Design Audit Against Rams' Principles
thedotmack/claude-mem
Audits a design against Dieter Rams' ten principles of good design, scores each with evidence, and hands off a make-plan prompt for a new, refined or redesigned outcome.
A skill your agent uses when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs…
MITAuto-check passed
Install Wsdm Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-experiments .claude/skills/wsdm-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 "wsdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-experiments into .claude/skills/wsdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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/WSDM-Skills/skills/wsdm-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 wsdm-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-experiments .agents/skills/wsdm-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 "wsdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-experiments into .agents/skills/wsdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-experiments .cursor/skills/wsdm-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 "wsdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-experiments into .cursor/skills/wsdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 WSDM-Skills/skills/wsdm-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 wsdm-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-experiments .gemini/skills/wsdm-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 "wsdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-experiments into .gemini/skills/wsdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-experiments .github/skills/wsdm-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 "wsdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-experiments into .github/skills/wsdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-experiments .opencode/skills/wsdm-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 "wsdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-experiments into .opencode/skills/wsdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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
wsdm-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 632 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs…
- Works in 4 steps: Split by time, and say so. Interaction… → Define the candidate set.… → Fix the cutoff story. Report metrics at… → …
- Auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls
- SKILL.md covers The evidence quadrants, Protocol decisions to fix…, Baseline selection and Ablations that isolate the…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Wsdm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs, baseline selection from recent WSDM/SIGIR/KDD editions, ablations that isolate the mechanism, efficiency reporting, and online-evidence framing.
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 a WSDM paper - offline ranking and recommendation metrics with bias controls
- Temporal-split protocols for interaction logs
- Baseline selection from recent WSDM/SIGIR/KDD editions
- Ablations that isolate the mechanism
Example prompts
- “/wsdm-experiments”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Split by time, and say so. Interaction data demands temporal splits;
- Define the candidate set. Sampled-negative evaluation (rank the true
- Fix the cutoff story. Report metrics at cutoffs that match the claimed
- Pick the unit of significance. Paired tests over queries/users/sessions,
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
Wsdm Experiments loads about 1.6k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 632 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). 632 words, ~1,563 tokens.
Download SKILL.mdSave it as
.claude/skills/wsdm-experiments/SKILL.md (or your agent's skills folder).- name
- wsdm-experiments
- description
- Use when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs, baseline selection from recent WSDM/SIGIR/KDD editions, ablations that isolate the mechanism, efficiency reporting, and online-evidence framing.
WSDM Experiments
Design an evaluation that survives WSDM's mixed academic-industry PC with no rebuttal to patch holes. The venue's evaluation culture is specific: reviewers assume interaction data is biased until you control for it, assume random splits leak until you say "temporal," and assume unnamed baselines were chosen to lose. Build the section so each assumption meets its answer.
The evidence quadrants
Cover the four questions every strong WSDM evaluation answers; weak papers usually max one quadrant and ignore two:
| Quadrant | Question | Typical instruments |
|---|---|---|
| Effectiveness | Better on the task? | nDCG/MRR/MAP@k, Recall/HR@k, AUC/logloss for CTR |
| Validity | Better for the claimed reason, or an artifact? | Bias controls, leakage checks, ablations |
| Efficiency | Affordable at serving time? | Latency, throughput, index/memory cost, training compute |
| Robustness | Where does it break? | Cold-start slices, head/tail splits, temporal drift, adversarial cases |
Effectiveness without validity is the classic WSDM rejection ("gains may be position-bias artifacts"); effectiveness without efficiency loses the industry reviewer for interactive-serving claims.
Protocol decisions to fix before running anything
- Split by time, and say so. Interaction data demands temporal splits;
document window boundaries and whether users span splits (see the manifest
pattern in
wsdm-reproducibility). If you must use a legacy leave-one-out protocol for comparability, run temporal as well and report both - protocols disagree often enough that the choice is a finding. - Define the candidate set. Sampled-negative evaluation (rank the true item against k sampled items) inflates and reorders metrics versus full ranking; state which you use and sample size. Mixing regimes across baselines invalidates the whole table.
- Fix the cutoff story. Report metrics at cutoffs that match the claimed surface (@5/@10 for user-facing ranking; deeper cutoffs need justification).
- Pick the unit of significance. Paired tests over queries/users/sessions, with the unit named. State the correction if testing many baselines.
Baseline selection
- Include the strongest recent method from the venue's own conversation - a 2027 submission compared only against pre-2023 baselines self-identifies as under-researched. Sweep the last two editions of WSDM, SIGIR, KDD, WWW, and RecSys for the topic's current best.
