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 an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency…
MITAuto-check passed
Install Mlsys Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-experiments .claude/skills/mlsys-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 "mlsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-experiments into .claude/skills/mlsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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/MLSys-Skills/skills/mlsys-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 mlsys-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-experiments .agents/skills/mlsys-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 "mlsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-experiments into .agents/skills/mlsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 mlsys-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-experiments .cursor/skills/mlsys-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 "mlsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-experiments into .cursor/skills/mlsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 MLSys-Skills/skills/mlsys-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 mlsys-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mlsys-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/MLSys-Skills/skills/mlsys-experiments .gemini/skills/mlsys-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 "mlsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-experiments into .gemini/skills/mlsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 mlsys-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 mlsys-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/MLSys-Skills/skills/mlsys-experiments .github/skills/mlsys-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 "mlsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-experiments into .github/skills/mlsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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 mlsys-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 mlsys-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/MLSys-Skills/skills/mlsys-experiments .opencode/skills/mlsys-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 "mlsys-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MLSys-Skills/skills/mlsys-experiments into .opencode/skills/mlsys-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlsys-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
mlsys-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 723 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 an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency…
- Auditing the evaluation of an MLSys paper
- SKILL.md covers Workload selection: the…, Baseline discipline, The reporting quartet and Ablation = attribution, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Selecting representative workloads and hardware
What it does
Mlsys Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency tails, memory, cost, and quality together, structuring ablations that attribute gains to mechanisms, and building scaling and sensitivity evidence reviewers trust.
Its SKILL.md is about 1.6k 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 an MLSys paper
- Selecting representative workloads and hardware
- Tuning baselines symmetrically
- Reporting throughput
Example prompts
- “/mlsys-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 (its code samples are yaml).
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
Mlsys Experiments loads about 1.6k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 723 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). 723 words, ~1,622 tokens.
Download SKILL.mdSave it as
.claude/skills/mlsys-experiments/SKILL.md (or your agent's skills folder).- name
- mlsys-experiments
- description
- Use when designing or auditing the evaluation of an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency tails, memory, cost, and quality together, structuring ablations that attribute gains to mechanisms, and building scaling and sensitivity evidence reviewers trust.
MLSys Experiments
Use this before the evaluation is frozen. An MLSys evaluation exists to answer one compound question: does the named mechanism deliver the claimed system-level payoff on workloads that matter, at acceptable cost, for understood reasons? Each clause needs its own evidence, and this venue — which published the MLPerf Training methodology in its own proceedings — holds measurement to benchmark-committee standards.
Workload selection: the make-or-break choice
"Unrepresentative workload" is the modal fatal objection at this venue. Defend the choice explicitly:
- Anchor on recognizable workloads: MLPerf-family tasks, widely used open models, or published request traces. Bespoke workloads need a characterization section showing their statistics (request rates, sequence lengths, arrival burstiness, model mix).
- Cover the axis your mechanism exploits: a scheduler for bursty load must be tested on bursty and uniform load; the uniform case bounds your claim, it does not weaken it.
- Include at least one workload where you expect little or no gain, and show it. A non-win row is the cheapest credibility purchase available.
- State workload licensing/provenance — traces you cannot release constrain your future
artifact badges (see
mlsys-artifact-evaluation).
Baseline discipline
- Compare against the current strongest systems, version-pinned, in their recommended configurations — and tune them with the same budget you gave your own system. Document both budgets; asymmetric tuning is treated as invalidating.
- Include the "do nothing" configuration where meaningful (e.g., default framework behavior); reviewers use it to sanity-check absolute numbers.
- If a relevant baseline cannot run in your setting, demonstrate the incompatibility rather than asserting it.
The reporting quartet
Never report throughput alone. Each headline claim carries four coordinates:
| Coordinate | Report as | Classic gaming pattern it prevents |
|---|---|---|
| Throughput/goodput | Under a stated latency constraint | Batch-size inflation that destroys tails |
| Latency | p50 and p99 (p99.9 for serving), distributions not means | Mean-only tables hiding stragglers |
| Memory | Peak device + host, at the measured configuration | Wins that only fit on 80GB parts, unstated |
| Cost/efficiency | $ or joules per unit work, with price/meter basis | "Faster" systems that are 4x more expensive |
Quality (accuracy/perplexity or task metric) rides along whenever the technique could plausibly change model output — quantization, sparsity, scheduling with dropping, approximation of any kind. "No quality change" is a claim requiring a measurement.
Ablation = attribution
Systems papers stack techniques; reviews ask which component pays.
- One ablation row per named mechanism, cumulative or leave-one-out — pick one scheme and say which.
