Design Audit Against Rams' Principles
thedotmack/claude-mem
Audits a design against Dieter Rams' ten principles of good design, scores each with evidence, and hands off a make-plan prompt for a new, refined or redesigned outcome.
A skill your agent uses when designing or auditing the evaluation of a SIGGRAPH / TOG paper, covering head-to-head comparisons against the strongest prior method, ablations, performance/timing…
MITAuto-check passed
Install Siggraph Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-experiments --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-experiments .claude/skills/siggraph-experiments && rm -rf skills-srcUse ~/.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/
Install the "siggraph-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-experiments into .claude/skills/siggraph-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-experiments", 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 install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-experimentsType 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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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-experiments .agents/skills/siggraph-experiments && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "siggraph-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-experiments into .agents/skills/siggraph-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-experiments", 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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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-experiments .cursor/skills/siggraph-experiments && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "siggraph-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-experiments into .cursor/skills/siggraph-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-experiments", 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 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path SIGGRAPH-Skills/skills/siggraph-experiments--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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-experiments .gemini/skills/siggraph-experiments && rm -rf skills-srcUse ~/.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/
Install the "siggraph-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-experiments into .gemini/skills/siggraph-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-experiments", 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.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-experimentsInstalls 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).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ 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-experiments .github/skills/siggraph-experiments && rm -rf skills-srcUse ~/.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/
Install the "siggraph-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-experiments into .github/skills/siggraph-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-experiments", 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.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ 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-experiments .opencode/skills/siggraph-experiments && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "siggraph-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-experiments into .opencode/skills/siggraph-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-experiments", 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-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 587 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing the evaluation of a SIGGRAPH / TOG paper, covering head-to-head comparisons against the strongest prior method, ablations, performance/timing…
- Auditing the evaluation of a SIGGRAPH / TOG paper
- SKILL.md covers Match evidence to the claim, The comparison is the evaluation, Metrics, honestly and Performance and timing are…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering head-to-head comparisons against the strongest prior method
What it does
Siggraph Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a SIGGRAPH / TOG paper, covering head-to-head comparisons against the strongest prior method, ablations, performance/timing reporting with hardware, image/geometry quality metrics, perceptual and user studies, and matching the evidence to the graphics claim shape.
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.
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
- Auditing the evaluation of a SIGGRAPH / TOG paper
- Covering head-to-head comparisons against the strongest prior method
- Performance/timing reporting with hardware
- Image/geometry quality metrics
Example prompts
- “/siggraph-experiments”
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 Experiments loads about 1.3k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 587 words of instructions outside code blocks.
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). 587 words, ~1,306 tokens.
Download SKILL.mdSave it as
.claude/skills/siggraph-experiments/SKILL.md (or your agent's skills folder).- name
- siggraph-experiments
- description
- Use when designing or auditing the evaluation of a SIGGRAPH / TOG paper, covering head-to-head comparisons against the strongest prior method, ablations, performance/timing reporting with hardware, image/geometry quality metrics, perceptual and user studies, and matching the evidence to the graphics claim shape.
SIGGRAPH Experiments
SIGGRAPH acceptance turns on evidence proportional to a graphics claim: a technique that claims
to be faster must be timed against a real baseline on stated hardware; one that claims higher
quality must be compared, quantitatively and visually, against the strongest prior method. This
skill matches evaluation to claim shape and pre-empts the domain-expert reviewer's first
objections. Anchor policy to resources/official-source-map.md.
Match evidence to the claim
| Claim shape | Evidence the reviewer expects | Common failure |
|---|---|---|
| "Higher quality" | Head-to-head vs SOTA with a metric (PSNR/SSIM/LPIPS/FLIP; Hausdorff/normal error for geometry) + side-by-side visuals + video | Only one's own results shown; no baseline |
| "Faster / real-time" | Wall-clock vs baseline at equal quality, with GPU/CPU, driver, resolution | Timing at unequal quality; no hardware stated |
| "More general / robust" | Results across a broad, non-cherry-picked scene set incl. hard cases | Works only on the paper's three easy inputs |
| "New capability" | Demonstrations prior methods provably cannot produce | Capability asserted, not shown against a method that fails |
| "Perceptually better" | A user/perceptual study with enough participants and a valid protocol | "Looks better" with no study |
The comparison is the evaluation
In graphics, the head-to-head comparison against the strongest prior method is not optional:
- Reproduce baselines faithfully. Use authors' code and recommended settings; if you must reimplement, say so and match their reported numbers where possible. A weakened baseline is the objection that sinks the paper.
- Equalize conditions. Same scene, viewpoint, lighting, sample/time budget. When you give yourself or the baseline an advantage, disclose it.
- Show the comparison both ways — a metric table and a visual side-by-side (still + video); numbers and pixels persuade different reviewers.
- Include the cases where you lose. Bounding your method's regime is credibility, not weakness.
Metrics, honestly
- Images: PSNR/SSIM for fidelity, LPIPS/FLIP for perceptual difference; state the reference and the region of interest. No single metric is sufficient — report several and show the images.
- Geometry: Hausdorff / mean surface distance, normal/curvature error, element quality; state the alignment and units.
