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 SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency…
MITAuto-check passed
Install Sigmod Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-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/SIGMOD-Skills/skills/sigmod-experiments .claude/skills/sigmod-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 "sigmod-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-experiments into .claude/skills/sigmod-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-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/SIGMOD-Skills/skills/sigmod-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 sigmod-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-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/SIGMOD-Skills/skills/sigmod-experiments .agents/skills/sigmod-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 "sigmod-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-experiments into .agents/skills/sigmod-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-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 sigmod-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-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/SIGMOD-Skills/skills/sigmod-experiments .cursor/skills/sigmod-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 "sigmod-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-experiments into .cursor/skills/sigmod-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-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 SIGMOD-Skills/skills/sigmod-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 sigmod-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmod-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/SIGMOD-Skills/skills/sigmod-experiments .gemini/skills/sigmod-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 "sigmod-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-experiments into .gemini/skills/sigmod-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-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 sigmod-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 sigmod-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/SIGMOD-Skills/skills/sigmod-experiments .github/skills/sigmod-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 "sigmod-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-experiments into .github/skills/sigmod-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-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 sigmod-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 sigmod-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/SIGMOD-Skills/skills/sigmod-experiments .opencode/skills/sigmod-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 "sigmod-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMOD-Skills/skills/sigmod-experiments into .opencode/skills/sigmod-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmod-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
sigmod-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 585 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 SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency…
- Auditing the evaluation of a SIGMOD paper
- SKILL.md covers The setup table comes first, Workload realism ladder, Baseline fairness protocol and Curves, not points, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering workload realism and standard benchmark usage
What it does
Sigmod Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency methodology, ablations that isolate the mechanism, and the setup disclosure a data-systems PC demands before trusting any speedup.
Its SKILL.md is about 1.4k 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 SIGMOD paper
- Covering workload realism and standard benchmark usage
- Baseline tuning fairness
- Scalability and tail-latency methodology
Example prompts
- “/sigmod-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
Sigmod Experiments loads about 1.4k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 585 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). 585 words, ~1,379 tokens.
Download SKILL.mdSave it as
.claude/skills/sigmod-experiments/SKILL.md (or your agent's skills folder).- name
- sigmod-experiments
- description
- Use when designing or auditing the evaluation of a SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency methodology, ablations that isolate the mechanism, and the setup disclosure a data-systems PC demands before trusting any speedup.
SIGMOD Experiments
The evaluation section decides most SIGMOD verdicts. A data-systems PC does not ask whether your system is fast; it asks whether the experiments would convince the person who built the baseline you beat. Design the evaluation to survive that specific reader.
The setup table comes first
Before any result, the paper owes a complete experimental contract: hardware (CPU, memory, storage class, network), software versions, datasets with scale, workloads with skew and mix parameters, baseline versions and tuning provenance, repetition counts, and warm-up policy. At SIGMOD this is not appendix material — reviewers skim to it before reading the design.
Workload realism ladder
| Rung | Example | Evidentiary weight |
|---|---|---|
| Microbenchmark | Single-operator stress loop | Explains mechanisms; proves little alone |
| Standard benchmark | TPC-style, YCSB-style suites at stated scale | Comparable across papers; known blind spots |
| Trace-derived | Public or characterized production traces | High, if provenance is disclosed |
| End-to-end application | Full query mix on realistic schema | Highest, rarely achieved |
A SIGMOD-strong evaluation climbs the ladder: microbenchmarks to expose the mechanism, a standard suite for comparability, and at least one workload that argues real deployments look like this. An evaluation living entirely on rung one gets the "toy workloads" objection regardless of speedups.
Baseline fairness protocol
The evaluation's credibility ceiling is the weakest baseline treatment:
- Use current versions, compiled with the same optimization effort as your system, configured per their documentation or authors.
- Enable the competitor's relevant features — comparing your tuned build against a default-config rival is the field's best-known foul.
- Where a baseline's own paper reports numbers on similar hardware, reconcile: if you measure it slower than its authors did, say why.
- Include the boring-but-strong baseline (a mature engine, a well-indexed Postgres, a hand-tuned flat scan); beating only research prototypes invites the question you least want.