- Ablate the insight, not only the code: if the claim is "lookahead scheduling wins," include the no-lookahead scheduler, not just the whole-system-off row.
- Sensitivity sweeps over the parameters a deployer must set (window sizes, thresholds); a mechanism needing per-workload hand-tuning must say so.
Show full SKILL.md (296 more words)Show less
Scaling and sensitivity evidence
# experiment-matrix.yaml — one row per plotted point, generated, never hand-edited
sweep:
models: [llama-class-8b, llama-class-70b, moe-16e]
hardware: [1xA100, 8xA100-nvlink] # state interconnect explicitly
load: [uniform-qps, bursty-trace-A]
precision: [bf16, int4]
trials: 5
warmup_steps: 20
metrics: [goodput@p99<250ms, p50, p99, peak_mem_gb, gpu_hours, quality]
seeds: [1, 2, 3, 4, 5]- Plot trends, not points: gain versus model size, versus load, versus GPU count. A single-configuration win invites the "will this survive scale?" objection you cannot answer in the four-day response window.
- Log everything; regenerate every figure from logs by script (measurement mechanics
and variance rules live in
mlsys-reproducibility).
Statistical floor for systems numbers
Systems papers rarely need hypothesis-testing machinery, but they always need variance honesty; the floor this venue's reviewers apply:
- Five or more trials for any number in a comparison table; report spread (stdev or IQR) and say which in the caption.
- Overlapping spreads mean the difference is not a finding — either run more trials or report the tie honestly; a "win" inside noise is worse than a reported tie because one reviewer rerun destroys it.
- For latency distributions, trials are not enough: within-run percentiles need enough requests that p99 is estimated from hundreds of samples, not three.
- Seed variation matters only where model quality enters the claim; systems noise
(placement, thermal state, co-tenants) dominates latency variance and is controlled
by measurement design, not seeds — the split is detailed in
mlsys-reproducibility.
Evaluation-design review before running
Run this table exercise before burning GPU-hours: for each claim in the abstract, write the figure that will support it, the workload it needs, and the strongest objection a systems reviewer could raise — then check the matrix covers that objection. Claims without a planned figure get cut from the abstract, not padded later.
Common reject patterns to design against
- Microbenchmark gains that never appear end-to-end; always include the end-to-end run.
- Gains measured only at one scale/hardware and claimed generally.
- Quality silently degraded by the optimization, discovered by a reviewer from your own appendix table.
- Cost omitted where the honest accounting would erase the win.
Output format
[Evaluation readiness] strong / adequate / weak
[Workload defense] <named anchors + characterization + non-win case present?>
[Baseline symmetry] <versions pinned, tuning budgets equal + documented?>
[Quartet coverage] <throughput/latency-tails/memory/cost per headline claim>
[Attribution] <mechanism -> ablation row>
[Decision-critical missing run] <one experiment>© 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 MLSys-Skills/skills/mlsys-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Mlsys 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 |
|---|---|---|---|---|---|---|
| Mlsys Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Design Audit Against Rams' Principlesthedotmack/claude-mem | 97k | — | ~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 Mlsys Experiments
What does Mlsys Experiments do?
A skill your agent uses when designing or auditing the evaluation of an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency…. Mlsys Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an MLSys paper, selecting representative workloads and hardware, tuning baselines symmetrically, reporting throughput, latency tails, memory, cost, and quality together, structuring ablations that attribute gains to mechanisms, and building scaling and sensitivity evidence reviewers trust.
When should I use Mlsys Experiments?
Mlsys Experiments fits situations like: auditing the evaluation of an MLSys paper; selecting representative workloads and hardware; tuning baselines symmetrically; reporting throughput.
How do I install Mlsys Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-experiments -a claude-code`. Or copy the skill folder (MLSys-Skills/skills/mlsys-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/mlsys-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Mlsys Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mlsys-experiments -a codex`. Or copy the skill folder (MLSys-Skills/skills/mlsys-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/mlsys-experiments in your project. Codex loads it when a task matches its description.
Can I use Mlsys 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 mlsys-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/mlsys-experiments, .gemini/skills/mlsys-experiments, .github/skills/mlsys-experiments and .opencode/skills/mlsys-experiments in your project.
What does Mlsys Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Mlsys Experiments is instructions for the agent only.
Does Mlsys 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 Mlsys 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 Mlsys Experiments use?
Mlsys 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 Mlsys Experiments use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Mlsys Experiments?
Skills that share tags, products or a category with Mlsys Experiments: Design Audit Against Rams' Principles (thedotmack/claude-mem, 97k 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 Mlsys Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,216 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.