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evaluation-methodology skillA

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evaluation-methodology is agent-read markdown (skill) from rudycity/superagent: PluginEval quality methodology — dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when calibrating scoring thresholds for your marketplace, or when explaining quality badges to external partners like Neon..

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

# Evaluation Methodology

This document is the authoritative reference for how PluginEval measures plugin and skill quality.
It covers the three evaluation layers, all ten scoring dimensions, the composite formula, badge
thresholds, anti-pattern flags, Elo ranking, and actionable improvement tips.

Related: [Full rubric anchors](references/rubrics.md)

---

## The Three Evaluation Layers

PluginEval stacks three complementary layers. Each layer produces a score between 0.0 and 1.0 for
each applicable dimension, and later layers override or blend with earlier ones according to
per-dimension blend weights.

### Layer 1 — Static Analysis

**Speed:** < 2 seconds. No LLM calls. Deterministic.

The static analyzer (`layers/static.py`) runs six sub-checks directly against the parsed SKILL.md:

| Sub-check | What it measures |
|---|---|
| `frontmatter_quality` | Name presence, description length, trigger-phrase quality |
| `orchestration_wiring` | Output/input documentation, code block count, orchestrator anti-pattern |
| `progressive_disclosure` | Line count vs. sweet-spot (200–600 lines), references/ and assets/ bonuses |
…

Read the whole file at its exact version.

How to install

Latest version
mdr add rudycity/superagent/evaluation-methodology@git:20260626.019dba9
Exact content
mdr add rudycity/superagent/evaluation-methodology@sha256:1fe24ef305365142

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

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Versions

versioncommittedcommitsizeaudit
git:20260626.019dba9 latest2026-06-26 019dba9 22,160 BA view

Audit of the latest version

A  17 of 17 checks passed. Deterministic, no model, same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (22160 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

rudycity/superagent · 23 stars · license MIT · pushed 2026-09-22 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_6xovf7mnm5r4x7fy
GET https://markdownregistry.com/api/v1/resolve?ref=rudycity/superagent/evaluation-methodology
GET https://markdownregistry.com/api/v1/blob/1fe24ef3053651427ee3c82927e28a12f9fc89209fd54e0f9e487319ac80beb0

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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