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 znlgis/opengis-skills --skill pdalgit clone --depth 1 https://github.com/znlgis/opengis-skillsWrote 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/znlgis/opengis-skills/pdal)<a href="https://agentmods.dev/skills/znlgis/opengis-skills/pdal"><img src="https://agentmods.dev/badge/skills/znlgis/opengis-skills/pdal/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/znlgis/opengis-skills/pdal"><img src="https://agentmods.dev/badge/skills/znlgis/opengis-skills/pdal.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00090 | $0.04582 |
| Opus 5.5 | $0.00036 | $0.01833 |
| Sonnet 5.5 | $0.00018 | $0.00916 |
| Haiku 4.5 | $0.00009 | $0.00458 |
Grade B, and why
pdal scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt update How it starts
The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
项目地址: https://github.com/PDAL/PDAL
官方文档: https://pdal.io/
Conda:
pdal/python-pdal(conda-forge,最新 2.10.2,截至 2026-10)PyPI:
pdal(Python 绑定,最新 3.5.5,截至 2026-10;版本号与主库独立)许可证: BSD-3-Clause
概述
PDAL(Point Data Abstraction Library)是处理点云数据的开源库与命令行工具集,常被称作“点云界的 GDAL”。它把点云读取、过滤、写入抽象为统一的 stage 模型,用 JSON pipeline 描述整条处理链。支持 LAS/LAZ、BPF、EPT、COPC、文本等多种格式。既提供 pdal 命令行(info/translate/pipeline 等),也提供 Python、C++ 绑定,常与 GDAL 配合完成地面分类与 DEM 生成。
- Stage 模型:reader → filter → writer 串联,每个 stage 由
type与参数描述 - 流式优先:默认尽量使用 stream mode,大文件不必整体载入
- 格式广泛:内置 LazPerf,LAZ 压缩读写开箱即用
- 生态衔接:writers.gdal 直接输出 GeoTIFF DEM,Python 绑定返回 NumPy 数组
环境准备
安装方法一:apt(Debian/Ubuntu)
sudo apt update
sudo apt install pdal libpdal-dev python3-pdal
安装方法二:conda(推荐,跨平台)
conda install -c conda-forge pdal python-pdal
# 或先建环境再安装
conda create -n pdal -c conda-forge pdal python-pdal
conda activate pdal
安装方法三:Docker
docker pull pdal/pdal:latest
docker run --rm pdal/pdal pdal --version
# 交互式使用,将当前目录挂载到 /data
docker run --rm -it -v "$PWD":/data -w /data pdal/pdal
Python 绑定(pip)
pip install PDAL # 依赖系统已安装 PDAL 库(提供 pdal-config)
验证安装
pdal --version # 输出如 PDAL 2.10.2 (…)
pdal --help # 列出全部子命令(info/translate/pipeline/…)
pdal --drivers # 列出可用的 readers/filters/writers
pdal --options filters.smrf # 查看某个 driver 的参数与默认值
pdal info --help # 查看单个子命令的参数
核心 API/命令
pdal info — 查看点云信息
pdal info input.las # 不带参数时默认等价 --stats(读取全部点统计)
pdal info input.las --summary # 概要(点数、范围、坐标系)
pdal info input.las --metadata # 元数据(含 LAS 头信息)
pdal info input.las --schema # 维度 schema
pdal info input.las --all # stats + schema + metadata
pdal info input.las -p 0-10 # 输出第 0-10 个点(0 起始)
pdal info input.las --query "636601.87, 849018.59, 425.10" # 按到该点的距离排序输出
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.
- 2d ago Changed · +4 lines bfedb5b2362f
- 9d ago Changed · +2 lines 236502dbd4ed
- 14d ago First seen · 349 lines · 90 tokens per session scan B eb4733ad8054
pdal is a skill published in the GitHub repository znlgis/opengis-skills (76 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 4,582 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-16.
Other skills, from other repositories
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
swmm-end-to-end
Top-level orchestration skill for agentic SWMM modelling. Use when an agent needs one entrypoint that decides which module tools to run, in what order, and when to stop, for example to build, run, QA, and optionally calibrate a SWMM case from prepared or partially prepared inputs.
swmm-calibration
Calibration and validation scaffold for EPA SWMM. Use when an agent needs to (1) compare simulated vs observed flow, (2) evaluate candidate parameter sets, (3) rank explicit candidates by an objective, (4) run a bounded random / LHS / adaptive search for the best-fitting parameters, (5) run a publication-grade SCE-UA…
swmm-gis
GIS/DEM preprocessing for SWMM experiments using the user's own QGIS/GRASS layers. Use when the user asks to (1) delineate subcatchments through QGIS/GRASS (standard or entropy-guided), (2) preprocess QGIS-derived subcatchment polygons into builder-ready CSV, (3) identify high-entropy hotspot subcatchments, or (4)…
swmm-uncertainty
Parameter and forcing uncertainty for EPA SWMM. Without observed flow, call propagateparameterranges (global ranges, one SWMM run per sample, peak spread); the Morris/OAT/Sobol tools need an observed series. Use when an agent needs to (1) propagate parameter uncertainty through SWMM (fuzzy alpha-cut or Monte Carlo)…
swmm-anywhere
Synthesize a plausible SWMM drainage network from public data (OSM streets + DEM) when NO real pipe-network data exists — input is just a bbox. Use ONLY when the user has no pipe shapefile/CAD/GIS data, or to establish a baseline before real data arrives; if real pipe data exists, route to swmm-network or swmm-gis…