Curves, not points
- Scalability: sweep threads/cores/nodes and show the shape; a single-point "64-core" result hides the knee reviewers care about.
- Data size: sweep scale factors; crossover points against baselines are more informative than any fixed-size win.
- Skew and contention: sweep the skew parameter; uniform-only evaluation of a concurrency or caching contribution is near-disqualifying.
- Latency: percentiles across the sweep, since p99 behavior under load is frequently where the contribution actually lives (or dies).
Show full SKILL.md (224 more words)Show less
Ablations that isolate the mechanism
If the system adds three techniques, the evaluation must attribute the gain: a cumulative build-up (base, +A, +A+B, full) or leave-one-out grid. The review question being preempted is "is the win just the rewrite?" — so where feasible, include the strongest possible your-system-minus-the-idea configuration as its own baseline.
Run hygiene
For every plotted point:
runs >= 5 (or stated justification), report median + spread
cold vs. warm state declared; caches handled identically across systems
same measurement harness for all systems; harness in the artifact
seeds pinned for generated data and workload shuffles
outlier policy stated before running, not after seeing resultsNumbers that move between the submitted and revised versions without an explained cause are a specific, remembered failure mode in multi-round review — hygiene at first submission protects the revision.
Matching metrics to contribution type
- Storage/index structures: throughput plus space amplification plus build/maintenance cost — one without the others invites the "what did it cost" question.
- Transaction/concurrency work: abort rates and tail latency under contention, not just committed-throughput peaks.
- Query processing: per-query breakdowns alongside suite totals; a suite geomean can hide one catastrophic regression reviewers will find.
- Distributed systems: report behavior during failures and rebalancing, not only steady state.
- ML-for-systems: include the training/adaptation cost and the stale-model regime; the "what happens when the workload shifts" question is now standard at this venue.
Negative-result honesty
Show where the system loses: the workload regime where the incumbent wins, the overhead paid on the unfavorable mix. A measured, explained loss buys more trust with this PC than any additional win, and it pre-writes the limitations paragraph reviewers will otherwise draft as an objection.
Output format
[Setup contract] complete / missing items listed
[Ladder position] rungs covered; realism gap
[Baseline audit] versions, tuning, features, reconciliation
[Curve coverage] scalability / size / skew / percentile sweeps present
[Attribution] ablation design isolates each claimed technique yes/no
[Loss map] regimes where the system loses, disclosed or hidden
[Decisive missing run] the one experiment to add before the round© 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 SIGMOD-Skills/skills/sigmod-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigmod 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 |
|---|---|---|---|---|---|---|
| Sigmod Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | 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 |
Similar skills
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
alirezarezvani/claude-skills
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
openclaw/clawhub
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
affaan-m/ECC
Generate a design system from an existing codebase or audit one for visual consistency: extract tokens (colors, typography, spacing, shadows) into design-tokens.json and CSS custom properties with…
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Questions about Sigmod Experiments
What does Sigmod Experiments do?
A skill your agent uses when designing or auditing the evaluation of a SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency…. Sigmod Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency methodology, ablations that isolate the mechanism, and the setup disclosure a data-systems PC demands before trusting any speedup.
When should I use Sigmod Experiments?
Sigmod Experiments fits situations like: auditing the evaluation of a SIGMOD paper; covering workload realism and standard benchmark usage; baseline tuning fairness; scalability and tail-latency methodology.
How do I install Sigmod Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-experiments -a claude-code`. Or copy the skill folder (SIGMOD-Skills/skills/sigmod-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigmod-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Sigmod Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-experiments -a codex`. Or copy the skill folder (SIGMOD-Skills/skills/sigmod-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigmod-experiments in your project. Codex loads it when a task matches its description.
Can I use Sigmod 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 sigmod-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/sigmod-experiments, .gemini/skills/sigmod-experiments, .github/skills/sigmod-experiments and .opencode/skills/sigmod-experiments in your project.
What does Sigmod Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigmod Experiments is instructions for the agent only.
Does Sigmod 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 Sigmod 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 Sigmod Experiments use?
Sigmod 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 Sigmod Experiments use?
About 1.4k tokens (SKILL.md is roughly 5.5k 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 Sigmod Experiments?
Skills that share tags, products or a category with Sigmod 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 Sigmod 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.