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Review Chamber

Checked
athola·vlatest·writing·MIT

Analyzes text inputs like code or writing, providing structured feedback on clarity, style, and potential improvements.

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 athola/claude-night-market --skill review-chamber -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

athola

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 athola/claude-night-market at 9045831255.

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

Commit
9045831255
Manifest
plugins/memory-palace/skills/review-chamber/SKILL.md
Scanned
4 files, 1,078 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

What it does

The review-chamber skill provides a structured environment for evaluating and refining AI agent outputs. It allows users to define specific criteria, assign weights to those criteria, and then receive feedback on how well an output meets those standards. This facilitates iterative improvement of the AI's performance through targeted adjustments and prompt engineering.

When to use it

  • Evaluating draft content: Assess a blog post draft against clarity, accuracy, and tone guidelines before publication.
  • Refining code generation: Check generated code for functionality, efficiency, and adherence to coding standards.
  • Improving creative writing: Get feedback on story elements like plot coherence, character development, and pacing.
  • Validating research summaries: Ensure a summary accurately reflects the key findings of a research paper based on defined criteria.

Key capabilities

  • Criteria definition with weighting
  • Output evaluation against specified criteria
  • Feedback generation based on evaluation results
  • Iterative refinement process support

Example prompts

  • "Review this email draft for professionalism and clarity, weighting clarity 60% and professionalism 40%."
  • "Evaluate the following code snippet for efficiency and readability. Efficiency is more important (70%) than readability (30%)."
  • "Assess this short story excerpt based on plot coherence (50%), character development (30%), and pacing (20%)."

Tips & gotchas

The quality of the review depends heavily on clearly defined criteria. Vague or poorly-defined criteria will result in less actionable feedback.