Agent skill

Siggraph Reproducibility

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when building the reproducibility story for a SIGGRAPH / TOG paper, covering deterministic result regeneration, scene/mesh/weight provenance, hardware and timing disclosure…

MITAuto-check passedResearch & Science

Install Siggraph Reproducibility

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-reproducibility --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-reproducibility .claude/skills/siggraph-reproducibility && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "siggraph-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-reproducibility into .claude/skills/siggraph-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-reproducibility", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-reproducibility

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-reproducibility --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-reproducibility .agents/skills/siggraph-reproducibility && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "siggraph-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-reproducibility into .agents/skills/siggraph-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-reproducibility", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-reproducibility --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-reproducibility .cursor/skills/siggraph-reproducibility && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "siggraph-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-reproducibility into .cursor/skills/siggraph-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-reproducibility", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path SIGGRAPH-Skills/skills/siggraph-reproducibility

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-reproducibility --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-reproducibility .gemini/skills/siggraph-reproducibility && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "siggraph-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-reproducibility into .gemini/skills/siggraph-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-reproducibility", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-reproducibility

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-reproducibility .github/skills/siggraph-reproducibility && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "siggraph-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-reproducibility into .github/skills/siggraph-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-reproducibility", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-reproducibility --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-reproducibility .opencode/skills/siggraph-reproducibility && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "siggraph-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-reproducibility into .opencode/skills/siggraph-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-reproducibility", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: siggraph-reproducibility
GitHub stars: 1.2k
Token cost: ~1.3k tokens
SKILL.md length: 512 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when building the reproducibility story for a SIGGRAPH / TOG paper, covering deterministic result regeneration, scene/mesh/weight provenance, hardware and timing disclosure…

Building the reproducibility story for a SIGGRAPH / TOG paper
SKILL.md covers Reproducibility here is…, Pin provenance at creation time, Handle non-determinism honestly and The release a reader can run, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Covering deterministic result regeneration

What it does

Siggraph Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Reproducible research. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜覆盖主流期刊的… The licence is MIT.

When your agent uses it

Building the reproducibility story for a SIGGRAPH / TOG paper
Covering deterministic result regeneration
Scene/mesh/weight provenance
Hardware and timing disclosure

Example prompts

“/siggraph-reproducibility”

Requirements

Docker

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Siggraph Reproducibility loads about 1.3k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 512 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~86
When it runs · the whole SKILL.md, loaded when a task matches: ~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 512 words, ~1,267 tokens.

Download SKILL.mdSave it as .claude/skills/siggraph-reproducibility/SKILL.md (or your agent's skills folder).

name: siggraph-reproducibility
description: 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.

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 resolution.

If you cannot say exactly what a result depends on, you cannot make it reproducible.

Pin provenance at creation time

These cannot be reconstructed after the fact:

Scenes and assets: record the source and version of every scene, mesh, texture, and BRDF; ship them or give a stable download. A method evaluated on unshareable assets is unreproducible by construction — say so and provide a shareable proxy scene.
Seeds and configs: log the seed, sample count, resolution, and every hyperparameter behind each figure. Store the config with the output, not in your memory.
Hardware and software stack: GPU model, driver, CUDA/compiler versions, OS. Graphics timings are meaningless without them.
Model artifacts: for learning-based work, pin the training data snapshot, the released weights' hash, and the inference command.

Show full SKILL.md (225 more words)Show less

Handle non-determinism honestly

Do not claim bit-exact reproduction you cannot deliver:

Declare the tolerance. State whether a result is bit-exact, or matched within a metric (PSNR/SSIM/LPIPS/Hausdorff) and threshold, and bundle the reference output to compare against.
Seed the stochastic path — Monte Carlo integration, stochastic simulation, dropout — and document the residual drift from GPU reductions or non-associative float math.
Separate deterministic and stochastic figures so a reader knows which they can reproduce exactly and which only in distribution.

