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A skill your agent uses when deciding whether a result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC/FOCS…
Install Colt Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-topic-selection --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-topic-selection .claude/skills/colt-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-topic-selection into .claude/skills/colt-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-topic-selection", 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-topic-selectionType 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-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-topic-selection --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-topic-selection .agents/skills/colt-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-topic-selection into .agents/skills/colt-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-topic-selection", 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-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-topic-selection --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-topic-selection .cursor/skills/colt-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-topic-selection into .cursor/skills/colt-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-topic-selection", 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-topic-selection--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-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-topic-selection --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-topic-selection .gemini/skills/colt-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-topic-selection into .gemini/skills/colt-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-topic-selection", 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-topic-selectionInstalls 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-topic-selection -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-topic-selection .github/skills/colt-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-topic-selection into .github/skills/colt-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-topic-selection", 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-topic-selection -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-topic-selection --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-topic-selection .opencode/skills/colt-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-topic-selection into .opencode/skills/colt-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-topic-selection", 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-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 828 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 result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC/FOCS…
- Works in 3 steps: Is the headline sentence a mathematical… → Would a learning theorist care before… → Does the proof carry the weight? If the…
- NeurIPS/ICML/AISTATS
- SKILL.md covers The three-question fit test, Routing table, The open-problem vehicle and Scope self-interrogation, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Colt Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC/FOCS, NeurIPS/ICML/AISTATS, JMLR, or a statistics journal, and whether the right vehicle is a full paper or a COLT open-problem piece.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Statistics. 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
- NeurIPS/ICML/AISTATS
- A statistics journal
- Whether the right vehicle is a full paper
- A COLT open-problem piece
Example prompts
- “/colt-topic-selection”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Is the headline sentence a mathematical statement? "We prove the first
- Would a learning theorist care before seeing experiments? COLT reviewers
- Does the proof carry the weight? If the technique is assembly of known parts,
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
Colt Topic Selection loads about 1.9k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 828 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). 828 words, ~1,856 tokens.
Download SKILL.mdSave it as
.claude/skills/colt-topic-selection/SKILL.md (or your agent's skills folder).- name
- colt-topic-selection
- description
- Use when deciding whether a result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC/FOCS, NeurIPS/ICML/AISTATS, JMLR, or a statistics journal, and whether the right vehicle is a full paper or a COLT open-problem piece.
COLT Topic Selection
Use this before writing begins. COLT solicits papers on theoretical aspects of machine learning, described in the 2026 CFP (checked 2026-07-08) as a subject at the intersection of computer science, statistics, and applied mathematics, with an explicitly inclusive view that includes theory shedding light on empirical phenomena. The practical bar: the contribution must be a theorem — a rate, a separation, a characterization, a hardness result, or an algorithm whose guarantee is the point.
The three-question fit test
- Is the headline sentence a mathematical statement? "We prove the first $O(\sqrt{T})$ regret bound for X" is COLT-shaped. "We propose a method that empirically improves X" is not, regardless of how much analysis decorates it.
- Would a learning theorist care before seeing experiments? COLT reviewers evaluate the result on the model's motivation and the bound's strength alone.
- Does the proof carry the weight? If the technique is assembly of known parts, the result must be strong enough to stand without technique credit; if the result is modest, the technique must be the contribution — one of the two must be true.
Routing table
| Signal in the project | Best venue reading |
|---|---|
| Regret/sample-complexity/oracle-complexity bound, new or improved rate | Core COLT |
| Matching lower bound via a new instance construction | Core COLT |
| Theory explaining a deep-learning phenomenon, theorem-first | COLT (in-scope by the CFP's inclusive view) or ML-conference theory track |
| Learning theory with a long, self-contained development (60+ pages of ideas, not just proofs) | JMLR or Annals of Statistics — journal-length exposition |
| Algorithmic result where combinatorial/complexity machinery dominates the learning content | STOC / FOCS / SODA |
| Learning theory, but the community fit is the smaller algorithmic-learning-theory circuit | ALT (sister venue, autumn deadline cycle — verify) |
| Theorems plus substantial experiments as co-equal evidence | AISTATS or NeurIPS/ICML |
| Statistical methodology with inference guarantees and applied audience | AISTATS or a statistics journal |
| Probabilistic/Bayesian modeling contribution, uncertainty-first | UAI |
| A precise, motivated question you cannot answer | COLT Open Problem piece (see below) |
The open-problem vehicle
COLT has a tradition of publishing short open-problem pieces in its proceedings — citable, reviewed, and historically influential (verified instance: Agarwal, Krishnamurthy, Langford, Luo & Schapire, "Open Problem: First-Order Regret Bounds for Contextual Bandits," COLT 2017, PMLR v65:4-7; several such problems have been resolved by later full papers). In the 2025 cycle the format was: at most 4 pages excluding references, title beginning "Open Problem:", non-anonymous, submitted via CMT on its own timeline. Whether and how the track runs in your cycle: 待核实 in the current CFP.