- Simulation/animation: energy/momentum behavior, stability under time-step, constraint residuals; a plot over time, not a single frame.
- Report variance where results are stochastic (multiple seeds/runs), and never compare at unequal sample counts or resolutions without saying so.
Show full SKILL.md (232 more words)Show less
Performance and timing are first-class
Timings are claims a reviewer will check:
- Report hardware (GPU/CPU model, memory, driver), resolution/scene size, and settings for every timing.
- Break down where time goes (preprocess vs per-frame vs per-sample) so the claim is auditable.
- Compare speed at matched quality — "faster" at lower quality is not faster.
Perceptual and user studies
When the claim is about perceived quality or usability:
- Pre-register the protocol; report participant count, task, stimuli, and the statistic (with a correction for multiple comparisons where relevant).
- Use a valid design (two-alternative forced choice, ranking, or a calibrated scale); report effect size and confidence intervals, not just significance.
- Put stimuli and raw responses in the supplemental for reproducibility.
Ablations isolate the contribution
- Turn off each component in turn and show the quality/speed cost — this proves the contribution is the part you claim, not an incidental engineering detail.
- For learning-based methods, ablate architecture, loss terms, and data; run a contamination check so test scenes are not in training.
- Key ablation rows go in the body; the full grid goes to the supplemental (see
siggraph-supplementary).
Anti-patterns
- No comparison to the obvious strongest baseline.
- Timings with no hardware, or "faster" at unequal quality.
- A single cherry-picked scene standing in for generality.
- One metric asserted as quality with no images shown.
- A perceptual claim with no study, or a study with too few participants to support it.
Output format
[Claim -> evidence] each claim matched to comparison/metric/timing/study? yes/no
[Baselines] strongest prior method compared, faithfully, at equal conditions? yes/no
[Metrics] appropriate metrics + visuals/video for each quality claim? yes/no
[Timing] hardware/resolution/settings reported, matched-quality? yes/no
[Ablations] each component isolated; contamination checked (if learned)? yes/no
[Gaps] <ordered, with the reviewer objection each closes>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in SIGGRAPH-Skills/skills/siggraph-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Siggraph Experiments 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Siggraph Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Design Audit Against Rams' Principlesthedotmack/claude-mem | 98k | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Experiment Auditwanshuiyin/Auto-claude-code-research-in-sleep | 17k | 1 repos | ~2.7k | Automated safety check: Notes | MIT | |
| Experiment Auditwanshuiyin/Auto-claude-code-research-in-sleep | 17k | — | ~3.2k | Automated safety check: Notes | MIT | |
| Experiment Designeralirezarezvani/claude-skills | 28k | 1 repos | ~783 | Automated safety check: Pass | MIT | |
| OpenClaw Design Auditopenclaw/clawhub | 9.5k | — | ~498 | Automated safety check: Pass | MIT |
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Experiment Audit
wanshuiyin/Auto-claude-code-research-in-sleep
Audit experiment integrity before claiming results. An agent skill from wanshuiyin/Auto-claude-code-research-in-sleep.
Experiment Audit
wanshuiyin/Auto-claude-code-research-in-sleep
Audit experiment integrity before claiming results. An agent skill from wanshuiyin/Auto-claude-code-research-in-sleep.
Experiment Designer
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A skill your agent uses when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
OpenClaw Design Audit
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Audits OpenClaw frontend code and rendered pages for token misuse, reimplemented primitives, accessibility and responsive defects and off-brand copy, with an evidence-based report.
Design System
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Questions about Siggraph Experiments
What does Siggraph Experiments do?
A skill your agent uses when designing or auditing the evaluation of a SIGGRAPH / TOG paper, covering head-to-head comparisons against the strongest prior method, ablations, performance/timing…. Siggraph Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a SIGGRAPH / TOG paper, covering head-to-head comparisons against the strongest prior method, ablations, performance/timing reporting with hardware, image/geometry quality metrics, perceptual and user studies, and matching the evidence to the graphics claim shape.
When should I use Siggraph Experiments?
Siggraph Experiments fits situations like: auditing the evaluation of a SIGGRAPH / TOG paper; covering head-to-head comparisons against the strongest prior method; performance/timing reporting with hardware; image/geometry quality metrics.
How do I install Siggraph Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a claude-code`. Or copy the skill folder (SIGGRAPH-Skills/skills/siggraph-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/siggraph-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Siggraph Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-experiments -a codex`. Or copy the skill folder (SIGGRAPH-Skills/skills/siggraph-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/siggraph-experiments in your project. Codex loads it when a task matches its description.
Can I use Siggraph Experiments 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-experiments -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-experiments, .gemini/skills/siggraph-experiments, .github/skills/siggraph-experiments and .opencode/skills/siggraph-experiments in your project.
What does Siggraph Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Siggraph Experiments is instructions for the agent only.
Does Siggraph Experiments 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 Experiments 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 Experiments use?
Siggraph Experiments 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 Experiments use?
About 1.3k tokens (SKILL.md is roughly 5.2k 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 Experiments?
Skills that share tags, products or a category with Siggraph Experiments: Design Audit Against Rams' Principles (thedotmack/claude-mem, 98k stars), Experiment Audit (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars), Experiment Audit (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars) and Experiment Designer (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Siggraph Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.