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 a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers…
MITAuto-check passed
Install Cav Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-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/CAV-Skills/skills/cav-experiments .claude/skills/cav-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 "cav-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-experiments into .claude/skills/cav-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-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/CAV-Skills/skills/cav-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 cav-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-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/CAV-Skills/skills/cav-experiments .agents/skills/cav-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 "cav-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-experiments into .agents/skills/cav-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-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 cav-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-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/CAV-Skills/skills/cav-experiments .cursor/skills/cav-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 "cav-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-experiments into .cursor/skills/cav-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-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 CAV-Skills/skills/cav-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 cav-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-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/CAV-Skills/skills/cav-experiments .gemini/skills/cav-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 "cav-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-experiments into .gemini/skills/cav-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-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 cav-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 cav-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/CAV-Skills/skills/cav-experiments .github/skills/cav-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 "cav-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-experiments into .github/skills/cav-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-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 cav-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 cav-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/CAV-Skills/skills/cav-experiments .opencode/skills/cav-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 "cav-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-experiments into .opencode/skills/cav-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-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
cav-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 526 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 a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers…
- Auditing a CAV (Computer Aided Verification) empirical evaluation
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, Fair-comparison checklist (the… and Reporting floor, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP)
What it does
Cav Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers with pinned versions and equal resource limits, timeout-dominated comparisons, soundness cross-checks and proof witnesses, cactus/scatter reporting, and matching evidence to the shape of each verification claim.
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 a CAV (Computer Aided Verification) empirical evaluation
- Covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP)
- Fair baseline solvers with pinned versions and equal resource limits
- Timeout-dominated comparisons
Example prompts
- “/cav-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
Cav Experiments loads about 1.3k tokens when it runs. Until then it costs about 102 tokens; SKILL.md has 526 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). 526 words, ~1,343 tokens.
Download SKILL.mdSave it as
.claude/skills/cav-experiments/SKILL.md (or your agent's skills folder).- name
- cav-experiments
- description
- Use when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers with pinned versions and equal resource limits, timeout-dominated comparisons, soundness cross-checks and proof witnesses, cactus/scatter reporting, and matching evidence to the shape of each verification claim.
CAV Experiments
Use this before submission when the evaluation is not yet locked. CAV reviewers are verification researchers; the empirical section is where a good technique is won or lost. The organizing principle is evidence proportional to the claim — the evaluation must test what the paper asserts, on benchmarks and baselines a skeptic accepts, under a resource budget that makes the comparison fair.
Evaluation audit
- Match evidence to the claim shape. A soundness claim needs a proof and/or a checkable witness, not a benchmark score. A performance claim needs a fair comparison on standard benchmarks under equal limits. A capability claim ("solves instances prior tools cannot") needs those instances and the prior tools actually run.
- Use standard benchmark sets at a pinned revision (SV-COMP, SMT-COMP, HWMCC, VNN-COMP, or a documented domain set). State the subset you ran and why; a hand-picked set invites the "cherry-picked" reject.
- Choose fair baselines: the strongest relevant prior tool(s), at their latest released version, run with a documented, equal resource limit (per-instance time and memory) on the same hardware. An outdated or mis-configured baseline is a scored weakness.
- Respect that verification comparisons are timeout-dominated. Report the limit explicitly; distinguish solved / unsolved / timeout / memout / error; and prefer cactus plots (instances solved vs. time) and scatter plots (per-instance head-to-head) over a single mean that a few timeouts distort.
- Cross-check soundness. Run a differential check against a trusted tool on all verdicts and report disagreements (ideally none); ship proof witnesses where correctness is the claim.
- Design limits in, not on: know before you run which logics, property classes, or sizes the method will not cover, and report them honestly.
Show full SKILL.md (255 more words)Show less
Claim-to-evidence design table
| Verification claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Procedure is sound/complete" | Theorem + proof; witness + independent checker | "Soundness asserted, never checked" |
| "Faster than prior tool" | Standard set (pinned), latest baseline, equal limits, cactus+scatter | "Untuned/old baseline; unequal limits" |
| "Solves instances others cannot" | Those instances run on both tools, at the stated limit | "Only our tool was run on them" |
| "Scales to large systems" | Runtime/memory across realistic sizes; largest instance stated | "Only small inputs; 'large' undefined" |
| "General across a theory/domain" | A diverse benchmark sample + explicit out-of-scope classes | "One family, claimed universal" |
Fair-comparison checklist (the reviewer's first objections)
[Baseline] latest released version? documented configuration? cited correctly?
[Limits] identical per-instance time and memory for all tools? stated hardware and cores?
[Set] standard benchmark set at a pinned revision? subset justified? instance list archived?
[Accounting] solved/unsolved/timeout/memout/error reported separately, not merged into one number?
[Soundness] differential check across tools? disagreements reported? witnesses for UNSAT/verified?
[Variance] for randomized/portfolio runs: seeds fixed, repetitions and variance reported?Reporting floor
- Report the resource limit with every runtime claim; a speed number without a timeout is meaningless.
- Use cactus and/or scatter plots for multi-instance comparisons; report how many instances each tool uniquely solved.
- State the hardware, core count, and number of runs; note any tool that errored or was excluded and why.
Vignette: evaluating a model-checking technique
Suppose the paper claims a new abstraction lets a model checker verify properties prior tools time out on. The matching plan: draw instances from a standard set at a pinned revision; run the new tool and the strongest prior checker(s), at their latest versions, under one uniform time/memory limit on stated hardware; report a cactus plot and a scatter plot, with solved/timeout/error broken out and the uniquely-solved instances named; differential-check verdicts against a trusted checker; and state the property classes and system sizes out of scope as declared limits — every number traceable to a logged run in the artifact.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: benchmark set(revision)/baseline+version/limits, or proof+witness>
[Baseline fairness] <baseline -> latest version? equal limits? documented config?>
[Soundness check] <differential check + witnesses present? yes/no>
[Reporting] <cactus/scatter? solved/timeout/error separated? hardware+seeds stated?>
[Limits-by-design] <logic/property-class/size out of scope -> stated?>
[Decision-critical next run] <one experiment to add or fix>© 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 CAV-Skills/skills/cav-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cav 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 |
|---|---|---|---|---|---|---|
| Cav 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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Questions about Cav Experiments
What does Cav Experiments do?
A skill your agent uses when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers…. Cav Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers with pinned versions and equal resource limits, timeout-dominated comparisons, soundness cross-checks and proof witnesses, cactus/scatter reporting, and matching evidence to the shape of each verification claim.
When should I use Cav Experiments?
Cav Experiments fits situations like: auditing a CAV (Computer Aided Verification) empirical evaluation; covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP); fair baseline solvers with pinned versions and equal resource limits; timeout-dominated comparisons.
How do I install Cav Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-experiments -a claude-code`. Or copy the skill folder (CAV-Skills/skills/cav-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cav-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Cav Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-experiments -a codex`. Or copy the skill folder (CAV-Skills/skills/cav-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cav-experiments in your project. Codex loads it when a task matches its description.
Can I use Cav 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 cav-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/cav-experiments, .gemini/skills/cav-experiments, .github/skills/cav-experiments and .opencode/skills/cav-experiments in your project.
What does Cav Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Cav Experiments is instructions for the agent only.
Does Cav 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 Cav 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 Cav Experiments use?
Cav 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 Cav Experiments use?
About 1.3k tokens (SKILL.md is roughly 5.4k 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 Cav Experiments?
Skills that share tags, products or a category with Cav 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 Cav 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.