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 WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track…
MITAuto-check passed
Install Wacv Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-experiments .claude/skills/wacv-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 "wacv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-experiments into .claude/skills/wacv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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/WACV-Skills/skills/wacv-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 wacv-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-experiments .agents/skills/wacv-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 "wacv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-experiments into .agents/skills/wacv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-experiments .cursor/skills/wacv-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 "wacv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-experiments into .cursor/skills/wacv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 WACV-Skills/skills/wacv-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 wacv-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-experiments .gemini/skills/wacv-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 "wacv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-experiments into .gemini/skills/wacv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-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 wacv-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/WACV-Skills/skills/wacv-experiments .github/skills/wacv-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 "wacv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-experiments into .github/skills/wacv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-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 wacv-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/WACV-Skills/skills/wacv-experiments .opencode/skills/wacv-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 "wacv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-experiments into .opencode/skills/wacv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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
wacv-experiments- GitHub stars
- 1.2k
- Token cost
- ~1k tokens
- SKILL.md length
- 402 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 WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track…
- Auditing WACV experiments
- SKILL.md covers Evidence by track, The four implicit questions, Comparative assessment under… and Uncertainty and honesty, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering Applications-track systems evidence (latency
What it does
Wacv Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track matched-baseline novelty, comparative assessment under the deployed condition, uncertainty over seeds and sessions, ablations, and evidence that survives the two-round review.
Its SKILL.md is about 1k 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 WACV experiments
- Covering Applications-track systems evidence (latency
- Robustness under real constraints) versus Algorithms-track matched-baseline novelty
- Comparative assessment under the deployed condition
Example prompts
- “/wacv-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
Wacv Experiments loads about 1k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 402 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). 402 words, ~1,032 tokens.
Download SKILL.mdSave it as
.claude/skills/wacv-experiments/SKILL.md (or your agent's skills folder).- name
- wacv-experiments
- description
- Use when designing or auditing WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track matched-baseline novelty, comparative assessment under the deployed condition, uncertainty over seeds and sessions, ablations, and evidence that survives the two-round review.
WACV Experiments
Use this to build evidence a WACV reviewer accepts in Round 1 instead of sending to Revise and Resubmit. The controlling idea: WACV reviews under two tracks, and each track has a different evidence bar. Facts are the WACV 2026/2027 cycles as read on 2026-07-09.
Evidence by track
| Question the reviewer asks | Applications track | Algorithms track |
|---|---|---|
| Does it work where it must? | Metric under the deployment constraint (power/latency/light/data budget) | Metric on the standard benchmark |
| Is the comparison fair? | Baselines re-tuned to the same constraint, not their defaults | Baselines under matched backbone/compute |
| What does it cost? | Measured latency, wattage, memory on the named device | FLOPs/params/throughput reported honestly |
| When does it fail? | Failure cases under the real condition | Ablation isolating the mechanism |
| Is the gain real? | Uncertainty over sessions and seeds | Uncertainty over seeds; significance where small |
The single most common WACV revision request is "you compared against baselines at their defaults, not under your constraint." Pre-empt it: an Applications claim is only supported if the baselines were given the same power/data/latency budget you operated under.
The four implicit questions
Design the experiment section to answer, in order: does it work, why, when does it fail, and what does it cost. For an applications paper the "what does it cost" row is load-bearing — a system that hits accuracy but blows the power budget has not made the contribution it claims.
Comparative assessment under the deployed condition
For each baseline B and each headline claim C under constraint K:
1. Re-tune / re-run B under K (same budget, same data regime) — not B's paper defaults.
2. Report your method and B on the same axis (e.g., boundary-F1 @ K watts).
3. Include an unconstrained upper-bound reference so the reader sees the cost of K.
4. Report each number with a spread (std over seeds and, for field/systems work, sessions).
If any baseline was run only at its default and not under K, the comparison is not yet fair.Show full SKILL.md (166 more words)Show less
Uncertainty and honesty
- Report variance: standard deviations over seeds, and for deployed/field systems over repeated sessions or devices, not a single run.
- Do not overclaim when a gap is within the spread; say "within a small margin" and show the numbers. WACV reviewers, being applications-minded, distrust hero runs.
- Keep the exact split, device, meter, and hyperparameters in the supplement so a Round 2 reviewer can confirm nothing changed silently between rounds.
Ablations that isolate the claim
An ablation should remove exactly the component your contribution rests on and show the metric move under the same condition. For an Algorithms-track paper this is the core of novelty; for an Applications-track paper, ablate the part that makes the system meet the constraint (the low-power front end, the data-efficient step), not a generic layer.
Reverify each cycle
- The two-track review criteria and any track-specific evidence expectations.
- Whether specific reporting (compute, energy) is requested in the current guidelines.
- Dataset licensing/anonymity rules for any data released with the paper.
Output format
[Track] Applications / Algorithms
[Works] headline metric under the required condition: <value ± spread>
[Fair] baselines run under the same constraint: yes/no
[Cost] latency/power/FLOPs on named device: <reported?>
[Fails] failure analysis under the real condition: <present?>
[Uncertainty] spread over seeds/sessions: yes/no
[R&R risk] <the comparison a reviewer would call unfair>© 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 WACV-Skills/skills/wacv-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Wacv 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 |
|---|---|---|---|---|---|---|
| Wacv Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT | |
| Design Audit Against Rams' Principlesthedotmack/claude-mem | 99k | — | ~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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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.
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Questions about Wacv Experiments
What does Wacv Experiments do?
A skill your agent uses when designing or auditing WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track…. Wacv Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing WACV experiments, covering Applications-track systems evidence (latency, power, robustness under real constraints) versus Algorithms-track matched-baseline novelty, comparative assessment under the deployed condition, uncertainty over seeds and sessions, ablations, and evidence that survives the two-round review.
When should I use Wacv Experiments?
Wacv Experiments fits situations like: auditing WACV experiments; covering Applications-track systems evidence (latency; robustness under real constraints) versus Algorithms-track matched-baseline novelty; comparative assessment under the deployed condition.
How do I install Wacv Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-experiments -a claude-code`. Or copy the skill folder (WACV-Skills/skills/wacv-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/wacv-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Wacv Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-experiments -a codex`. Or copy the skill folder (WACV-Skills/skills/wacv-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/wacv-experiments in your project. Codex loads it when a task matches its description.
Can I use Wacv 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 wacv-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/wacv-experiments, .gemini/skills/wacv-experiments, .github/skills/wacv-experiments and .opencode/skills/wacv-experiments in your project.
What does Wacv Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Wacv Experiments is instructions for the agent only.
Does Wacv 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 Wacv 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 Wacv Experiments use?
Wacv 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 Wacv Experiments use?
About 1k tokens (SKILL.md is roughly 4.1k 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 Wacv Experiments?
Skills that share tags, products or a category with Wacv Experiments: Design Audit Against Rams' Principles (thedotmack/claude-mem, 99k 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 Wacv Experiments?
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.