A local library for managing AI-generated images. It lets you browse, rate, organize, analyze, find similar images, and export them through a desktop app or command-line tools.

Use it to import image folders, inspect and rate images, create collections, run image analysis, search for visually similar images, and export selected work.

It gives developers one place to review large image collections without opening files one by one in a separate image viewer.

cull — Skill for Claude Code and Codex by glebis

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 glebis/claude-skills --skill cull

Clone the repo

git clone --depth 1 https://github.com/glebis/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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/glebis/claude-skills/cull/github.svg)](https://agentmods.dev/skills/glebis/claude-skills/cull)

Your own site

<a href="https://agentmods.dev/skills/glebis/claude-skills/cull"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/cull/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/glebis/claude-skills/cull"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/cull.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 123 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,954 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. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Privilege Escalation · line 111
Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.

Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.

How audits are shown

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	$0.00123	$0.01954
Opus 5	$0.00062	$0.00977
Sonnet 5	$0.00025	$0.00391
Haiku 4.5	$0.00012	$0.00195

Measured 10d ago against content hash 2831fedc9fd5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

cull 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 10d 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.

cull/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cull

Cull is a local AI-art image-library app: import folders, browse, rate/decide, build collections, run vision/quality analysis, find similar via embeddings, and export/publish.

Cull exposes the same operations four ways: the cull CLI, the cull:// URL scheme, the GUI, and an MCP server — all thin wrappers over one Rust core. Default to the CLI + URL scheme. They need no MCP connection and survive app restarts. Reach for the MCP only when interactive control is needed that the headless surface doesn't implement yet (see "When the MCP is needed").

The one core rule (do not skip)

To show or review images, use Cull — never open <image> or Preview. The user does not want Preview windows. Display by importing into Cull's library and fronting the app on the folder (below). Fronting the app is fine; opening image files with open is not.

Driving Cull headless (the default path)

The binary lives at /Applications/Cull.app/Contents/MacOS/cull. Set it once:

CULL="/Applications/Cull.app/Contents/MacOS/cull"

With no subcommand it launches the GUI; with a subcommand it runs headless and exits, writing to the same library DB the running app reads. Add --json for machine-readable output.

Show / review a batch — the most common task. Import (headless), then front the app on the folder via the URL scheme:

$CULL --json import_folder --folder_path "/abs/path/to/batch"
open -a /Applications/Cull.app "cull://open?path=/abs/path/to/batch&view=grid"   # &view=loupe for single-image detail
open -a Cull                                            # ensure the window is frontmost

Re-running import_folder is safe — already-imported files are skipped.

Implemented CLI subcommands

These are live in the shipped binary (cull --help to confirm). Field names match the MCP tool params.

Command	Purpose
`import_folder --folder_path P` / `import_files --file_paths a,b`	Bring a folder / specific files into the library
`list_folders` / `list_images [--limit N --offset N]` / `list_collections`	Enumerate folders / images / collections
`get_library_stats`	Library-wide counts
`list_export_presets` / `export_images --image_ids … --output_dir … --format …`	List presets / export to disk (also `--collection_id` or `--folder_path`)
`get_embedding_model_download_info` / `download_embedding_model` / `generate_embeddings`	Embedding model prereq + build (async)
`analyze_image_quality` / `get_image_quality` / `get_quality_count`	Run quality analysis / read scores / count by bucket
`call_tool <tool> --params_json '{…}'`	Generic escape hatch — call any MCP-named tool with a JSON object

Read the full file on GitHub · 112 lines

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.

10d ago First seen · 112 lines · 123 tokens per session scan A 2831fedc9fd5

Subscribe to this mod's changes

cull is a skill published in the GitHub repository glebis/claude-skills (374 stars, last pushed 7d ago), licensed MIT. It adds 123 tokens to every session and 1,954 once invoked, about $0.0006 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 2026-08-30.

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