model-evaluator

model-evaluator is a skill for Claude Code, Codex from inbharatai/claude-skills. It costs 26 tokens per session (406 once invoked), scanned A, original, MIT.

A toolkit for testing machine-learning models, which make predictions from data. It covers cross-validation, confusion matrices, ROC curves, bias checks, and interpretability.

In plain words
What is it for?
Use it to compare model performance, inspect classification errors, audit bias, and explain predictions.
Why use it?
It helps reveal whether a model performs reliably, treats groups fairly, and can be understood beyond a single score.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code.

Good fit Use it to compare model performance, inspect classification errors, audit bias, and explain predictions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/inbharatai/claude-skills/model-evaluator
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add inbharatai/claude-skills --skill model-evaluator
Clone the repo
git clone --depth 1 https://github.com/inbharatai/claude-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for model-evaluator

README.md
[![agentmods](https://agentmods.dev/badge/skills/inbharatai/claude-skills/model-evaluator/github.svg)](https://agentmods.dev/skills/inbharatai/claude-skills/model-evaluator)
Your own site
<a href="https://agentmods.dev/skills/inbharatai/claude-skills/model-evaluator"><img src="https://agentmods.dev/badge/skills/inbharatai/claude-skills/model-evaluator/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for model-evaluator

Your own site · 80×15
<a href="https://agentmods.dev/skills/inbharatai/claude-skills/model-evaluator"><img src="https://agentmods.dev/badge/skills/inbharatai/claude-skills/model-evaluator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 406 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.00406
Opus 5 $0.00013 $0.00203
Sonnet 5 $0.00005 $0.00081
Haiku 4.5 $0.00003 $0.00041

Measured 7d ago against content hash 8245754c58ca, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

model-evaluator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/model-evaluator/SKILL.md · 68 lines

What it actually says

Model Evaluator

Overview

Evaluate ML models rigorously — cross-validation, confusion matrices, ROC curves, bias audits, and interpretability.

When to Use This Skill

Use Model Evaluator when you need to:

  • Work with model evaluator tasks in your project or workflow
  • Automate model evaluator operations at scale
  • Generate production-quality model evaluator output quickly

Instructions

When this skill is active, Claude will:

  1. Understand the full context of your model evaluator request
  2. Apply best practices and conventions for Data & Analytics
  3. Produce clean, well-structured, production-ready output
  4. Explain key decisions and offer alternatives where relevant

Examples

Example 1 — Basic Usage

User: Help me get started with model evaluator.

Claude: I'll walk you through the essential steps for model evaluator in your context...

Example 2 — Advanced Usage

User: I need a production-ready model evaluator setup with full error handling.

Claude: Here's a complete, production-hardened model evaluator implementation...

Guidelines

  • Always validate inputs before processing
  • Follow the conventions of the target platform or language
  • Prefer explicit over implicit — clarity beats cleverness
  • Include comments for non-obvious logic
  • Suggest tests or validation steps where appropriate

Dependencies

Required: python, sklearn, shap

Platforms

Available on: claude.ai, claude-code, api


Part of the claude-skills collection — 183+ skills for Claude.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 68 lines · 26 tokens per session scan A 8245754c58ca

Subscribe to this mod's changes

model-evaluator is a skill published in the GitHub repository inbharatai/claude-skills (33 stars, last pushed 6mo ago), licensed MIT. It adds 26 tokens to every session and 406 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

optimize

Automatically refines every user prompt into a structured, actionable version, then immediately executes the optimized prompt. When triggered explicitly with "/optimize {prompt}", "optimize:", or "optimize prompt:", outputs the refined prompt as text instead.

Hashaam101/prompt-optimizer · 51 tokens

extremerouter-stt

Speech-to-text via ExtremeRouter /v1/audio/transcriptions using OpenAI Whisper / Groq / Gemini / Deepgram / AssemblyAI / NVIDIA / HuggingFace models. Use when the user wants to transcribe audio, convert speech to text, or get subtitles from audio files.

rsalmn/ExtremeRouter · 64 tokens

extremerouter

Entry point for ExtremeRouter — local/remote AI gateway with OpenAI-compatible REST for chat, image, TTS, embeddings, web search, web fetch. Use when the user mentions ExtremeRouter, NINEROUTERURL, or wants AI without writing provider boilerplate. This skill covers setup + indexes capability skills; fetch the relevant…

rsalmn/ExtremeRouter · 84 tokens

AIProductManager

Complete AI-native product management — AI feature strategy, model selection, evaluation frameworks, AI UX design, responsible AI, and building products that use LLMs, CV, and ML as core features.

vignesh2027/Claude-Agentic-Skills2.0-version · 43 tokens

knowledge-graph-builder

Activates KnowledgeGraph — an expert in building, querying, and reasoning over knowledge graphs. Use when you need entity extraction, relationship mapping, ontology design, Neo4j/RDF graph construction, graph-RAG pipelines, or complex multi-hop reasoning over structured knowledge.

vignesh2027/Claude-Agentic-Skills2.0-version · 58 tokens

rag-architect

Activates the RAG-Architect agent for designing and building Retrieval-Augmented Generation systems. Use this skill when you need to build a document Q&A system, design a knowledge base with semantic search, set up vector stores (Chroma, Pinecone, pgvector), implement hybrid retrieval (dense + BM25 sparse), add…

vignesh2027/Claude-Agentic-Skills2.0-version · 95 tokens