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NVIDIAKnown publisherNVIDIA/Model-Optimizer/skills/evaluation/SKILL.md

evaluation

Skill5.6k starsChanged 3 days agoApache-2.0View on GitHub

Compatibility name for modelopt-evaluation. Evaluate model accuracy with NeMo Evaluator Launcher.

22 other projects use a version of this.

Trending

More talk lately.about the whole repository

Dec 2025: 0 mentionsJan 2026: 2 mentionsFeb 2026: 1 mentionMar 2026: 0 mentionsApr 2026: 2 mentionsMay 2026: 1 mentionJun 2026: 0 mentionsJul 2026: 0 mentionsAug 2026: 2 mentionsSep 2026: 20 mentionsOct 2026: 15 mentionsNov 2026: 11 mentions
Dec 2025Jun 2026Nov 2026
Mentions a month of the NVIDIA/Model-Optimizer repository that our weekly crawl found. There is no adoption data for this file yet.

Most recently taken up by

Who's talking about NVIDIA/Model-Optimizer

The biggest 5 of 54 mentions linking to the repository that our crawl found. Each line is their own headline.

See all 54

Talked about on GitHub and Bluesky (54 times).

On GitHub · 50

45 more on GitHub

Used with

Projects with it also use bdi-mental-states, hosted-agents and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Downey6689Skill

    bdi-mental-states

    Transforms external RDF context into BDI mental states. Invoke when modeling beliefs, desires, and intentions.

    Also in 5 projects that use this

  • Downey6689Skill

    hosted-agents

    Guidance for hosted/sandboxed agents. Invoke when running background agents in isolated VMs or multi-client setups.

    Also in 5 projects that use this

  • DVwebBiz11Skill

    self-improvement-loops

    This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.

    Also in 4 projects that use this

  • mjunaidcaSkill

    building-chatgpt-apps

    Guides creation of ChatGPT Apps with interactive widgets using OpenAI Apps SDK and MCP servers. Use when building ChatGPT custom apps with visual UI components, embedded widgets, or rich interactive experiences. Covers widget architecture, MCP server setup with FastMCP, response metadata, and Developer Mode configuration. NOT when building standard MCP servers without widgets (use building-mcp-servers skill instead).

    Also in 4 projects that use this

  • mjunaidcaSkill

    building-chat-interfaces

    Build AI chat interfaces with custom backends, authentication, and context injection. Use when integrating chat UI with AI agents, adding auth to chat, injecting user/page context, or implementing httpOnly cookie proxies. Covers ChatKitServer, useChatKit, and MCP auth patterns. NOT when building simple chatbots without persistence or custom agent integration.

    Also in 4 projects that use this

  • +7 more from mjunaidca

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 21 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read and follow modelopt-evaluation, resolving its relative references from the canonical modelopt-evaluation/ directory.

    line 6

“Common” is measured: kept by at least 6 in 10 of the 21 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Read and follow modelopt-evaluation, resolving its relative references from the canonical modelopt-evaluation/ directory.

Download now (ZIP)

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