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 a USENIX FAST storage evaluation, covering real devices and firmware, device-state control (aging, preconditioning, fill, TRIM), standard workloads…
MITAuto-check passed
Install Fast Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-experiments .claude/skills/fast-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 "fast-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-experiments into .claude/skills/fast-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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/FAST-Skills/skills/fast-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 fast-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-experiments .agents/skills/fast-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 "fast-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-experiments into .agents/skills/fast-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-experiments .cursor/skills/fast-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 "fast-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-experiments into .cursor/skills/fast-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 FAST-Skills/skills/fast-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 fast-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-experiments .gemini/skills/fast-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 "fast-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-experiments into .gemini/skills/fast-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-experiments .github/skills/fast-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 "fast-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-experiments into .github/skills/fast-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-experiments .opencode/skills/fast-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 "fast-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-experiments into .opencode/skills/fast-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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
fast-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 599 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 a USENIX FAST storage evaluation, covering real devices and firmware, device-state control (aging, preconditioning, fill, TRIM), standard workloads…
- Auditing a USENIX FAST storage evaluation
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, Device-state and measurement… and Crash-consistency and…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering real devices and firmware
What it does
Fast Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing a USENIX FAST storage evaluation, covering real devices and firmware, device-state control (aging, preconditioning, fill, TRIM), standard workloads and traces (SNIA IOTTA, YCSB, filebench, fio), write amplification, tail latency, endurance and wear, crash-consistency testing, fair baselines, and matching the metric to the shape of each storage claim.
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 a USENIX FAST storage evaluation
- Covering real devices and firmware
- Device-state control (aging
- Preconditioning
Example prompts
- “/fast-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
Fast Experiments loads about 1.6k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 599 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). 599 words, ~1,604 tokens.
Download SKILL.mdSave it as
.claude/skills/fast-experiments/SKILL.md (or your agent's skills folder).- name
- fast-experiments
- description
- Use when designing or auditing a USENIX FAST storage evaluation, covering real devices and firmware, device-state control (aging, preconditioning, fill, TRIM), standard workloads and traces (SNIA IOTTA, YCSB, filebench, fio), write amplification, tail latency, endurance and wear, crash-consistency testing, fair baselines, and matching the metric to the shape of each storage claim.
FAST Experiments
Use this before submission when the storage evaluation is not yet locked. FAST reviewers are storage people; the evaluation is where a good idea is won or lost, and the questions are storage-specific. The organizing principle is measure the storage cost you claim to change, on real hardware in a realistic state — not a throughput bar on a fresh drive.
Evaluation audit
- Match the metric to the storage claim. A claim about endurance needs bytes-written / P/E cycles, not throughput; a claim about responsiveness needs tail latency (p99/p99.9), not the mean; a claim about space needs measured on-media footprint; a claim about durability needs a crash-consistency test. The wrong metric is the most common FAST reject.
- Use real devices, and name them. Model, capacity, interface (SATA/SAS/NVMe), and — critically — firmware version, plus host, kernel, and filesystem/mkfs options. Nominally identical drives differ part-to-part and across firmware; a result without the device table is not auditable.
- Control device state. SSDs must be preconditioned to steady state (fresh-out-of-box numbers flatter every design); report fill level and TRIM/discard state. For file systems, report whether the volume was aged/fragmented or empty — aging can dominate the result.
- Drive with credible workloads and traces. Standard generators (fio, filebench, YCSB for KV/DB) and archived traces (SNIA IOTTA block/object traces, production-derived traces) beat an ad-hoc microbenchmark. Ship the job files and replay scripts.
- Choose fair baselines, including the strongest prior system and a reasonable default, tuned with a documented, equal budget. An untuned or default-config baseline is a scored weakness.
- Test the invariant your optimization risks. If a change defers writes, batches, or reorders, show crash consistency still holds (record-and-replay / fault injection), not just that it is faster.
- Design threats in, not on: know before you run which confounds (device variance, thermal throttling, cache effects, contamination of a trace) will bite, and instrument to bound them.
