ai-agent-evaluation-benchmarking SKILL.md skillA

ai-agent-evaluation-benchmarking from francostino/opencode-skills-collection is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add francostino/opencode-skills-collection/ai-agent-evaluation-benchmarking@git:20261015.fac8b7e

Pins git:20261015.fac8b7e in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
francostino/opencode-skills-collection
Path
bundled-skills/ai-agent-evaluation-benchmarking/SKILL.md
Versions recorded
2
Latest version
git:20261015.fac8b7e, committed Oct 15, 2026
First version recorded
Oct 3, 2026
Size
6,355 bytes
Repository stars
87
License
MIT

Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# AI Agent Capability Evaluation & Benchmarking

**What it is:** Standardizes multi-metric capability benchmarking, token cost efficiency, and regression monitoring across autonomous coding agents.

## Overview

Provides a standardized, auditable framework and data model for **AI Agent Capability Evaluation & Benchmarking** operations across distributed engineering and decentralized application systems.

## When to Use This Skill

- When recording agent evaluation runs with a fixed dataset, rubric, runtime/model revision and measured outcomes.
- When comparing a candidate with a baseline under matched tasks, environment, tool permissions and cost accounting.
- When generating reproducible CSV, SQL DDL, JSON Schema, and Notion property registers for tracking compliance.

## How It Works

1. Define the parameters, thresholds, and identity bindings required for the target operational register.
2. Select appropriate boundary enforcement values from validated enum select sets.
3. Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.

## Field Reference
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add francostino/opencode-skills-collection/ai-agent-evaluation-benchmarking@sha256:952617fd1a233abd

The line at the top of this page pins the label git:20261015.fac8b7e, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_r6jk3kcsjggikujt.svg)](https://markdownregistry.com/a/art_r6jk3kcsjggikujt)

1 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 2 versions of this file, between Oct 3, 2026 and Oct 15, 2026, about one every 12 days.

versioncommittedcommitsizeaudit
git:20261015.fac8b7e latest2026-10-15 fac8b7e 6,355 BA view · diff
git:20261003.12d23182026-10-03 12d2318 5,387 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the 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 (6355 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

francostino/opencode-skills-collection · 87 stars · license MIT · pushed 2026-10-18 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_r6jk3kcsjggikujt
GET https://markdownregistry.com/api/v1/resolve?ref=francostino/opencode-skills-collection/ai-agent-evaluation-benchmarking
GET https://markdownregistry.com/api/v1/blob/952617fd1a233abd7f0bea8d3b6514a687df4f109d32bc13c19e94943de9aebe

Your AI assistant 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.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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