colt-experiments
From the author
Use when deciding whether a COLT (Conference on Learning Theory) paper needs numerical content at all — COLT has no experiments requirement — and, when numerics genuinely help, designing small illustrative simulations that visualize a proved bound, a separation, or a phase transition without diluting the theory.
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-experiments -a claude-code
Installs to .claude/skills/colt-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.
- stoc-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when judging whether computation belongs in a STOC (ACM Symposium on Theory of Computing) paper at all — STOC accepts on theorems, with no empirical-evaluation expectation — and when it does, scoping it as certified proof computation, constructive search, or clearly labeled illustration rather than benchmarking.Media★ 1.3K
- itcs-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare, well-scoped illustrative computation or simulation — and how to keep any computational content checkable and subordinate to the mathematics.Media★ 1.3K
- focs-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when deciding whether and how computation appears in a FOCS (IEEE Symposium on Foundations of Computer Science) paper — a venue that accepts on theorems with no evaluation section expected — covering machine-verified case analyses, computer-discovered constructions, and honest illustrative plots.Media★ 1.3K
- corl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing experiments for a CoRL robot-learning paper — seeds and evaluation-episode counts, task-suite breadth, real-robot versus simulation evidence, sim-to-real gap measurement, baseline fairness across BC/RL/VLA families, generalization splits, and statistics for success-rate claims.Science★ 1.3K
- colm-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the empirical core of a COLM paper — contamination analysis for evaluation data, fair baselines under matched prompting and compute, pinned model versions and decoding parameters, uncertainty over runs and samples, scaling coverage, and honest reporting of API-model comparisons.Science★ 1.3K
- popl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing the empirical component of a POPL paper — deciding whether evidence should be a mechanization, a prototype, case studies, or benchmarks; reporting proof effort and case-study coverage honestly; and keeping performance numbers in a supporting role so the formal claim stays the paper's center of gravity.Science★ 1.3K
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in