- Include the embarrassing-simple baseline (popularity, BM25, most-recent, logistic regression on hand features). WSDM reviewers are veterans of neural methods losing to tuned heuristics; showing the heuristic beaten builds trust, and omitting it suggests it wasn't.
- Tune baselines with the same budget as your method and say so - "default hyperparameters for baselines" is a read-and-reject phrase for this PC.
- For foundation-model comparisons, pin versions and dates and control the prompt-engineering effort across systems.
Show full SKILL.md (225 more words)Show less
Ablations that isolate the mechanism
The contribution sentence names a mechanism; the ablation table must isolate it. Pattern:
Full model 0.412
- remove the debiasing weight (the mechanism) 0.371 <- the claim's evidence
- remove auxiliary loss (engineering) 0.405
- replace learned propensity with uniform 0.383
Strongest baseline 0.379If removing the named mechanism hurts less than removing an engineering detail, the paper's story and its evidence disagree - fix the story or the method before a reviewer does it for you. Report ablations on more than one dataset when results are close; single-dataset ablations invite the "tuned on that set" read.
Efficiency and scale reporting
For any method aimed at ranking, retrieval, or serving:
- Report inference latency per query/user at a stated batch size and hardware, plus index or memory footprint next to effectiveness numbers.
- Separate one-time costs (training, index build) from per-request costs.
- Scale claims need a scaling curve (data or corpus size vs cost/quality), not
an adjective. "Web-scale" with a 100k-item experiment is a tells-table entry
in
wsdm-writing-stylefor a reason.
Online and production evidence
A/B results strengthen a WSDM paper when framed correctly: they are
attested evidence of deployment value (traffic share, duration, metric
definitions, guardrails - the protocol requirements in wsdm-reproducibility),
not a substitute for reproducible offline comparison. The clean pattern pairs
them: offline tables establish the method ranking on inspectable data; the
online section shows the offline win survived serving reality. State
discrepancies between the two honestly - the offline-online gap is itself a
finding this community values.
Pre-submission experiment audit
[ ] Temporal (or justified) splits, documented, leakage checks run
[ ] Candidate-set regime stated and uniform across systems
[ ] Baselines: recent-strong + simple-heuristic, tuning parity stated
[ ] Significance: paired test, named unit, cutoff-matched claims
[ ] Ablation isolates the *named* mechanism, multi-dataset if close
[ ] Efficiency: latency/memory beside effectiveness for serving claims
[ ] Robustness slice: cold-start or tail reported, not just aggregate
[ ] Every number in the abstract traceable to a tableOutput format
[Quadrants] effectiveness / validity / efficiency / robustness: covered or gap
[Protocol] split, candidate set, cutoffs, significance unit: <summary>
[Baselines] recency + simplicity + tuning parity: pass / additions needed
[Mechanism ablation] isolates claim: yes / story-evidence mismatch
[Online evidence] attested framing correct: yes / no / n-a
[Fix-first] the single highest-risk evaluation gap© 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 WSDM-Skills/skills/wsdm-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Wsdm 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 |
|---|---|---|---|---|---|---|
| Wsdm Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | 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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Experiment Designer
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Questions about Wsdm Experiments
What does Wsdm Experiments do?
A skill your agent uses when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs…. Wsdm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls, temporal-split protocols for interaction logs, baseline selection from recent WSDM/SIGIR/KDD editions, ablations that isolate the mechanism, efficiency reporting, and online-evidence framing.
When should I use Wsdm Experiments?
Wsdm Experiments fits situations like: auditing the evaluation of a WSDM paper - offline ranking and recommendation metrics with bias controls; temporal-split protocols for interaction logs; baseline selection from recent WSDM/SIGIR/KDD editions; ablations that isolate the mechanism.
How do I install Wsdm Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-experiments -a claude-code`. Or copy the skill folder (WSDM-Skills/skills/wsdm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/wsdm-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Wsdm Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-experiments -a codex`. Or copy the skill folder (WSDM-Skills/skills/wsdm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/wsdm-experiments in your project. Codex loads it when a task matches its description.
Can I use Wsdm 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 wsdm-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/wsdm-experiments, .gemini/skills/wsdm-experiments, .github/skills/wsdm-experiments and .opencode/skills/wsdm-experiments in your project.
What does Wsdm Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Wsdm Experiments is instructions for the agent only.
Does Wsdm 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 Wsdm 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 Wsdm Experiments use?
Wsdm 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 Wsdm Experiments use?
About 1.6k tokens (SKILL.md is roughly 6.3k 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 Wsdm Experiments?
Skills that share tags, products or a category with Wsdm 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 Wsdm 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.