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 IEEE INFOCOM evaluations, covering analytical results with stated and justified assumptions, simulation with a named simulator and logged seeds…
MITAuto-check passed
Install Infocom Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill infocom-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills infocom-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/INFOCOM-Skills/skills/infocom-experiments .claude/skills/infocom-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 "infocom-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INFOCOM-Skills/skills/infocom-experiments into .claude/skills/infocom-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infocom-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/INFOCOM-Skills/skills/infocom-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 infocom-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills infocom-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/INFOCOM-Skills/skills/infocom-experiments .agents/skills/infocom-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 "infocom-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INFOCOM-Skills/skills/infocom-experiments into .agents/skills/infocom-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infocom-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 infocom-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills infocom-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/INFOCOM-Skills/skills/infocom-experiments .cursor/skills/infocom-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 "infocom-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INFOCOM-Skills/skills/infocom-experiments into .cursor/skills/infocom-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infocom-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 INFOCOM-Skills/skills/infocom-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 infocom-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills infocom-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/INFOCOM-Skills/skills/infocom-experiments .gemini/skills/infocom-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 "infocom-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INFOCOM-Skills/skills/infocom-experiments into .gemini/skills/infocom-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infocom-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 infocom-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 infocom-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/INFOCOM-Skills/skills/infocom-experiments .github/skills/infocom-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 "infocom-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INFOCOM-Skills/skills/infocom-experiments into .github/skills/infocom-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infocom-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 infocom-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 infocom-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/INFOCOM-Skills/skills/infocom-experiments .opencode/skills/infocom-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 "infocom-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INFOCOM-Skills/skills/infocom-experiments into .opencode/skills/infocom-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "infocom-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
infocom-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 561 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 IEEE INFOCOM evaluations, covering analytical results with stated and justified assumptions, simulation with a named simulator and logged seeds…
- Auditing IEEE INFOCOM evaluations
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, Simulation and reproducibility… and Analytical-work floor, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering analytical results with stated and justified assumptions
What it does
Infocom Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing IEEE INFOCOM evaluations, covering analytical results with stated and justified assumptions, simulation with a named simulator and logged seeds, testbed and measurement studies with real traffic, honest and tuned baselines, and matching evidence to the shape of each networking claim across the analysis-simulation-testbed spectrum.
Its SKILL.md is about 1.4k 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 IEEE INFOCOM evaluations
- Covering analytical results with stated and justified assumptions
- Simulation with a named simulator and logged seeds
- Testbed and measurement studies with real traffic
Example prompts
- “/infocom-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
Infocom Experiments loads about 1.4k tokens when it runs. Until then it costs about 96 tokens; SKILL.md has 561 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). 561 words, ~1,358 tokens.
Download SKILL.mdSave it as
.claude/skills/infocom-experiments/SKILL.md (or your agent's skills folder).- name
- infocom-experiments
- description
- Use when designing or auditing IEEE INFOCOM evaluations, covering analytical results with stated and justified assumptions, simulation with a named simulator and logged seeds, testbed and measurement studies with real traffic, honest and tuned baselines, and matching evidence to the shape of each networking claim across the analysis-simulation-testbed spectrum.
INFOCOM Experiments
Use this before submission when the evaluation is not yet locked. INFOCOM reviewers span the full analysis → simulation → testbed spectrum, and the evaluation is where a good idea is won or lost — especially since there is (traditionally) no rebuttal to repair a weak comparison. The organizing principle is evidence proportional to the claim, delivered by the method the claim demands: a bound needs a proof, a design needs a fair experiment, a behavior claim needs measurement.
Evaluation audit
- Match the method to the claim. An optimality/approximation claim needs a proof under stated assumptions; a performance claim needs a fair experiment; a real-world behavior claim needs measurement on real traffic. A simulation cannot substitute for a proof, and a proof under unrealistic assumptions cannot substitute for an experiment.
- State and justify every assumption. For analytical work, list the assumptions (A1, A2, ...) and defend each; a theorem true only under an assumption no network satisfies is a scored weakness a reviewer will name — and you cannot rebut it.
- Name the simulator and pin the setup. State whether it is ns-3, OMNeT++, a custom simulator, or a testbed; report the topology, traffic model, number of runs, seeds, and confidence intervals. "A large-scale simulation" with no setup is not evidence.
- Choose honest baselines, including the strongest prior scheme and a simple-but-reasonable alternative, each tuned with a documented, equal budget. An untuned baseline is the classic INFOCOM reject.
