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 ASPLOS paper — choosing among real silicon, FPGA prototypes, and simulators with cycle-accuracy caveats stated, selecting…
MITAuto-check passed
Install Asplos Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill asplos-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-experiments .claude/skills/asplos-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 "asplos-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-experiments into .claude/skills/asplos-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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/ASPLOS-Skills/skills/asplos-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 asplos-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-experiments .agents/skills/asplos-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 "asplos-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-experiments into .agents/skills/asplos-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 asplos-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-experiments .cursor/skills/asplos-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 "asplos-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-experiments into .cursor/skills/asplos-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 ASPLOS-Skills/skills/asplos-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 asplos-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-experiments .gemini/skills/asplos-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 "asplos-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-experiments into .gemini/skills/asplos-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 asplos-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 asplos-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/ASPLOS-Skills/skills/asplos-experiments .github/skills/asplos-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 "asplos-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-experiments into .github/skills/asplos-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 asplos-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 asplos-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/ASPLOS-Skills/skills/asplos-experiments .opencode/skills/asplos-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 "asplos-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-experiments into .opencode/skills/asplos-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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
asplos-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 846 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 ASPLOS paper — choosing among real silicon, FPGA prototypes, and simulators with cycle-accuracy caveats stated, selecting…
- Auditing the evaluation of an ASPLOS paper — choosing among real silicon
- SKILL.md covers The instrument ladder, Cycle-accuracy caveats are…, Workloads and baselines that… and Attribution: ablate the…, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- FPGA prototypes
What it does
Asplos Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an ASPLOS paper — choosing among real silicon, FPGA prototypes, and simulators with cycle-accuracy caveats stated, selecting workload suites and baselines that hold up across three communities, attributing wins via ablation, and reporting energy, area, and overhead honestly.
Its SKILL.md is about 1.9k 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 ASPLOS paper — choosing among real silicon
- FPGA prototypes
- Simulators with cycle-accuracy caveats stated
- Selecting workload suites and baselines that hold up across three communities
Example prompts
- “/asplos-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
Asplos Experiments loads about 1.9k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 846 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). 846 words, ~1,933 tokens.
Download SKILL.mdSave it as
.claude/skills/asplos-experiments/SKILL.md (or your agent's skills folder).- name
- asplos-experiments
- description
- Use when designing or auditing the evaluation of an ASPLOS paper — choosing among real silicon, FPGA prototypes, and simulators with cycle-accuracy caveats stated, selecting workload suites and baselines that hold up across three communities, attributing wins via ablation, and reporting energy, area, and overhead honestly.
ASPLOS Experiments
An ASPLOS evaluation answers to three communities at once: architects who will audit the modeling, OS people who will audit the workload realism, and PL people who will audit what the software layer actually does. The section's core discipline is matching each claim to an instrument whose error model can carry it — and saying what that error model is.
The instrument ladder
| Instrument | What it can prove | What it cannot | Must be reported |
|---|---|---|---|
| Real silicon | End-to-end effects, OS interactions, true tails | Designs needing hardware that doesn't exist | CPU/stepping, kernel + config, microcode, BIOS knobs (SMT/turbo/prefetchers), memory topology |
| FPGA prototype | Feasibility, cycle behavior of new logic at the prototype's clock | Absolute performance of an ASIC-class part | Board, clock, resource utilization, what was scaled down and why |
| Cycle-level simulator (e.g. gem5-class) | Relative effects of microarchitectural change under stated configs | Anything outside modeled fidelity — I/O, OS noise, firmware behavior are commonly stylized | Simulator + exact version/commit, config files, warm-up and region-selection method, validation against a real machine where possible |
| Analytical/energy models (McPAT-class, first-order area) | Trend-level energy/area comparisons | Absolute mW or mm² as truth | Model version, technology node assumptions, and the claim written as trend not absolute |
The cardinal sin is a claim-instrument mismatch: absolute latency claims from an unvalidated simulator, or OS-interaction claims from a user-space harness. Rapid and full reviewers both hunt for it.
Cycle-accuracy caveats are content, not apology
When simulation carries a claim, the paper must state: which structures are modeled in detail vs stylized; how simulation regions were chosen (full runs, checkpoints, sampled regions à la SimPoint-style methodology); how long the warm-up was; and — strongest of all — a validation experiment showing the simulator tracks a real machine on a measurable subset. A one-paragraph validation against silicon buys credibility that no amount of extra benchmarks can.
Workloads and baselines that survive three audiences
- Draw workloads from suites the communities recognize (SPEC-class CPU suites, parallel suites, cloud/graph/serving workloads appropriate to the claim) and include at least one full application or kernel-integrated scenario — accelerator papers evaluated only on extracted kernels routinely get the "where is the rest of the system" review.
- The baseline is the strongest deployed alternative configured by someone who wants it to win: current kernel policy with its tunables set properly, the vendor library, the state-of-the-art accelerator at an honest technology normalization.
- Technology normalization must be explicit when comparing across nodes or clocks: state the scaling assumptions rather than silently converting.
Attribution: ablate the mechanism you credit
Every "X improves Y because of mechanism M" needs a run with M removed, weakened, or transplanted onto the baseline. In cross-layer papers this means ablating each side of the boundary separately — hardware hints without the new policy, policy without the hints — because the venue's whole premise is that the coupling matters; prove the coupling, not just the sum.
