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 experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers…
MITAuto-check passed
Install Cvpr Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-experiments .claude/skills/cvpr-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 "cvpr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-experiments into .claude/skills/cvpr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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/CVPR-Skills/skills/cvpr-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 cvpr-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-experiments .agents/skills/cvpr-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 "cvpr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-experiments into .agents/skills/cvpr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-experiments .cursor/skills/cvpr-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 "cvpr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-experiments into .cursor/skills/cvpr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 CVPR-Skills/skills/cvpr-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 cvpr-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-experiments .gemini/skills/cvpr-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 "cvpr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-experiments into .gemini/skills/cvpr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-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 cvpr-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/CVPR-Skills/skills/cvpr-experiments .github/skills/cvpr-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 "cvpr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-experiments into .github/skills/cvpr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-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 cvpr-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/CVPR-Skills/skills/cvpr-experiments .opencode/skills/cvpr-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 "cvpr-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-experiments into .opencode/skills/cvpr-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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
cvpr-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 732 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 experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers…
- Works in 5 steps: List the tables and figures the argument… → Price each in GPU-hours using a pilot… → Spend on order-of-information value: the… → …
- Auditing the experimental program of a CVPR paper
- SKILL.md covers Does it work: main comparisons, Why it works: the ablation is…, When it fails: qualitative… and What it costs: efficiency as a…, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Cvpr Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers treat as mandatory, qualitative and failure-case evidence, efficiency metrics tied to the Compute Reporting Form, and generalization tests beyond a single dataset.
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 experimental program of a CVPR paper
- Covering benchmark and baseline selection under matched-compute fairness
- The ablation study reviewers treat as mandatory
- Qualitative and failure-case evidence
Example prompts
- “/cvpr-experiments”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- List the tables and figures the argument needs (main comparison, decisive ablation,
- Price each in GPU-hours using a pilot run; the totals routinely show the "nice to
- Spend on order-of-information value: the ablation that could falsify the core
- Reserve 15–20% of budget for rebuttal week; the most common January request is a
- Log everything into the recipe ledger (cvpr-reproducibility) as it runs; the CRF
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
Cvpr Experiments loads about 1.6k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 732 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). 732 words, ~1,630 tokens.
Download SKILL.mdSave it as
.claude/skills/cvpr-experiments/SKILL.md (or your agent's skills folder).- name
- cvpr-experiments
- description
- Use when designing or auditing the experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers treat as mandatory, qualitative and failure-case evidence, efficiency metrics tied to the Compute Reporting Form, and generalization tests beyond a single dataset.
CVPR Experiments
CVPR runs on benchmark evidence: reviewers at the 2026 edition sorted 16,092 submissions largely by asking "do the tables prove the sentence?" This skill designs an experimental program that answers the four questions every vision review implicitly asks — does it work, why does it work, when does it fail, and what does it cost.
Does it work: main comparisons
- Benchmarks: use the datasets your subfield's last two cycles used, current versions, standard splits. A new task may justify a new benchmark, but then the benchmark itself becomes a contribution needing validation (and, if claimed as one, public release by camera-ready — verified 2026 policy).
- Baselines: the leaderboard's current top methods plus the strongest simple baseline. The comparison that kills papers in review is the one you omitted because it was too strong.
- Fairness: match backbones, pretraining corpora, input resolution, and training schedule wherever possible — or tabulate the mismatch explicitly. Beating a ResNet-era method with a ViT-L and calling it method innovation is the single most common CVPR review objection.
- Provenance: mark which numbers are re-runs under your protocol vs. quoted from papers; asterisk and footnote the re-runs.
Why it works: the ablation is not optional
Vision reviewers treat ablations as the paper's proof of understanding. Structure the grid so each row removes or replaces exactly one design decision:
# ablation-matrix.txt — one experiment per line, one variable per experiment
A0 full method (reference row)
A1 - temporal attention → per-frame baseline tests the core claim
A2 - our loss → standard L1 is the loss or the architecture doing it?
A3 - pretrain → from scratch how much rides on initialization?
A4 swap: our module in baseline X does the gain transfer?
A5 sensitivity: key hyperparameter sweep is A0 a lucky point?Rows A4 (transplant) and A5 (sensitivity) separate memorable ablation sections from perfunctory ones. Every ablation row cited in prose belongs in the 8-page body; the long grid goes to the supplement.
When it fails: qualitative evidence with integrity
Cherry-picked grids convince nobody at a venue that invented the genre. The credible pattern:
| Qualitative element | Purpose |
|---|---|
| Random (or id-listed) sample grid | Shows typical, not best-case, behavior |
| Side-by-side vs. two strongest baselines | Same inputs, aligned crops, labeled columns |
| Failure cases with a taxonomy | "Fails under occlusion and low light" beats silence |
| Video for anything temporal | In the supplement — external links are banned |
State the selection rule in the caption ("first 8 validation images", "random seed 0"). A stated rule converts pretty pictures into evidence.
