cvpr-experiments
From the author
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.
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-experiments -a claude-code
Installs to .claude/skills/cvpr-experiments/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
Want your agent to suggest skills while you type? See how to use skills from your IDE.
Safety checks on SKILL.md
No warnings.
Why 88
aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.
Similar skills
Closest to this one by what they do and when they are used.
- iclr-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.Other★ 1.3K
- acl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.Science★ 1.3K
- eccv-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the experimental program of an ECCV paper — benchmark selection that survives a September conference, matched-substrate baseline fairness in the foundation-model era, ablations that isolate the claimed mechanism, qualitative failure evidence, and run sequencing toward a March freeze.Science★ 1.3K
- icse-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as SE reviewers expect them, qualitative-methods rigor, ablations for AI-based techniques, and threats-driven study design.Testing★ 1.3K
- cav-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers with pinned versions and equal resource limits, timeout-dominated comparisons, soundness cross-checks and proof witnesses, cactus/scatter reporting, and matching evidence to the shape of each verification claim.Other★ 1.3K
- vldb-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.Science★ 1.3K
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in