Finding Experiments
PostHog/posthog
Resolves a PostHog experiment reference from natural language to a concrete experiment ID by browsing experiment-list (not feature-flag tools), with disambiguation when multiple experiments match.
A skill your agent uses 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…
MITAuto-check passed
Install Colt Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-experiments .claude/skills/colt-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 "colt-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-experiments into .claude/skills/colt-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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/COLT-Skills/skills/colt-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 colt-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-experiments .agents/skills/colt-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 "colt-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-experiments into .agents/skills/colt-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 colt-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-experiments .cursor/skills/colt-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 "colt-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-experiments into .cursor/skills/colt-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 COLT-Skills/skills/colt-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 colt-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-experiments .gemini/skills/colt-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 "colt-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-experiments into .gemini/skills/colt-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 colt-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 colt-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/COLT-Skills/skills/colt-experiments .github/skills/colt-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 "colt-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-experiments into .github/skills/colt-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 colt-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 colt-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/COLT-Skills/skills/colt-experiments .opencode/skills/colt-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 "colt-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-experiments into .opencode/skills/colt-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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
colt-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 788 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses 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…
- Deciding whether a COLT (Conference on Learning Theory) paper needs numerical content at all — COLT has no experiments requirement — and
- SKILL.md covers Should this paper contain…, Design rules when numerics…, A rate-illustration recipe and Separation and…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Numerics genuinely help
What it does
Colt Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.7k 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
- Deciding whether a COLT (Conference on Learning Theory) paper needs numerical content at all — COLT has no experiments requirement — and
- Numerics genuinely help
- Designing small illustrative simulations that visualize a proved bound
- A phase transition without diluting the theory
Example prompts
- “/colt-experiments”
Requirements
- Python 3
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 (its code samples are python).
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
Colt Experiments loads about 1.7k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 788 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). 788 words, ~1,686 tokens.
Download SKILL.mdSave it as
.claude/skills/colt-experiments/SKILL.md (or your agent's skills folder).- name
- colt-experiments
- description
- 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.
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 simulate |
If the answer is no, spend the pages on proof overviews instead; a decorative benchmark table in a COLT submission signals venue confusion and can lower reviewer confidence.
Design rules when numerics earn their place
- Simulate the theorem's exact regime: the assumptions, the adversary, the parameter ranges. An illustration outside the proved regime must be labeled exploratory.
- Prefer synthetic constructions where ground truth is computable; COLT illustrations are about visualizing mathematics, not about datasets.
- Overlay the proved bound on the empirical curve so the reader sees slope agreement, constant gap, and where finite-size effects fade.
- Report the estimator of variability (bars = standard error over R runs, stated in the caption), fixed seeds, and replication counts — theorists distrust single trajectories on principle.
- Keep total numerical content to roughly a figure or two; the appendix holds the procedure description (no code-upload channel existed in the 2026 cycle).
A rate-illustration recipe
The canonical COLT figure is a log-log rate check — empirical error or regret against the driving parameter, with the theoretical slope for reference:
import numpy as np
rng = np.random.default_rng(seed=2026) # fixed, reported seed
ns, R = np.logspace(2, 5, 8).astype(int), 50 # sample sizes, replications
emp = np.array([[run_once(n, rng) for _ in range(R)] for n in ns])
mean, se = emp.mean(axis=1), emp.std(axis=1, ddof=1) / np.sqrt(R)
slope = np.polyfit(np.log(ns), np.log(mean), 1)[0]
print(f"fitted slope {slope:.3f} vs. theoretical -1/2")
# plot log-log with se bars; overlay C * n**(-0.5) reference lineThe caption must state: the model matches Assumptions 1-2, R = 50 replications, bars are ±1 standard error, and the reference line is the Theorem 1 rate with fitted constant. A fitted slope of −0.48 against a proved −1/2 is a persuasive picture; a slope of −0.7 is a finding you must discuss (constants regime? bound loose? bug?) rather than hide.
Separation and phase-transition pictures
- For a separation between two models or algorithm classes, plot both behaviors on the same instance family so the divergence is visual, and print the instance-family parameters in the caption.
- For a threshold phenomenon (learnable iff α > α*), sweep across the threshold with enough resolution to show the transition sharpening as n grows — a single n is not a phase-transition picture.
- Adversarial constructions from lower-bound proofs often make the best instances: simulating your own hard instance shows it is concretely hard, not just asymptotically.
Show full SKILL.md (312 more words)Show less
Placement and captioning inside the paper
- Body placement: at most one illustration figure in the 12-page body, positioned
next to the theorem it visualizes, never in the introduction as decoration; the
remaining panels and the full procedure live in a terminal appendix section
(
colt-supplementarykeeps numerics out of the proof flow). - A COLT-grade caption is self-sufficient: instance family and parameters, the assumptions regime, replication count, what the bars are, what the reference line is, and the one-clause takeaway ("empirical slope matches the Theorem 1 rate").
