "Generate a schema to assess the clarity and persuasiveness of a sales email draft."
  • "Define a feedback structure for an agent to critique a weekly project status report."

Tips & gotchas

Ensure you provide sufficient context about the specific domain or output type when requesting a schema, as generic templates may lack necessary detail. This skill is most effective when paired with other evaluation or analysis skills to execute the actual review process.