photon_path_tracer

C++ code quality standards: style, naming, comments, structure, brevity. Use when: writing new C/C++/CUDA code, reviewing style, refactoring for readability, cleaning up comments, or enforcing project conventions. Applies to all .cpp, .h, .cu, .cuh files.

Multi-stage pipeline orchestration using 3 executables (ppt_analyze → photon_tracer → ppt_diagnose). Use when: wiring the analyze→render→diagnose pipeline, setting up preview or production rendering, coordinating skill invocation order, configuring render parameters, or debugging stage-to-stage data flow.

GPU post-processing pipeline inside photon_tracer: firefly filter (median+MAD), bloom (mip-chain Gaussian), tonemap (ACES+sRGB gamma), OptiX denoiser. Use when: fixing firefly artifacts, tuning bloom thresholds, adjusting tonemapping, debugging output corruption, integrating OptiX denoiser, or optimizing post-FX performance.

Image quality analysis, artifact detection, convergence diagnostics, noise decomposition, and parameter corrections. Use when: diagnosing image quality problems, running ppt_diagnose, analyzing convergence, identifying bottlenecks, tuning render parameters via feedback, or comparing A/B renders.

GPU render pipeline internals. Use when: modifying renderer source code, fixing material/intersection/energy bugs, or debugging noise/fireflies in implementation. NOT for simply running the executable (use orchestrator skill).

Scene loading, PBRT/OBJ parsing, material mapping, scene classification, and auto-tuning parameter recommendation. Use when: loading scenes, fixing parsing bugs, adjusting scene classification, tuning emitter detection, modifying SceneProfile, or running ppt_analyze for pre-render analysis.

Interactive scene editing and manipulation. Use when: modifying scenes in the editor, adding/removing objects, adjusting materials interactively, or debugging scene construction.

Convergence regression testing, reference comparison, statistical validation, and cross-stage test orchestration. Use when: writing tests for any pipeline stage, setting up convergence baselines, running furnace/reciprocity/chi-squared tests, validating render quality against reference images, creating mock test scenes, or tracking test coverage across the pipeline.

Token usage and skill cost monitoring. Use when: asking which skill was used, how many tokens a request cost, what the active skills were, token breakdown per turn, session token totals, skill cost estimate, cost of recent request, which SKILL.md files were loaded.