The release a reader can run

text

[README]      what it is; one command to build; one command to reproduce a headline figure;
              expected runtime and hardware
[Build]       pinned (Docker/conda/CMake) with exact GPU/driver/compiler versions
[Assets]      scenes/meshes/textures/weights bundled or stably linked
[repro/]      a script per headline figure: config in, image/metric/frame out, ref bundled
[MAPPING]     paper figure/table -> script -> expected output + tolerance
[LICENSE]     OSI-approved, so results can be reused and stamped

Reproducibility vs. anonymity

SIGGRAPH Technical Papers review has historically been single-blind (reviewers see authors), so the anonymization tax that ML/SE venues pay at review time is usually lighter here — but confirm the current cycle's blinding policy (待核实 for exact 2026 wording). If a cycle does require anonymized review, strip owner strings, lab names, and identifying URLs from the code and supplemental before upload, and swap in a de-anonymized permanent archive at camera-ready.

Anti-patterns

Reporting a timing with no hardware, or a quality number with no metric and no reference image.
Shipping code without the scenes/meshes/weights it needs — it compiles but reproduces nothing.
Claiming reproduction while leaving the sampler unseeded.
Deferring the whole release to camera-ready, when provenance had to be pinned during the work.
Treating "available upon request" as a release — it is a scored weakness, not a neutral choice.

Output format

text

[Result classes] images / meshes / simulation / learned / timings present
[Provenance] scenes+assets pinned? seeds+configs logged? hardware stack recorded? yes/no
[Determinism] tolerance stated + reference outputs bundled? yes/no
[Release] build + repro scripts + figure->script mapping present? yes/no
[Blinding] cycle policy confirmed (single-blind vs anonymized)? action if anonymized
[Gaps] <ordered, with what must be pinned before it is lost>

Files

Just SKILL.md in SIGGRAPH-Skills/skills/siggraph-reproducibility of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Siggraph Reproducibility next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Siggraph Reproducibility compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Siggraph Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.3k	Automated safety check: Pass	MIT	12 days ago
Peer ReviewK-Dense-AI/claude-scientific-writer	2.4k	2 repos	~3.1k	Automated safety check: Notes	MIT	today
CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw	617	1 repos	~1.8k	Automated safety check: Pass	None	7 mo ago
Compute Environment Setupaipoch/open-science	5.5k	—	~2.6k	Automated safety check: Pass	Apache-2.0	today
Figure Styleaipoch/open-science	5.5k	—	~5.1k	Automated safety check: Pass	Apache-2.0	today
Add Bactopia Toolbactopia/bactopia	522	—	~4.1k	Automated safety check: Pass	MIT	2 mo ago

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2.4k GitHub starsUsed in 2 repos~3.1k tokens
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CHARLS Paper Reproduction Guide
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Questions about Siggraph Reproducibility

What does Siggraph Reproducibility do?

A skill your agent uses when building the reproducibility story for a SIGGRAPH / TOG paper, covering deterministic result regeneration, scene/mesh/weight provenance, hardware and timing disclosure…. Siggraph Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.

When should I use Siggraph Reproducibility?

Siggraph Reproducibility fits situations like: building the reproducibility story for a SIGGRAPH / TOG paper; covering deterministic result regeneration; scene/mesh/weight provenance; hardware and timing disclosure.

How do I install Siggraph Reproducibility in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a claude-code`. Or copy the skill folder (SIGGRAPH-Skills/skills/siggraph-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/siggraph-reproducibility in your project. Claude Code loads it when a task matches its description.

How do I install Siggraph Reproducibility in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a codex`. Or copy the skill folder (SIGGRAPH-Skills/skills/siggraph-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/siggraph-reproducibility in your project. Codex loads it when a task matches its description.

Can I use Siggraph Reproducibility in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-reproducibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/siggraph-reproducibility, .gemini/skills/siggraph-reproducibility, .github/skills/siggraph-reproducibility and .opencode/skills/siggraph-reproducibility in your project.

What does Siggraph Reproducibility need to run?

SKILL.md names no scripts, command-line tools or credentials: Siggraph Reproducibility is instructions for the agent only. Our summary lists: Docker.

Does Siggraph Reproducibility access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Siggraph Reproducibility safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Siggraph Reproducibility use?

Siggraph Reproducibility is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Siggraph Reproducibility use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Siggraph Reproducibility?

Skills that share tags, products or a category with Siggraph Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Siggraph Reproducibility?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Siggraph Reproducibility

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

SIGGRAPH Reproducibility

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

Files

Compare with similar skills

Similar skills

More from brycewang-stanford/Awesome-Journal-Skills

Related

Questions about Siggraph Reproducibility