- Report variance, not point estimates. Confidence intervals over multiple seeded runs; appropriate statistics; say what the intervals represent.
- For testbeds/measurement, describe the hardware, the trace or workload, the collection methodology, and the confounds — and bound them.
Claim-to-evidence design table
| Networking claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Algorithm is near-optimal" | Proof of an approximation/competitive ratio under stated assumptions | "Only shown on examples the authors chose" |
| "Scheme improves throughput/delay" | Seeded simulation or testbed vs. a tuned baseline, with CIs | "Untuned baseline; single run; no variance" |
| "Scales to large networks" | Runtime/quality across realistic topology sizes | "Only a 10-node topology tested" |
| "Holds in real deployments" | Testbed or trace-driven measurement with real traffic | "Synthetic traffic claimed to generalize" |
| "The learner adds the value" | Ablation vs. a non-ML heuristic on the same inputs | "ML component's marginal value never isolated" |
Show full SKILL.md (193 more words)Show less
Simulation and reproducibility floor
[Simulator] name it (ns-3/OMNeT++/custom); pin the version; describe the model, not just the tool
[Topology] real or realistic (e.g., from a topology dataset); state size and generation method
[Traffic] the workload/trace and its source; synthetic models justified against real behavior
[Runs/seeds] multiple seeded runs; report the number and log the seeds; give confidence intervals
[Baselines] the strongest competitor + a simple one, each tuned with a documented equal budget
[Compute] the runtime/scale actually reached, not vague feasibility languageAnalytical-work floor
- List assumptions explicitly and justify each against real network conditions.
- Give proof sketches in the body and full proofs in a tight in-budget appendix (it counts toward the nine pages) or state where the full proof lives.
- Validate the theory in simulation: show the bound is tight/loose where the analysis predicts.
Vignette: evaluating a caching/offloading policy
Suppose the paper claims an online caching policy with a competitive ratio that also beats prior heuristics. The matching plan: prove the ratio under stated assumptions; then simulate on a real request trace with multiple seeds, comparing against the strongest prior policy tuned with an equal budget and a simple LRU baseline; report hit-rate and delay with confidence intervals; show where the empirical gap matches the analytical bound; and state the regime (skewed vs. uniform demand) where the guarantee is loose — every number traceable to a logged run.
Statistical reporting floor
- Confidence intervals over seeded runs for every simulated/measured comparison; say what they represent.
- The number of runs and the source of randomness for any stochastic component.
- The topology sizes and traffic models actually used, not vague "large-scale" language.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> method map] <claim: proof | simulation | testbed/measurement>
[Assumptions] <listed and justified? yes/no>
[Simulator/setup] <named? seeds logged? CIs reported? yes/no>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Decision-critical next run] <one experiment or proof to add before the deadline>© 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 INFOCOM-Skills/skills/infocom-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Infocom 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 |
|---|---|---|---|---|---|---|
| Infocom Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | 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 Infocom Experiments
What does Infocom Experiments do?
A skill your agent uses when designing or auditing IEEE INFOCOM evaluations, covering analytical results with stated and justified assumptions, simulation with a named simulator and logged seeds…. Infocom Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing IEEE INFOCOM evaluations, covering analytical results with stated and justified assumptions, simulation with a named simulator and logged seeds, testbed and measurement studies with real traffic, honest and tuned baselines, and matching evidence to the shape of each networking claim across the analysis-simulation-testbed spectrum.
When should I use Infocom Experiments?
Infocom Experiments fits situations like: auditing IEEE INFOCOM evaluations; covering analytical results with stated and justified assumptions; simulation with a named simulator and logged seeds; testbed and measurement studies with real traffic.
How do I install Infocom Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill infocom-experiments -a claude-code`. Or copy the skill folder (INFOCOM-Skills/skills/infocom-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/infocom-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Infocom Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill infocom-experiments -a codex`. Or copy the skill folder (INFOCOM-Skills/skills/infocom-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/infocom-experiments in your project. Codex loads it when a task matches its description.
Can I use Infocom 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 infocom-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/infocom-experiments, .gemini/skills/infocom-experiments, .github/skills/infocom-experiments and .opencode/skills/infocom-experiments in your project.
What does Infocom Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Infocom Experiments is instructions for the agent only.
Does Infocom 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 Infocom 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 Infocom Experiments use?
Infocom 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 Infocom Experiments use?
About 1.4k tokens (SKILL.md is roughly 5.4k 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 Infocom Experiments?
Skills that share tags, products or a category with Infocom 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 Infocom 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.