Eval Harness Builder
Your skill works today. Will it work after the next model update? Build the harness that answers with numbers. Builds a standalone regression test harness with mechanical grading to verify skill behavior after model or code updates.
$12
· or 60 creditsSecure checkout via Stripe
Works with the AI tools you already use
About this skill
The problem
Skills are often trusted based on vibes rather than data. Without a formal regression suite, model updates or minor prompt edits can silently break core functionality without you noticing until a user complains.
What it does
- Analyzes a target SKILL.md to identify absolute promise-breaking behaviors.
- Generates five structured test cases including happy paths, defect detection, and boundary rule triggers.
- Constructs a "Probe" test case specifically designed to tempt the model into its known failure modes.
- Produces a standalone markdown harness with binary MECH (mechanical) and JUDGMENT grading criteria.
- Designs adversarial ground-truth sets for detection-type skills to measure false-positive rates.
Why this beats prompting it yourself
Most DIY evals suffer from "rubber-stamping" where the model passes everything that looks roughly correct. This skill enforces 0% partial credit and two-directional grading to catch both missed defects and fabricated errors that humans often overlook during manual testing.
Use cases
- Verify skill stability after swapping from GPT-4o to Claude 3.5 Sonnet.
- Run a regression suite after refactoring a skill's instruction set.
- Audit a skill's performance against "lazy" failure modes before marketplace listing.
- Debug why a skill is suddenly hallucinating or skipping safety guards.
Known limitations
Requires the user to provide or identify a reference document for any criteria involving subjective taste or brand tone.
Details
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Permissions
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Creator
Scar Tissue Systems is one person's working AI-agent toolkit, sold as-is. I build small, focused skills for people who run real work through AI agents daily, capturing and filing what they find, keeping their skill systems honest over time, and catching the patterns that make AI-assisted writing read as machine-written. Every skill here started as something I built for my own workflow, used for months, broke in real ways, and fixed before I ever considered selling it. The name is the promise: nothing ships until something has actually gone wrong with it first. No filler, no theoretical best practices, every rule in every skill exists because something specific broke without it, and the changelogs say so.
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