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 ISCA paper — pinning simulator fidelity to the claims it must carry, documenting gem5-class configurations and sampling…
MITAuto-check passed
Install Isca Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isca-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-experiments .claude/skills/isca-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 "isca-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-experiments into .claude/skills/isca-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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/ISCA-Skills/skills/isca-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 isca-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-experiments .agents/skills/isca-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 "isca-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-experiments into .agents/skills/isca-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 isca-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-experiments .cursor/skills/isca-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 "isca-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-experiments into .cursor/skills/isca-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 ISCA-Skills/skills/isca-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 isca-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-experiments .gemini/skills/isca-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 "isca-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-experiments into .gemini/skills/isca-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 isca-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 isca-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/ISCA-Skills/skills/isca-experiments .github/skills/isca-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 "isca-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-experiments into .github/skills/isca-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 isca-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 isca-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/ISCA-Skills/skills/isca-experiments .opencode/skills/isca-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 "isca-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-experiments into .opencode/skills/isca-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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
isca-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 784 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 ISCA paper — pinning simulator fidelity to the claims it must carry, documenting gem5-class configurations and sampling…
- Works in 4 steps: Tool and version. Which… → Fidelity scope. Which parts of the… → Simulation regions. Full workloads,… → …
- Auditing the evaluation of an ISCA paper — pinning simulator fidelity to the claims it must carry
- SKILL.md covers Declare the methodology contract, Match each claim to an…, Workloads argue… and Baselines: configure the…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Isca Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an ISCA paper — pinning simulator fidelity to the claims it must carry, documenting gem5-class configurations and sampling choices, selecting workload suites that represent the claim's domain, tuning baselines in good faith, and separating architectural effect from modeling artifact.
Its SKILL.md is about 1.8k 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 ISCA paper — pinning simulator fidelity to the claims it must carry
- Documenting gem5-class configurations and sampling choices
- Selecting workload suites that represent the claims domain
- Tuning baselines in good faith
Example prompts
- “/isca-experiments”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Tool and version. Which simulator/emulator/RTL flow, at which exact version
- Fidelity scope. Which parts of the machine are modeled cycle-by-cycle,
- Simulation regions. Full workloads, checkpoints, or sampled regions
- Anchoring. What ties the model to reality — validation against a physical
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 ini).
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
Isca Experiments loads about 1.8k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 784 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). 784 words, ~1,750 tokens.
Download SKILL.mdSave it as
.claude/skills/isca-experiments/SKILL.md (or your agent's skills folder).- name
- isca-experiments
- description
- Use when designing or auditing the evaluation of an ISCA paper — pinning simulator fidelity to the claims it must carry, documenting gem5-class configurations and sampling choices, selecting workload suites that represent the claim's domain, tuning baselines in good faith, and separating architectural effect from modeling artifact.
ISCA Experiments
Most ISCA evaluations run on models of machines rather than machines, so the evaluation section is really two nested arguments: that the modeled effect is real, and that the model deserves trust for this effect. Reviewers at this venue are professionally skeptical about the second argument, and papers die on it more often than on the first. Everything below serves one rule: the paper must state what its numbers are made of.
Declare the methodology contract
Early in the methodology section, answer four questions explicitly — this is the contract reviewers try to reconstruct when authors omit it:
- Tool and version. Which simulator/emulator/RTL flow, at which exact version or commit, with which local modifications? "gem5" without a commit is not a methodology; behavior differs meaningfully across releases and forks.
- Fidelity scope. Which parts of the machine are modeled cycle-by-cycle, which are stylized (fixed-latency, functional-only), and which are absent (typical gaps: OS effects, TLB behavior, DRAM refresh, on-chip network contention)? A claim must not rest on a component in the stylized list.
- Simulation regions. Full workloads, checkpoints, or sampled regions (SimPoint-style)? How many instructions of warm-up before measurement, and how was that length chosen? Cold-structure artifacts masquerade as results.
- Anchoring. What ties the model to reality — validation against a physical machine on a subset, cross-checking against published characterization, or agreement between two independent instruments? One honest anchoring paragraph outweighs three extra benchmark suites.
Match each claim to an instrument that can carry it
| Claim type | Sufficient instrument | Chronic mismatch to avoid |
|---|---|---|
| Relative IPC/latency effect of a microarchitectural change | Cycle-level simulation with the changed structures modeled in detail | Quoting the result as absolute time or absolute joules |
| Absolute end-to-end performance | Real silicon or FPGA prototype measurement | Deriving it from an unvalidated software model |
| Energy/power | Measured power, or a named model (McPAT-class) with node assumptions stated | Modeled milliwatts presented without the model's error bars or vintage |
| Area/timing feasibility | Synthesis of the added logic, or a sizing argument from structure counts | "Negligible area" with no numbers |
| OS/IO-dependent behavior | Full-system simulation or hardware | User-level simulation silently ignoring the kernel |
| Datacenter/at-scale effects | Measurement study or trace-driven analysis with trace provenance | Extrapolating single-node simulation to fleet claims |
Workloads argue representativeness, not volume
Choose suites because they exercise the mechanism's operating region, and say so. A cache-hierarchy paper needs memory-intensive selections and must report MPKI or footprint evidence that the pressure is real; an accelerator paper needs at least one end-to-end application, because kernels-only evaluation invites the question of what fraction of total time the kernel is. Standard anchors (SPEC-class CPU suites, graph/ML/server suites as appropriate) buy comparability; a workload nobody recognizes needs a characterization subsection justifying its inclusion. Report per-workload results — geomean-only reporting reads as concealment, and the interesting review questions live in the outliers.
