What is model-researcher?

A process for researching a machine-learning model before benchmarking it. It gathers details such as the model type, size, license, hardware needs, suitable GPU providers, and testing approach.

Use it to inspect Hugging Face model cards, check inference availability, estimate VRAM, find similar models, and plan evaluations.

It prevents starting benchmarks without knowing whether a model is available, affordable to run, or being tested appropriately.

How nyosegawa/agentic-bench configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to nyosegawa/agentic-bench. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/nyosegawa/agentic-bench/main/.claude/skills/model-researcher/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/nyosegawa/agentic-bench

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nyosegawa/agentic-bench/model-researcher/github.svg)](https://agentmods.dev/skills/nyosegawa/agentic-bench/model-researcher)

Your own site

<a href="https://agentmods.dev/skills/nyosegawa/agentic-bench/model-researcher"><img src="https://agentmods.dev/badge/skills/nyosegawa/agentic-bench/model-researcher/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nyosegawa/agentic-bench/model-researcher"><img src="https://agentmods.dev/badge/skills/nyosegawa/agentic-bench/model-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,484 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.

Model	Per session	Once invoked
Fable 5.1	$0.00074	$0.01484
Opus 5.5	$0.00030	$0.00594
Sonnet 5.5	$0.00015	$0.00297
Haiku 4.5	$0.00007	$0.00148

Measured 2mo ago against content hash 17d7ce18984d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

model-researcher 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 2mo ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/gpu_estimator.py, scripts/hf_inference_check.py, scripts/hf_model_info.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.claude/skills/model-researcher/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Model Researcher

You are an experienced ML engineer investigating a model before running benchmarks.

Your Goal

Given a model name, produce a structured research summary:

Model identity (type, architecture, parameter count, license)
Hardware requirements (VRAM, recommended GPU)
Recommended provider (cheapest viable option)
Evaluation strategy (what to test and how)

Workflow

Step 1: Gather Model Information

Run the helper script to pull structured metadata:

python .claude/skills/model-researcher/scripts/hf_model_info.py MODEL_ID

To check if the model is available on HF Inference API (serverless):

python .claude/skills/model-researcher/scripts/hf_inference_check.py MODEL_ID

To search for similar or alternative models:

python .claude/skills/model-researcher/scripts/hf_model_search.py --task llm --sort downloads --limit 10
python .claude/skills/model-researcher/scripts/hf_model_search.py --search "qwen" --limit 5

If the script fails or the model is not on HuggingFace:

Search the web for the model's official page, paper, or GitHub repo
Manually gather: architecture, parameter count, input/output modalities, license

Step 2: Estimate VRAM and Cost

Run the estimator with --check-env to filter by available tokens and sort by cost:

python .claude/skills/model-researcher/scripts/gpu_estimator.py \
  --params PARAM_COUNT --quant fp16 --model-type llm --check-env --json

--check-env reads .env and:

Shows which providers have tokens set (✓/✗)
Filters out providers without required tokens
Sorts remaining by cheapest hourly rate
Shows free tier / subscription info

Review the cost estimate and include it in the research summary. Adjust based on:

Model-specific requirements (e.g., diffusion models need extra VRAM for image buffers)
Framework overhead (transformers vs vllm vs diffusers)

Step 3: Determine Model Type and Evaluation Strategy

Classify the model and load the appropriate evaluation guide:

Read the full file on GitHub · 135 lines

Files

What ships with it

16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

2mo ago First seen · 135 lines · 74 tokens per session scan A 17d7ce18984d

Subscribe to this mod's changes

model-researcher is a skill published in the GitHub repository nyosegawa/agentic-bench (5 stars, last pushed 8mo ago), licensed MIT. It adds 74 tokens to every session and 1,484 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.

Data and AI Research

model-researcher next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
model-researchernyosegawa/agentic-bench	Skill	5	8mo ago	MIT	A	74	Claude Code
agent-platform-model-registrygoogle/skills	Skill	22k	yesterday	Apache-2.0	A	60	Claude Code, Codex
foundry-config-setupmicrosoft/agent-framework	Skill	15k	yesterday	MIT	A	65	Claude Code, Codex
google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills	Skill	22k	yesterday	Apache-2.0	A	132	Claude Code, Codex
training-checkwanshuiyin/Auto-claude-code-research-in-sleep	Skill	19k	3d ago	MIT	A	35	Claude Code
nemo-automodel-launcher-configNVIDIA/skills	Skill	3.9k	yesterday	Apache-2.0	A	30	Claude Code
finetuningmicrosoft/skills	Skill	3.2k	yesterday	MIT	A	132	Claude Code

agentic-bench: Skill for Claude Code