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.
npx agentmods add skills/graniet/kheish/segment-anythingnpx skills add graniet/kheish --skill segment-anythinggit clone --depth 1 https://github.com/graniet/kheishWrote 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.
[](https://agentmods.dev/skills/graniet/kheish/segment-anything)<a href="https://agentmods.dev/skills/graniet/kheish/segment-anything"><img src="https://agentmods.dev/badge/skills/graniet/kheish/segment-anything.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00040 | $0.03529 |
| Opus 5 | $0.00020 | $0.01765 |
| Sonnet 5 | $0.00008 | $0.00706 |
| Haiku 4.5 | $0.00004 | $0.00353 |
Grade A, and why
segment-anything scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth This is a copy
80% identical to segment-anything-model — 48 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 521 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Segment Anything Model (SAM)
Comprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation.
When to use SAM
Use SAM when:
- Need to segment any object in images without task-specific training
- Building interactive annotation tools with point/box prompts
- Generating training data for other vision models
- Need zero-shot transfer to new image domains
- Building object detection/segmentation pipelines
- Processing medical, satellite, or domain-specific images
Key features:
- Zero-shot segmentation: Works on any image domain without fine-tuning
- Flexible prompts: Points, bounding boxes, or previous masks
- Automatic segmentation: Generate all object masks automatically
- High quality: Trained on 1.1 billion masks from 11 million images
- Multiple model sizes: ViT-B (fastest), ViT-L, ViT-H (most accurate)
- ONNX export: Deploy in browsers and edge devices
Use alternatives instead:
- YOLO/Detectron2: For real-time object detection with classes
- Mask2Former: For semantic/panoptic segmentation with categories
- GroundingDINO + SAM: For text-prompted segmentation
- SAM 2: For video segmentation tasks
Quick start
Installation
# From GitHub
pip install git+https://github.com/facebookresearch/segment-anything.git
# Optional dependencies
pip install opencv-python pycocotools matplotlib
# Or use HuggingFace transformers
pip install transformers
What ships with it
2 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.
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.
- 6d ago First seen · 521 lines · 40 tokens per session scan A f0fc4bf9d0cb
segment-anything is a skill published in the GitHub repository graniet/kheish (227 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 3,529 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 80% identical to segment-anything-model, differing in 48 lines, and is treated as a copy.
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