Show full SKILL.md (292 more words)Show less
Claim-to-evidence design table
| Storage claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Cuts write amplification / extends endurance" | Bytes-written from device counters (SMART/logs) at steady state; projected P/E budget | "Estimated WA on a fresh drive; no device counters" |
| "Lower/steadier latency" | Full latency distribution incl. p99/p99.9 under load | "Reports mean latency only" |
| "Scales to real capacities/workloads" | Real-sized datasets and standard traces on real devices | "Tiny dataset on a simulator" |
| "Preserves crash consistency" | Fault-injection / block-level record-and-replay recovery test | "Claims consistency, never crash-tests it" |
| "Faster than system X" | X tuned with equal, documented budget; same hardware and state | "Default-config or older-hardware baseline" |
| "Reliability finding generalizes" | Population, models, and duration stated; external validity bounded | "One model, one datacenter, claimed universal" |
Device-state and measurement floor
[Devices] list model, capacity, interface, firmware; host, kernel, mkfs/mount options
[Steady state] precondition SSDs to steady state; state fill level and TRIM; disclose FOB vs. aged
[Warmup] discard cold-cache warmup unless the cold path IS the claim; state cache/DRAM sizes
[Repeats] multiple runs; report variance/CIs; note thermal or throttling effects
[Counters] read WA, bytes-written, GC activity from device logs where available, not estimates
[Trace replay] replay archived traces with a documented tool; state timing fidelity (open vs. closed loop)Crash-consistency and durability testing
Durability claims are load-bearing at FAST and are frequently under-tested:
[Model] state the failure model (power loss, kernel panic, fsync semantics)
[Injection] use block-level record-and-replay or a fault injector to cut writes at many points
[Check] verify the post-recovery state satisfies the invariant (no torn/lost committed data)
[Coverage] report how many crash points / orderings were tested, not a single anecdoteProvenance floor for traces and field studies
- Archive the replayed trace (or document access) and the replay scripts; a trace named but not shipped cannot be reproduced.
- For field/reliability studies, state the population, drive models, technology (SLC/MLC/TLC/QLC), duration, and how failures/replacements were defined and counted.
- Report how outliers, warmup, and defective units were handled — silent inclusion or exclusion skews every downstream number.
Vignette: evaluating a compaction change for a KV store
Suppose the paper claims an endurance-aware compaction scheduler cuts bytes written. The matching plan: run on named SSDs at steady state with firmware recorded; drive with YCSB plus an archived production trace; measure bytes-written from the device's own counters, not the LSM's estimate; report read-latency distributions incl. p99.9 to prove the trade is bounded; compare against the tuned stock compactor with an equal budget; and run a crash-consistency record-and-replay test to confirm deferring compactions did not weaken durability — every number traceable to a logged run in the artifact.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> metric map] <claim: device/metric/statistic>
[Device reality] <models + firmware + state (steady/aged/fill/TRIM) stated? yes/no>
[Baseline fairness] <baseline -> tuned? equal budget? same hardware/state?>
[Durability check] <crash-consistency / fault-injection test present? yes/no>
[Threats-by-design] <device variance / warmup / trace contamination -> instrumentation>
[Decision-critical next run] <one experiment to add>© 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 FAST-Skills/skills/fast-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Fast 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 |
|---|---|---|---|---|---|---|
| Fast Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | 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 |
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 Fast Experiments
What does Fast Experiments do?
A skill your agent uses when designing or auditing a USENIX FAST storage evaluation, covering real devices and firmware, device-state control (aging, preconditioning, fill, TRIM), standard workloads…. Fast Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing a USENIX FAST storage evaluation, covering real devices and firmware, device-state control (aging, preconditioning, fill, TRIM), standard workloads and traces (SNIA IOTTA, YCSB, filebench, fio), write amplification, tail latency, endurance and wear, crash-consistency testing, fair baselines, and matching the metric to the shape of each storage claim.
When should I use Fast Experiments?
Fast Experiments fits situations like: auditing a USENIX FAST storage evaluation; covering real devices and firmware; device-state control (aging; preconditioning.
How do I install Fast Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-experiments -a claude-code`. Or copy the skill folder (FAST-Skills/skills/fast-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/fast-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Fast Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-experiments -a codex`. Or copy the skill folder (FAST-Skills/skills/fast-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/fast-experiments in your project. Codex loads it when a task matches its description.
Can I use Fast 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 fast-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/fast-experiments, .gemini/skills/fast-experiments, .github/skills/fast-experiments and .opencode/skills/fast-experiments in your project.
What does Fast Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Fast Experiments is instructions for the agent only.
Does Fast 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 Fast 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 Fast Experiments use?
Fast 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 Fast Experiments use?
About 1.6k tokens (SKILL.md is roughly 6.4k 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 Fast Experiments?
Skills that share tags, products or a category with Fast 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 Fast 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.