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path-tracing路径追踪

Agent Skill

path-tracing 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

865

周安装

35

GitHub Stars

93

下载量

272
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:path-tracing(路径追踪)
来源仓库:https://github.com/letta-ai/skills
仓库路径:skills/path-tracing
安装命令:
npx skills add https://github.com/letta-ai/skills --skill path-tracing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/letta-ai/skills --skill path-tracing

简介

用于查找、检索和筛选相关信息。path-tracing 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 适合在技术溯源或文献追踪任务中定位来源。
  • 通过 GitHub 安装,支持主流 AI 开发环境。
  • 建议在使用前核实来源仓库的维护情况。
  • 注意可能触发的网络请求或外部依赖。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Path Tracing and Ray Tracing Implementation

This skill provides guidance for implementing path tracers and ray tracers, particularly for image reconstruction tasks where a target image must be matched within a similarity threshold.

When to Use This Skill

  • Implementing ray tracers or path tracers in C/C++
  • Reconstructing images by reverse-engineering scene parameters
  • Building rendering systems with geometric primitives (spheres, planes)
  • Tasks requiring image similarity matching (L2 norm, cosine similarity)
  • Rendering scenes with shadows, reflections, or procedural textures

Workflow: Baseline-First Development

Phase 1: Establish a Working Baseline

Before any optimization or parameter tuning, establish a complete working render:

  1. Start with minimal samples - Use S=1 or S=2 to verify the pipeline produces complete output
  2. Verify output file completeness - Check that the output file contains valid, complete image data before proceeding
  3. Test in target environment early - If the task specifies a chroot jail, sandbox, or specific execution environment, test there immediately
  4. Fix all compiler warnings first - Treat warnings as errors; typos like doubled instead of double d cause undefined behavior

Phase 2: Scene Analysis

When reconstructing from a reference image:

  1. Read and parse image header - Extract dimensions, format, color depth
  2. Sample key pixels systematically - Sample corners, center, and regions of interest
  3. Identify scene elements - Count all objects (spheres, planes, lights) before implementing
  4. Analyze gradients and patterns - Sample multiple points to derive mathematical relationships for sky gradients, floor patterns
  5. Document derived parameters - Write down calculated values (camera FOV, sphere position/radius, light direction)

Phase 3: Incremental Implementation

  1. One feature at a time - Add sphere, then floor, then shadows, then soft shadows
  2. Validate each addition - Render and compare after each feature
  3. Calculate render time before choosing sample count - For a 2400×1800 image at 50 samples, estimate: pixels × samples × rays_per_sample × time_per_ray
  4. Never modify code while renders are running - This creates race conditions and confusion about which version produced which output

Phase 4: Validation

  1. Use the exact similarity metric - If grading uses normalized L2 or cosine similarity, compute that metric, not RMS error or other proxies
  2. Create a reusable validation script early - Avoid rewriting comparison code repeatedly
  3. Verify output file before submission - Check file size, parse the image, confirm dimensions match expected

Common Scene Elements

Sky Gradients

Analyze by sampling multiple y-coordinates at a fixed x to derive the vertical gradient formula. Sample multiple x-coordinates at a fixed y to check for horizontal variation.

Checkered Floor

To match checker patterns:

  • Sample points across the floor to determine checker scale
  • Identify the checker color values precisely
  • Verify pattern origin and orientation

Spheres

Derive sphere parameters by:

  • Finding the visual center of the sphere in image coordinates
  • Estimating radius from the sphere's apparent size
  • Calculating reflection and shadow geometry to verify position

Shadows

  • Hard shadows: Single ray to light source
  • Soft shadows: Multiple samples with jittered light directions
  • Verify shadow direction matches light position

Time Management

Calculate Expected Render Time First

render_time ≈ (width × height × samples × bounces) / rays_per_second

Typical ray tracer performance: 100K-1M rays/second depending on scene complexity.

For a 2400×1800 image:

  • S=1: ~4M rays, seconds to complete
  • S=10: ~40M rays, minutes to complete
  • S=50: ~200M rays, could take 10+ minutes
  • S=100: ~400M rays, may exceed time limits

Adjust Parameters for Time Budget

If the task has a time limit:

  1. Calculate maximum feasible sample count
  2. Start with that limit, not above
  3. Consider rendering at lower resolution first for validation

Common Pitfalls to Avoid

Code Quality Issues

  • Typos in type declarations - doubled vs double d causes undefined behavior
  • Ignoring compiler warnings - Fix all warnings before running long processes
  • Race conditions - Never edit code while a render is still running

Validation Mistakes

  • Wrong similarity metric - Match the exact metric used for grading
  • Incomplete output files - Always verify file completeness before considering done
  • Downsampled validation - If final output must be full resolution, validate at full resolution

Time Management Mistakes

  • Starting with high sample counts - Always start low (S=1) to verify correctness
  • Not calculating expected render time - Lead to timeout and wasted iterations
  • Iterating without completing - Better to have one complete low-quality render than many incomplete high-quality attempts

Analysis Mistakes

  • Missing scene elements - Thoroughly identify all objects before implementing
  • Arbitrary parameter guessing - Derive parameters mathematically from reference image samples
  • Sign errors in gradients - Double-check gradient direction by sampling multiple points

Verification Checklist

Before considering the task complete:

  • Code compiles without warnings
  • Output file exists and is complete (correct size, valid format)
  • Output dimensions match expected dimensions
  • Similarity metric meets the required threshold
  • Tested in the target execution environment
  • All scene elements from reference are present in output

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.36%
按下载量换算83

Gemini CLI

23.95%
按下载量换算65

Codex

19.4%
按下载量换算53

Antigravity

11.3%
按下载量换算31

OpenCode

7.97%
按下载量换算22

Cursor

3.11%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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