siggraph-reproducibility
Use when building the reproducibility story for a SIGGRAPH / TOG paper, covering deterministic result regeneration, scene/mesh/weight provenance, hardware and timing disclosure, floating-point and GPU non-determinism, and a code/data release that a reader or a Graphics Replicability Stamp volunteer can actually run.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SIGGRAPH Reproducibility In computer graphics, reproducibility means a reader can **regenerate your figures and timings**, not merely re-derive your equations. SIGGRAPH's culture rewards this heavily — the community runs its own replicability stamps (see `siggraph-artifact-evaluation`) — but the review itself is decided on the paper and its supplemental video, so reproducibility is something you *build into* the work from the start, not bolt on at camera-ready. Anchor policy to `resources/official-source-map.md`. ## Reproducibility here is result-reproducibility A graphics result is an image, a mesh, a frame sequence, or a timing on specific hardware. Each class has its own failure mode: - **Rendered images** depend on scene assets, sampler seeds, and the renderer's floating-point path — two "correct" runs can differ by pixels. - **Geometry/mesh outputs** depend on the exact input mesh and its scale/orientation conventions. - **Simulations** depend on time-step, solver tolerances, and RNG seeding. - **Learning-based results** depend on released weights and inference data, not just source code. - **Timings** — a first-class SIGGRAPH claim — depend on GPU/CPU, driver, and resolutio
- Reproducibility here is result-reproducibility
- Pin provenance at creation time
- Handle non-determinism honestly
- The release a reader can run
- Reproducibility vs. anonymity
- Anti-patterns
- Output format
What does the siggraph-reproducibility skill do?
Use when building the reproducibility story for a SIGGRAPH / TOG paper, covering deterministic result regeneration, scene/mesh/weight provenance, hardware and timing disclosure, floating-point and GPU non-determinism, and a code/data release that a reader or a Graphics Replicability Stamp volunteer can actually run.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.