- In-text discussion of the figure belongs in a Remark or a short subsection titled for what it shows ("Illustration of the separation"), keeping the paper's logical skeleton purely deductive.
- If space runs out, the figure is the first cut — state the numerical finding in one sentence with a pointer to the appendix, and spend the recovered half page on a proof overview.
Honesty rules that theorist reviewers enforce
- Never claim empirical support for regimes the theorem does not cover without the word "conjecture" or "exploratory" attached.
- Never tune the illustration (seeds, instances) to flatter the bound; if fluctuations are visible, show them.
- If the observed constants are large, say so — "the constant in Theorem 1 is pessimistic; empirically C ≈ 3" is a respected sentence at this venue.
- A numerical section may not compensate for a proof gap, and reviewers will not trade one for the other.
Cycle-volatility warnings
- The no-experiments-expected norm is structural to COLT, but scope language and any code policy are re-stated each cycle in the CFP (待核实 annually).
- Reviewer appetite for numerics varies by subfield: optimization and RL-theory reviewers often welcome a good rate plot; pure statistical-learning reviewers frequently prefer the pages spent on proofs. Calibrate to your subject area.
- If your empirical component outgrows illustration into contribution, re-run venue
selection (
colt-topic-selection) — the paper may be drifting toward NeurIPS, ICML, or AISTATS.
Output format
[Numerics verdict] none needed / illustration justified / empirical core (re-route?)
[Regime match] simulation inside proved regime / exploratory (labeled?)
[Figure plan] <rate plot / separation / phase transition; caption contents>
[Statistical floor] seeds, replications, bars defined
[Honesty check] <any claim exceeding the theorems>© 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 COLT-Skills/skills/colt-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Colt 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 |
|---|---|---|---|---|---|---|
| Colt Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Finding ExperimentsPostHog/posthog | 40k | — | ~783 | Automated safety check: Pass | Custom licence | |
| ExperimentsArize-ai/phoenix | 12k | — | ~1.8k | Automated safety check: Pass | Custom licence | |
| Scroll Experiencesickn33/agentic-awesome-skills | 47k | 2 repos | ~534 | Automated safety check: Pass | MIT | |
| Webgl Experiencenexu-io/open-design | 100k | — | ~903 | Automated safety check: Pass | Apache-2.0 | |
| Creating ExperimentsPostHog/posthog | 40k | — | ~2.7k | Automated safety check: Pass | Custom licence |
Similar skills
- Official
Experiments
Arize-ai/phoenix
Run, read, and compare dataset-backed experiments to find evidence that a prompt or pipeline is improving.
Scroll Experience
sickn33/agentic-awesome-skills
Expert in building immersive scroll-driven experiences - parallax storytelling, scroll animations, interactive narratives, and cinematic web experiences.
Webgl Experience
nexu-io/open-design
A full-screen, real-time WebGL/WebGL2 experience — animated shaders, 3D scenes, generative visuals, particle fields — rendered live on the GPU with a typographic overlay.
- Official
Creating Experiments
PostHog/posthog
Guides agents through experiment creation: reading the project's setup with experiment-setup-context, defining the hypothesis, configuring rollout and bucketing, setting up analytics and running…
Scroll Experience
davila7/claude-code-templates
Expert in building immersive scroll-driven experiences - parallax storytelling, scroll animations, interactive narratives, and cinematic web experiences.
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Questions about Colt Experiments
What does Colt Experiments do?
A skill your agent uses 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…. Colt Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Colt Experiments?
Colt Experiments fits situations like: deciding whether a COLT (Conference on Learning Theory) paper needs numerical content at all — COLT has no experiments requirement — and; numerics genuinely help; designing small illustrative simulations that visualize a proved bound; A phase transition without diluting the theory.
How do I install Colt Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-experiments -a claude-code`. Or copy the skill folder (COLT-Skills/skills/colt-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/colt-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Colt Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-experiments -a codex`. Or copy the skill folder (COLT-Skills/skills/colt-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/colt-experiments in your project. Codex loads it when a task matches its description.
Can I use Colt 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 colt-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/colt-experiments, .gemini/skills/colt-experiments, .github/skills/colt-experiments and .opencode/skills/colt-experiments in your project.
What does Colt Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Colt Experiments is instructions for the agent only. Our summary lists: Python 3.
Does Colt 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 Colt 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 Colt Experiments use?
Colt 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 Colt Experiments use?
About 1.7k tokens (SKILL.md is roughly 6.7k 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 Colt Experiments?
Skills that share tags, products or a category with Colt Experiments: Finding Experiments (PostHog/posthog, 40k stars), Experiments (Arize-ai/phoenix, 12k stars), Scroll Experience (sickn33/agentic-awesome-skills, 47k stars) and Webgl Experience (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Colt 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.