The claim-instrument matrix
Freeze this before writing; it becomes the evaluation section's skeleton and the rebuttal's ammunition:
claim instrument workloads baseline(+config) metric + spread where
end-to-end speedup real 2-socket+CXL SPEC17 + graph(5) Linux 6.9 tiering, runtime, gmean, 10 runs, §6.2
tuned per docs 95% CI
coupling is necessary same subset(6) each-half ablation delta vs full design §6.4
generality across latency gem5 (pinned cfg) subset(6) same policy trend, sim-validated §6.5
overhead where design idles real hardware non-tiered set stock kernel <=2% regression bound §6.6
energy trend McPAT-class model subset baseline design trend only, node stated §6.7Report dispersion for anything measured on real hardware (runs, variance source, CI); report sensitivity for anything simulated (which config parameters move the result). Include the workload where the design loses and explain the boundary — a measured regression with a mechanism story is evidence of understanding, and its absence is conspicuous to reviewers who build systems themselves.
Show full SKILL.md (306 more words)Show less
Measurement noise on real hardware is a design input
Silicon experiments carry noise sources that simulators hide, and the paper's run protocol must name its countermeasures: pin frequency governors or report the governor used; control or randomize NUMA placement; interleave A/B runs rather than batching (thermal and cache state drift over a session); and distinguish warm-start from cold-start numbers explicitly. When an effect is within the machine's observed run-to-run variance, the honest sentence is that the experiment cannot distinguish the designs — reviewers respect the sentence and pounce on its absence.
Energy, power, and area claims
- On silicon, name the meter: RAPL-class counters, wall-power instrumentation, or board-level telemetry — each has known blind spots worth one caveat clause.
- Model-derived energy or area numbers (McPAT-class, synthesis estimates) support comparisons under stated assumptions, not datasheet-grade values; write them as ratios with the technology node and model version attached.
- FPGA utilization (LUTs, BRAM, DSPs) is evidence of feasibility at the prototype's scale — extrapolating it to ASIC area needs an explicit argument, or the claim should stay at feasibility.
Evaluation-methodology papers
Note that "experimental methodologies" is itself on the 2027 topics list: if
the most defensible contribution turns out to be the measurement approach — a
validation harness, a workload characterization, a simulation-sampling method —
consider promoting it from a subsection to the paper, with asplos-topic-selection
re-run on the promoted claim.
Sweeps and knees
Cross-layer designs live or die on regime boundaries, so at least one sweep per load-bearing parameter (device latency, core count, working-set size, offered load) should run past the knee — the point where the benefit saturates or inverts. A curve truncated before its knee is read by systems reviewers as a curve hiding its knee. State where the knee is and why it sits there; the mechanism story at the boundary is often the most-cited sentence in the paper.
Output format
[Matrix] every claim has instrument+baseline+location: Y/N (orphans listed)
[Instrument audit] any claim exceeding its instrument's error model? list
[Simulator hygiene] version/config/regions/warm-up stated · validated vs silicon?
[Baseline strength] strongest deployed alternative, tuned: Y/N per claim
[Attribution] per-layer ablations present: Y/N
[Adverse results] losing workload + boundary explanation in paper: Y/N© 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 ASPLOS-Skills/skills/asplos-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Asplos 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 |
|---|---|---|---|---|---|---|
| Asplos Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Design Audit Against Rams' Principlesthedotmack/claude-mem | 99k | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Experiment Auditwanshuiyin/Auto-claude-code-research-in-sleep | 17k | 1 repos | ~2.7k | Automated safety check: Notes | MIT | |
| Experiment Auditwanshuiyin/Auto-claude-code-research-in-sleep | 17k | — | ~3.2k | Automated safety check: Notes | MIT | |
| Experiment Designeralirezarezvani/claude-skills | 28k | 1 repos | ~783 | Automated safety check: Pass | MIT | |
| OpenClaw Design Auditopenclaw/clawhub | 9.5k | — | ~498 | Automated safety check: Pass | MIT |
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Questions about Asplos Experiments
What does Asplos Experiments do?
A skill your agent uses when designing or auditing the evaluation of an ASPLOS paper — choosing among real silicon, FPGA prototypes, and simulators with cycle-accuracy caveats stated, selecting…. Asplos Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an ASPLOS paper — choosing among real silicon, FPGA prototypes, and simulators with cycle-accuracy caveats stated, selecting workload suites and baselines that hold up across three communities, attributing wins via ablation, and reporting energy, area, and overhead honestly.
When should I use Asplos Experiments?
Asplos Experiments fits situations like: auditing the evaluation of an ASPLOS paper — choosing among real silicon; FPGA prototypes; simulators with cycle-accuracy caveats stated; selecting workload suites and baselines that hold up across three communities.
How do I install Asplos Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill asplos-experiments -a claude-code`. Or copy the skill folder (ASPLOS-Skills/skills/asplos-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/asplos-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Asplos Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill asplos-experiments -a codex`. Or copy the skill folder (ASPLOS-Skills/skills/asplos-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/asplos-experiments in your project. Codex loads it when a task matches its description.
Can I use Asplos 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 asplos-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/asplos-experiments, .gemini/skills/asplos-experiments, .github/skills/asplos-experiments and .opencode/skills/asplos-experiments in your project.
What does Asplos Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Asplos Experiments is instructions for the agent only.
Does Asplos 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 Asplos 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 Asplos Experiments use?
Asplos 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 Asplos Experiments use?
About 1.9k tokens (SKILL.md is roughly 7.7k 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 Asplos Experiments?
Skills that share tags, products or a category with Asplos 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 Asplos 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.