What it costs: efficiency as a first-class axis
The 2026 cycle made compute visible venue-wide via the mandatory Compute Reporting Form (hardware + verification sections required; deeper compute sections optional but tied to recognition badges). Align the paper with the form: report params, FLOPs, latency (named hardware, batch size, resolution), and training GPU-hours for your method and re-run baselines where you can. "Real-time" with no hardware named contradicts your own CRF and reviewers can now check.
Show full SKILL.md (316 more words)Show less
Generalization: the second-dataset rule
A method shown on one dataset is a result about that dataset. Cheap robustness
evidence reviewers reward: evaluate the trained model on a second domain without
retuning; report cross-dataset transfer; if your field has corruption/shift suites, run
them. One honest sentence about where transfer degrades is worth more than a defensive
omission — it becomes your limitations paragraph (see cvpr-writing-style).
Budgeting the program before running it
At CVPR-scale training costs, experiment selection is a resource-allocation problem. Plan the program backward from the paper's skeleton:
- List the tables and figures the argument needs (main comparison, decisive ablation, efficiency table, qualitative grid, one transfer result).
- Price each in GPU-hours using a pilot run; the totals routinely show the "nice to have" grid costing more than the flagship.
- Spend on order-of-information value: the ablation that could falsify the core claim runs first, not last — discovering in October that A1 ≈ A0 should redirect the project, not decorate it.
- Reserve 15–20% of budget for rebuttal week; the most common January request is a small matched-setting re-run, and having compute standing by converts a weakness into a mini-table.
- Log everything into the recipe ledger (
cvpr-reproducibility) as it runs; the CRF optional sections then fill themselves.
Statistical care, vision edition
Multi-seed everything is unaffordable at modern budgets; the defensible pattern is
multi-seeded cheap experiments with mean ± std, a single flagged flagship run, and no
headline claims resting on differences smaller than observed seed noise (full protocol
in cvpr-reproducibility). For generative work, repeat evaluation sampling; for
detection, fix and disclose the exact mAP implementation.
Reverify each cycle
- Standard splits and evaluation-server rules for your benchmarks (some test sets are
withheld; see
cvpr-reproducibilityfor submission-server discipline). - CRF structure and any new compute/efficiency reporting requirements.
- Benchmark leaderboard state the month before the deadline.
- Any dataset-ethics or human-data review requirements added to the form (待核实 each cycle).
Output format
[Evidence audit] works / why / fails / costs — covered?
[Comparison risks] unmatched: <backbone/pretrain/resolution/schedule>
[Ablation] rows isolating single factors: <n>; transplant + sensitivity present?
[Qualitative] selection rule stated? failures taxonomized?
[Efficiency] params/FLOPs/latency/GPU-hours vs CRF: consistent?
[Priority additions] <ordered by review impact>© 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 CVPR-Skills/skills/cvpr-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cvpr 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 |
|---|---|---|---|---|---|---|
| Cvpr 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 Designeralirezarezvani/claude-skills | 28k | 1 repos | ~783 | Automated safety check: Pass | MIT | |
| Experimental Designaiming-lab/AutoResearchClaw | 15k | — | ~286 | Automated safety check: Pass | MIT | |
| 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 |
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alirezarezvani/claude-skills
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Experimental Design
aiming-lab/AutoResearchClaw
Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw.
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
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Questions about Cvpr Experiments
What does Cvpr Experiments do?
A skill your agent uses when designing or auditing the experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers…. Cvpr Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers treat as mandatory, qualitative and failure-case evidence, efficiency metrics tied to the Compute Reporting Form, and generalization tests beyond a single dataset.
When should I use Cvpr Experiments?
Cvpr Experiments fits situations like: auditing the experimental program of a CVPR paper; covering benchmark and baseline selection under matched-compute fairness; the ablation study reviewers treat as mandatory; qualitative and failure-case evidence.
How do I install Cvpr Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-experiments -a claude-code`. Or copy the skill folder (CVPR-Skills/skills/cvpr-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cvpr-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Cvpr Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-experiments -a codex`. Or copy the skill folder (CVPR-Skills/skills/cvpr-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cvpr-experiments in your project. Codex loads it when a task matches its description.
Can I use Cvpr 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 cvpr-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/cvpr-experiments, .gemini/skills/cvpr-experiments, .github/skills/cvpr-experiments and .opencode/skills/cvpr-experiments in your project.
What does Cvpr Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Cvpr Experiments is instructions for the agent only.
Does Cvpr 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 Cvpr 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 Cvpr Experiments use?
Cvpr 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 Cvpr Experiments use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Cvpr Experiments?
Skills that share tags, products or a category with Cvpr Experiments: Design Audit Against Rams' Principles (thedotmack/claude-mem, 98k stars), Experiment Designer (alirezarezvani/claude-skills, 28k stars), Experimental Design (aiming-lab/AutoResearchClaw, 15k stars) and Experiment Audit (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cvpr 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.