colt-experiments
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# COLT Experiments Start from the venue truth: **COLT imposes no experiments requirement, and most COLT papers contain no experiments.** The 2026 CFP (checked 2026-07-08) asks for theoretical machine-learning contributions and mentions no empirical-evaluation expectation; papers are accepted on theorems. The decision this skill supports is therefore *whether* to include numerics, and only then *how*. ## Should this paper contain numerics at all? | Situation | Include numerics? | Rationale | |---|---|---| | Clean upper/lower bound pair, standard model | No | Plots add length, not belief | | New algorithm whose practicality is part of the pitch | Small illustration | Shows the constants are not absurd | | Theory explaining an empirical phenomenon (in-scope per the CFP's inclusive view) | Yes, essential | The phenomenon must be exhibited, then explained | | Conjectured tightness you cannot prove | Careful, labeled | A scaling plot can support a conjecture — never upgrade it | | Phase transition / separation between models | Often worthwhile | A picture of the transition is the most readable evidence | | Purely structural result (equivalences, characterizations) | No | Nothing to simul
- Should this paper contain numerics at all?
- Design rules when numerics earn their place
- A rate-illustration recipe
- Separation and phase-transition pictures
- Placement and captioning inside the paper
- Honesty rules that theorist reviewers enforce
- Cycle-volatility warnings
- Output format
What does the colt-experiments skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.