U-Claw is a portable AI workspace that places an OpenClaw assistant, its configuration, memory, sessions, and tools on a USB drive. Users set it up on supported computers and carry the workspace between them, configuring a model with their own API key. The catalogue contains skills and instructions related to using or preparing this portable setup.
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 skills add dongsheng123132/u-claw --skill image-compressgit clone --depth 1 https://github.com/dongsheng123132/u-clawWrote 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/dongsheng123132/u-claw/image-compress)<a href="https://agentmods.dev/skills/dongsheng123132/u-claw/image-compress"><img src="https://agentmods.dev/badge/skills/dongsheng123132/u-claw/image-compress/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.
<a href="https://agentmods.dev/skills/dongsheng123132/u-claw/image-compress"><img src="https://agentmods.dev/badge/skills/dongsheng123132/u-claw/image-compress.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.00498 |
| Opus 5 | $0.00017 | $0.00249 |
| Sonnet 5 | $0.00007 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
image-compress 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 13d 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.
What it actually says
图片压缩 / 转换
帮用户压缩图片体积、调整尺寸、转换格式，全部本地处理，照片不外传。
能力概述
- 压缩：降低体积（控制质量/分辨率），适合发邮件、传微信
- 改尺寸：按宽高或百分比缩放
- 转格式：jpg / png / webp 互转（webp 体积最小）
- 批量：处理整个文件夹
操作方式
用 Bash 工具，Python 的 Pillow 库（跨平台、纯本地）：
python -c "import PIL" 2>/dev/null || pip install -q Pillow
# 单张压缩（质量 75，宽度限制 1600px 等比缩放）
python - <<'PY'
from PIL import Image
im = Image.open("input.jpg")
if im.width > 1600:
im = im.resize((1600, int(im.height*1600/im.width)))
im.convert("RGB").save("output.jpg", "JPEG", quality=75, optimize=True)
print("已压缩 -> output.jpg")
PY
# 批量：当前目录所有 jpg/png -> webp（体积更小）
python - <<'PY'
from PIL import Image
import glob, os
for f in glob.glob("*.jpg") + glob.glob("*.png"):
out = os.path.splitext(f)[0] + ".webp"
Image.open(f).save(out, "WEBP", quality=80)
print(f"{f} -> {out}")
PY
使用建议
- 处理前
ls -lh看原图体积，处理后再ls -lh对比，告诉用户压缩了多少 - 默认不覆盖原图（输出新文件名），除非用户明确要求覆盖
- 含透明通道的 PNG 转 JPG 会丢透明，转 webp 可保留——按需选格式
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.
- 13d ago First seen · 51 lines · 34 tokens per session scan A 92ff4e8b367a
image-compress is a skill published in the GitHub repository dongsheng123132/u-claw (1,748 stars, last pushed 5d ago), licensed MIT. It adds 34 tokens to every session and 498 once invoked, about $0.0002 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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