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 empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned…
MITAuto-check passed
Install Icdm Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icdm-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/ICDM-Skills/skills/icdm-experiments .claude/skills/icdm-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 "icdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-experiments into .claude/skills/icdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icdm-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/ICDM-Skills/skills/icdm-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 icdm-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icdm-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/ICDM-Skills/skills/icdm-experiments .agents/skills/icdm-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 "icdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-experiments into .agents/skills/icdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icdm-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 icdm-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icdm-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/ICDM-Skills/skills/icdm-experiments .cursor/skills/icdm-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 "icdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-experiments into .cursor/skills/icdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icdm-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 ICDM-Skills/skills/icdm-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 icdm-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icdm-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/ICDM-Skills/skills/icdm-experiments .gemini/skills/icdm-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 "icdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-experiments into .gemini/skills/icdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icdm-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 icdm-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 icdm-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/ICDM-Skills/skills/icdm-experiments .github/skills/icdm-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 "icdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-experiments into .github/skills/icdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icdm-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 icdm-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 icdm-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/ICDM-Skills/skills/icdm-experiments .opencode/skills/icdm-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 "icdm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICDM-Skills/skills/icdm-experiments into .opencode/skills/icdm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icdm-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
icdm-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 495 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 empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned…
- Auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition
- SKILL.md covers Define the mining task before…, The four evidence axes, Baselines and tuning symmetry and Ablations that isolate the…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Strong and fairly-tuned baselines
What it does
Icdm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned baselines, ablations that isolate the named mechanism, scalability curves that test scale claims, and discovery-validity checks that separate real findings from evaluation artifacts.
Its SKILL.md is about 1.2k 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 empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition
- Strong and fairly-tuned baselines
- Ablations that isolate the named mechanism
- Scalability curves that test scale claims
Example prompts
- “/icdm-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
Icdm Experiments loads about 1.2k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 495 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). 495 words, ~1,184 tokens.
Download SKILL.mdSave it as
.claude/skills/icdm-experiments/SKILL.md (or your agent's skills folder).- name
- icdm-experiments
- description
- Use when designing or auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned baselines, ablations that isolate the named mechanism, scalability curves that test scale claims, and discovery-validity checks that separate real findings from evaluation artifacts.
ICDM Experiments
Design the evaluation an ICDM reviewer will trust: a defined mining task, baselines tuned as carefully as your method, ablations that isolate the mechanism, a measured scale story, and a discovery-validity argument. ICDM's data-centric reviewers punish leaderboard-only wins and un-checkable discovery claims, and the whole evaluation must fit inside the 10-page all-inclusive cap.
Define the mining task before the metric
- State the task operationally: inputs, outputs, and what a correct answer is. "Anomaly detection" is a genre; "rank edges by anomalousness in a one-pass stream, evaluated against injected ground truth" is a task.
- Fix the evaluation protocol — splits, negatives, thresholds, ranking cutoffs — before running anything, and describe it precisely enough to reproduce inside the page cap.
The four evidence axes
| Axis | Question it answers | Typical evidence |
|---|---|---|
| Quality | Is the mining result good on the task? | Ranking/accuracy vs baselines with variance |
| Scale | Does the scale claim hold? | Latency/memory curves across data sizes |
| Mechanism | Is the named mechanism the reason? | Ablations toggling exactly that component |
| Validity | Is the finding real, not an artifact? | Controlled injections, known-truth checks |
A strong ICDM paper touches all four; missing "mechanism" or "validity" is the usual reason a methodologically fine paper reads as thin.
Baselines and tuning symmetry
- Compare against current strong baselines, and tune them with the same budget you gave your method; an under-tuned baseline is the fastest way to lose reviewer trust.
- Include the obvious simple baseline. If a cheap method nearly matches you, say so and argue the regime where your mechanism pays off.
- Report every number with variance over seeded runs; a single-run table invites the "is this noise?" review.
Ablations that isolate the mechanism
The mechanism-attached novelty of icdm-writing-style must be demonstrated, not asserted.
Mechanism: single-pass isolation sketch with m random partitions.
Ablation grid:
- remove the sketch, keep full storage -> isolates the streaming contribution
- vary m (partition count) -> maps the accuracy/memory knob
- swap the hash family -> tests sensitivity to the mechanism's core
- replace isolation score with density score -> isolates the isolation principle
Each row answers "was THIS the reason it worked?"Show full SKILL.md (210 more words)Show less
Test the scale claim, do not assert it
- If you claim scalability, plot behavior across at least an order of magnitude of data size, and report the cost model (linear, sub-linear memory, amortized constant update).
- Separate wall-clock from asymptotic claims; hardware-dependent speedups need the hardware stated and, ideally, an operation count that is not hardware-dependent.
Discovery validity: the ICDM instinct
- Where truth is unknown, build a setting where it is: injected anomalies, planted patterns, synthetic graphs with known structure — so you can show the method recovers known signal.
- Guard against leakage: temporal tasks need time-respecting splits; graph tasks need to avoid train/test edge overlap. State the guard explicitly.
- Do not overclaim when differences are within variance; an honest "matches at lower cost" is stronger here than a fragile "outperforms."
Vignette: an ablation that saved the claim
A team reports strong stream-anomaly numbers but reviewers cannot tell whether the sketch or the underlying isolation criterion did the work. Adding two ablation rows — full-storage isolation (isolating the streaming contribution) and a density-score swap (isolating the isolation principle) — showed the sketch preserved batch quality while the isolation principle drove detection. The claim survived because the mechanism was shown, not stated, and both rows fit in the appendix inside the 10-page cap.
Output format
[Task] <operational task definition>
[Axes covered] quality / scale / mechanism / validity - list gaps
[Baselines] strong + tuned symmetrically: yes / no
[Ablation] isolates the named mechanism: yes / no
[Validity] known-truth or leakage-guarded: yes / no
[Top evidence gap] <single most important missing experiment>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in ICDM-Skills/skills/icdm-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Icdm 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 |
|---|---|---|---|---|---|---|
| Icdm Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | 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 Icdm Experiments
What does Icdm Experiments do?
A skill your agent uses when designing or auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned…. Icdm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned baselines, ablations that isolate the named mechanism, scalability curves that test scale claims, and discovery-validity checks that separate real findings from evaluation artifacts.
When should I use Icdm Experiments?
Icdm Experiments fits situations like: auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition; strong and fairly-tuned baselines; ablations that isolate the named mechanism; scalability curves that test scale claims.
How do I install Icdm Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-experiments -a claude-code`. Or copy the skill folder (ICDM-Skills/skills/icdm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/icdm-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Icdm Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-experiments -a codex`. Or copy the skill folder (ICDM-Skills/skills/icdm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/icdm-experiments in your project. Codex loads it when a task matches its description.
Can I use Icdm 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 icdm-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/icdm-experiments, .gemini/skills/icdm-experiments, .github/skills/icdm-experiments and .opencode/skills/icdm-experiments in your project.
What does Icdm Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Icdm Experiments is instructions for the agent only.
Does Icdm 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 Icdm 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 Icdm Experiments use?
Icdm 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 Icdm Experiments use?
About 1.2k tokens (SKILL.md is roughly 4.7k 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 Icdm Experiments?
Skills that share tags, products or a category with Icdm 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 Icdm 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.