skills.rest
slowman2084
slowman2084COMMUNITY
Explore all skills by this author →
GitHub

meta-reviewer

Evaluate meta-prompt-engineer outputs against ExpectedOutput fingerprints for cheating and overfitting.

6|Updated Mar 5, 2026
Search Tags:#audit#evidence#review#generalization#prompt#overfitting#cheating
One-click install
npx skills add https://github.com/slowman2084/meta-agent --skill meta-reviewer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meta-reviewer
Source: https://github.com/slowman2084/meta-agent/tree/main/source/skills/meta-reviewer
Command: npx skills add https://github.com/slowman2084/meta-agent --skill meta-reviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides independent auditing of meta-prompt-engineer outputs to detect cheating, overfitting, and lack of generalization in prompts.

Core Features & Use Cases

  • Detects verbatim or paraphrased plagiarism from ExpectedOutput in candidate prompts.
  • Evaluates overfitting to known test cases and tests generalization to new inputs.
  • Produces a formal review report with evidence-driven conclusions and actionable fixes.

Quick Start

Submit the candidate prompt and the associated test cases to generate a structured, evidence-driven review.

Frequently Asked Questions about meta-reviewer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect prompt overfitting and cheating in AI prompt engineering?▼

To detect prompt overfitting and cheating, you audit candidate prompts against ExpectedOutput fingerprints. This process identifies verbatim plagiarism or paraphrased content and evaluates whether the prompt generalizes to new inputs.

What is prompt overfitting and how does it affect generalization?▼

Prompt overfitting occurs when a prompt is too closely tailored to known test cases, failing to generalize to new inputs. Auditing compares the prompt against ExpectedOutput fingerprints to identify these generalization gaps.

How do I review and audit a candidate prompt for plagiarism?▼

You review and audit a candidate prompt by submitting it with associated test cases. The audit evaluates the text for verbatim or paraphrased plagiarism from ExpectedOutput and produces a structured review with evidence.

Can I get actionable fixes when a prompt evaluation returns a REJECT decision?▼

Yes, when a prompt evaluation returns a REJECT decision, the structured review report includes actionable fixes. The report provides evidence-driven conclusions and section-by-section justifications for the PASS, WARN, or REJECT decisions.

What are the limitations of using automated reviews for prompt generalization gaps?▼

Automated reviews for prompt generalization gaps rely on comparing candidate prompts against provided ExpectedOutput fingerprints and test cases. They may not detect subtle contextual failures outside the submitted test case coverage.

Related Skills

Explore More Content & Communication Skills→
slowman2084slowman2084
6

meta-testcase-gen

Generate ten YAML test cases with empty ExpectedOutput and Judge fields.

Community
Advanced
nesnilnehcnesnilnehc
7

automate-repair

Automate iterative code review, testing, and targeted fixes to converge a repository to green.

Community
Advanced
XinAlohaXinAloha
3

verify-loop-delivery

Audit AI agent task outputs against project contracts and checklists.

Community
Advanced
HaykTarkhanyanHaykTarkhanyan

peer-review

Generate hierarchical peer-review reports with major and minor comments.

Community
Advanced