Show full SKILL.md (321 more words)Show less
Baselines: configure the adversary to win
The baseline is the strongest relevant prior mechanism tuned the way its authors would tune it, at equal hardware budget where the comparison implies one. State the equalization: same storage, same ports, same technology assumptions. When comparing against a prior paper's mechanism, reimplement it in your framework and say how you verified the reimplementation reproduces its published behavior — reviewers who authored those baselines are plausibly on the committee.
Attribution and sensitivity
For every "gain comes from M" sentence, include the run with M disabled or replaced by its naive variant. Then sweep the two or three parameters the design is most sensitive to (table sizes, thresholds, latencies) and show where the benefit collapses — a visible break point is credibility, not weakness. Report run-to-run variation wherever nondeterminism exists (real hardware, multithreaded simulation): repeated trials with dispersion, not single lucky runs.
A config manifest per reported number
Make every figure regenerable from a manifest the artifact ships (see
isca-reproducibility and isca-artifact-evaluation):
; exp/f7-headline.manifest — one file per figure/table
[instrument]
simulator = gem5
commit = a1b2c3d4 (+ local patches: patches/*.diff)
mode = full-system, O3 core model
[machine-model]
core = 8-wide OOO, 352-entry ROB, 3.2 GHz nominal
l1d/l1i = 48K/32K, 8-way
l2 = 1.25M private ; llc = 3M/core shared, 16-way
dram = DDR5-4800, 2 ch, model=detailed
[measurement]
regions = simpoints(k=10, interval=100M)
warmup = 50M inst per region
metric = IPC, geomean over per-workload weighted regions
trials = 3 (report min/median/max where variance > 1%)
[workloads]
suite = SPEC-class CPU suite, ref inputs; list = workloads.txtEvaluation-section order that answers reviewers in sequence
Headline comparison first; attribution/ablation second; sensitivity third; overheads (storage, energy, area, complexity) fourth; explicit limitations last. Burying overheads after the conclusion-adjacent paragraphs is the venue's most transparent tell; putting them in the main flow signals confidence.
Red flags this venue's reviewers name in reviews
- Simulator described in one sentence; configuration table missing latencies.
- Warm-up unstated; sampled regions chosen by an unnamed procedure.
- Baseline older than the idea it defends, or untuned defaults.
- Geomean-only reporting; suspicious absence of any losing workload.
- Modeled energy stated to three significant figures.
- A limitations paragraph that lists only limitations the design doesn't have.
Verified cycle facts (page rules, dates, double-blind handling of artifact links)
live in ../../resources/official-source-map.md, checked 2026-07-08; methodology
norms above are community practice, not CFP text, and should be applied with
judgment. The manifests built here are reused verbatim by isca-reproducibility
(freeze ritual) and isca-artifact-evaluation (claims table) — invest once,
spend three times.
© 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 ISCA-Skills/skills/isca-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Isca 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 |
|---|---|---|---|---|---|---|
| Isca Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Design Audit Against Rams' Principlesthedotmack/claude-mem | 98k | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Experiment Auditwanshuiyin/Auto-claude-code-research-in-sleep | 17k | 1 repos | ~2.7k | Automated safety check: Notes | MIT | |
| Experiment Auditwanshuiyin/Auto-claude-code-research-in-sleep | 17k | — | ~3.2k | Automated safety check: Notes | MIT | |
| Experiment Designeralirezarezvani/claude-skills | 28k | 1 repos | ~783 | Automated safety check: Pass | MIT | |
| OpenClaw Design Auditopenclaw/clawhub | 9.5k | — | ~498 | Automated safety check: Pass | MIT |
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Questions about Isca Experiments
What does Isca Experiments do?
A skill your agent uses when designing or auditing the evaluation of an ISCA paper — pinning simulator fidelity to the claims it must carry, documenting gem5-class configurations and sampling…. Isca Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an ISCA paper — pinning simulator fidelity to the claims it must carry, documenting gem5-class configurations and sampling choices, selecting workload suites that represent the claim's domain, tuning baselines in good faith, and separating architectural effect from modeling artifact.
When should I use Isca Experiments?
Isca Experiments fits situations like: auditing the evaluation of an ISCA paper — pinning simulator fidelity to the claims it must carry; documenting gem5-class configurations and sampling choices; selecting workload suites that represent the claims domain; tuning baselines in good faith.
How do I install Isca Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isca-experiments -a claude-code`. Or copy the skill folder (ISCA-Skills/skills/isca-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/isca-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Isca Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isca-experiments -a codex`. Or copy the skill folder (ISCA-Skills/skills/isca-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/isca-experiments in your project. Codex loads it when a task matches its description.
Can I use Isca 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 isca-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/isca-experiments, .gemini/skills/isca-experiments, .github/skills/isca-experiments and .opencode/skills/isca-experiments in your project.
What does Isca Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Isca Experiments is instructions for the agent only.
Does Isca 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 Isca 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 Isca Experiments use?
Isca 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 Isca Experiments use?
About 1.8k tokens (SKILL.md is roughly 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 Isca Experiments?
Skills that share tags, products or a category with Isca 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 Isca 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.