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 VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single…
MITAuto-check passed
Install Vldb Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-experiments .claude/skills/vldb-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 "vldb-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-experiments into .claude/skills/vldb-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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/VLDB-Skills/skills/vldb-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 vldb-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-experiments .agents/skills/vldb-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 "vldb-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-experiments into .agents/skills/vldb-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 vldb-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-experiments .cursor/skills/vldb-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 "vldb-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-experiments into .cursor/skills/vldb-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 VLDB-Skills/skills/vldb-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 vldb-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-experiments .gemini/skills/vldb-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 "vldb-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-experiments into .gemini/skills/vldb-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 vldb-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 vldb-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/VLDB-Skills/skills/vldb-experiments .github/skills/vldb-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 "vldb-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-experiments into .github/skills/vldb-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 vldb-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 vldb-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/VLDB-Skills/skills/vldb-experiments .opencode/skills/vldb-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 "vldb-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-experiments into .opencode/skills/vldb-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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
vldb-experiments- GitHub stars
- 1.2k
- Token cost
- ~981 tokens
- SKILL.md length
- 401 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 VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single…
- Works in 4 steps: The problem exists — measure the… → The mechanism causes the gain — ablate… → The gain survives scale — curves along… → …
- Auditing the evaluation of a VLDB paper
- SKILL.md covers What the evaluation must…, Workload realism ladder, Baseline fairness protocol and Reporting floor, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Vldb Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.
Its SKILL.md is about 980 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 VLDB paper
- Covering workload and dataset realism at scale
- Competitor tuning fairness
- Scalability curves versus single points
Example prompts
- “/vldb-experiments”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- The problem exists — measure the baseline failing on a credible
- The mechanism causes the gain — ablate your components; a monolithic
- The gain survives scale — curves along data size, cluster size, and
- The cost is known — measure where your design loses and say so.
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
Vldb Experiments loads about 981 tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 401 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). 401 words, ~981 tokens.
Download SKILL.mdSave it as
.claude/skills/vldb-experiments/SKILL.md (or your agent's skills folder).- name
- vldb-experiments
- description
- Use when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.
VLDB Experiments
Use this before the evaluation section hardens. At this venue the experiments are the argument: a PVLDB reviewer typically flips from the introduction straight to the plots and decides how skeptically to read everything else.
What the evaluation must establish
Four distinct burdens, each needing its own experiments:
- The problem exists — measure the baseline failing on a credible workload before showing your fix.
- The mechanism causes the gain — ablate your components; a monolithic "our system vs. theirs" plot proves selection, not mechanism.
- The gain survives scale — curves along data size, cluster size, and concurrency, not one configuration chosen after the fact.
- The cost is known — measure where your design loses and say so.
Workload realism ladder
| Rung | Example | Reviewer credit |
|---|---|---|
| Micro-benchmark | single operator, synthetic keys | Mechanism insight only |
| Standard benchmark | TPC-style, YCSB, JCC-H-class | Comparable, but "benchmark-only" is a known flag |
| Benchmark + skew/drift | standard suite with realistic distributions | Solid |
| Trace or production-derived | replayed real workload | Strongest, disclose provenance |
Climb as high as the data you can legally use allows; state the rung honestly.
Baseline fairness protocol
The people who built your baselines review here. For every competitor:
- Latest stable version (or a justified pin), same hardware, same data.
- Tuning effort comparable to what you gave your own system — document the knobs tried in both cases.
- If a competitor is excluded, one honest sentence why (license, no support for the workload) beats silence.
- Include the strong-but-inconvenient baseline: the hand-tuned config, the single-node engine that wins at small scale, the "just add an index" answer. Reviewers propose these in their first pass; preempt them.
Show full SKILL.md (134 more words)Show less
Reporting floor
- Throughput and latency, with tails (p95/p99) where users feel tails.
- Repetition counts and spread (see
vldb-reproducibilityfor the variance protocol); medians for skewed metrics. - Axes from zero or clearly marked; log scales labeled; error bands defined in the caption.
- Every number in the abstract traceable to a specific figure or table.
Ablation and sweep matrix
For each design component C1..Cn:
full system vs full-minus-Ci (mechanism attribution)
For each claimed robustness dimension D:
sweep D across its realistic range (skew, size, concurrency, selectivity)
Loss map:
identify >=1 region where a baseline wins; measure it; discuss itThe loss map is deliberately mandatory. A paper whose system wins everywhere in every plot triggers reviewer search behavior — they will find the losing region themselves, without your framing.
Scale honesty
Run the largest experiments your infrastructure permits, then scope claims to what was run. "Designed for larger deployments" is acceptable prose; extrapolated curves presented as measurements are not. If cloud credits capped the study, say so — builders on the panel have lived that constraint.
Output format
[Burden coverage] problem-exists / mechanism / scale / cost — evidence per burden
[Workload rung] <ladder position, justification>
[Baseline fairness] <competitor -> version, tuning parity, exclusions>
[Reporting floor] tails, reps, spread — gaps
[Loss map] present (region, magnitude) / missing (risk)
[Next decisive run] <the one experiment that most changes acceptance odds>© 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 VLDB-Skills/skills/vldb-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Vldb 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 |
|---|---|---|---|---|---|---|
| Vldb Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~981 | 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
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Experiment Designer
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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 Vldb Experiments
What does Vldb Experiments do?
A skill your agent uses when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single…. Vldb Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.
When should I use Vldb Experiments?
Vldb Experiments fits situations like: auditing the evaluation of a VLDB paper; covering workload and dataset realism at scale; competitor tuning fairness; scalability curves versus single points.
How do I install Vldb Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-experiments -a claude-code`. Or copy the skill folder (VLDB-Skills/skills/vldb-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/vldb-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Vldb Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-experiments -a codex`. Or copy the skill folder (VLDB-Skills/skills/vldb-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/vldb-experiments in your project. Codex loads it when a task matches its description.
Can I use Vldb 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 vldb-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/vldb-experiments, .gemini/skills/vldb-experiments, .github/skills/vldb-experiments and .opencode/skills/vldb-experiments in your project.
What does Vldb Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Vldb Experiments is instructions for the agent only.
Does Vldb 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 Vldb 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 Vldb Experiments use?
Vldb 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 Vldb Experiments use?
About 981 tokens (SKILL.md is roughly 3.9k 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 Vldb Experiments?
Skills that share tags, products or a category with Vldb 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 Vldb 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.