Choose the open-problem route when you can state the question with full formality, prove the easy directions, explain why standard techniques fail, and ideally attach a modest prize of honor (tradition, not requirement). It converts a stalled project into community agenda-setting.
Scope self-interrogation
Q1. State the main claim as: quantifier prefix + model + bound/separation.
-> Cannot? The project is not yet a COLT project; it is a research direction.
Q2. Name the nearest prior theorem and your delta type
(gap-closing / log-removal / assumption-weakening / new-model separation).
-> No nameable neighbor? Either the model is unmotivated or the search
is incomplete -- both are pre-writing problems.
Q3. Is every experiment you are planning deletable without weakening the claim?
-> If deleting them guts the paper, route to AISTATS/NeurIPS/ICML instead.
Q4. Will the proof survive a hostile expert with unlimited appendix access?
-> "Probably" means the verification pass comes before the venue decision.Show full SKILL.md (378 more words)Show less
Vignette: three fates for one project
A team analyzes gradient descent on a two-layer network and can prove convergence to a global minimum under an over-parameterization condition.
- As stands — plausible COLT: the headline is a theorem about a practical algorithm, in-scope under the CFP's "theory that sheds light on empirical phenomena." The COLT version leads with the convergence rate, the over-parameterization threshold, and the technique that beats prior NTK-style arguments; the experiment section shrinks to one illustrative training curve.
- Weakened theory, strong benchmarks — misroute: if the honest version needs assumptions no practical network meets and the interesting content is empirical, the NeurIPS/ICML framing (empirical contribution, theory as support) is both more honest and more likely to succeed.
- Question sharpened, proof missing — open problem: if the threshold conjecture resists proof but can be stated exactly, a COLT open-problem piece stating the conjecture, the partial results, and why current techniques fail converts the stall into a citable contribution.
Common misroutes seen at COLT
- The "theory-flavored systems paper": an algorithm with a convergence guarantee under assumptions the target application violates, pitched as theory. Reviewers ask what the theorem teaches; have an answer that is not the benchmark table.
- The "known result in new clothes": a bound classical in sequential analysis or
empirical-process theory, rediscovered. The statistics lane of
colt-related-workexists to catch this before a reviewer does. - The "journal paper in a 12-page costume": a development whose value is the full
landscape, mutilated to fit. If the body cannot carry the spine (see
colt-writing-style), choose JMLR. - The "two-community orphan": too applied for COLT, too theoretical for an applied venue. Usually a framing failure — pick the community whose open question you actually answer and write for it alone.
Timing considerations
- COLT's single annual deadline (February 4, 2026 for the 39th edition) sits between the autumn ML-conference cluster and summer; a NeurIPS reject in September leaves comfortable repair time, an ICML reject usually does not — plan the cascade.
- ALT and COLT deadlines are roughly anti-phased, making ALT the natural same-community fallback; verify the current ALT cycle before promising the team.
Cycle-volatility warnings
- Scope emphasis and the topics list are re-issued every cycle; the intersection framing above is the 2026 wording (待核实 later).
- Open-problem track existence, format, and deadline: current CFP only.
Output format
[Fit] core COLT / plausible COLT / misroute
[Headline claim] <quantifiers + model + bound/separation, one line>
[Delta type] <vs. nearest prior theorem>
[Alternative vehicle] full paper / open-problem piece / ALT / JMLR / AISTATS / STOC-FOCS
[Pre-writing blocker] <verification, motivation, or search gap to close first>© 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-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Colt Topic Selection 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 Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Sandbox Benchvercel/next.js | 143k | — | ~4.1k | Automated safety check: Pass | MIT | |
| Statistical Analysisspacering-net/codeg | 3.9k | 3 repos | ~5k | Automated safety check: Pass | MIT | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| AI Daily DigestvigorX777/ai-daily-digest | 1.6k | — | ~1.3k | Automated safety check: Pass | None | |
| Statistical Powerspacering-net/codeg | 3.9k | 1 repos | ~3.6k | Automated safety check: Notes | MIT |
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Questions about Colt Topic Selection
What does Colt Topic Selection do?
A skill your agent uses when deciding whether a result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC/FOCS…. Colt Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a result is COLT-shaped (Conference on Learning Theory) — a theorem-first learning-theory contribution — versus better routed to ALT, STOC/FOCS, NeurIPS/ICML/AISTATS, JMLR, or a statistics journal, and whether the right vehicle is a full paper or a COLT open-problem piece.
When should I use Colt Topic Selection?
Colt Topic Selection fits situations like: neurIPS/ICML/AISTATS; A statistics journal; whether the right vehicle is a full paper; A COLT open-problem piece.
How do I install Colt Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-topic-selection -a claude-code`. Or copy the skill folder (COLT-Skills/skills/colt-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/colt-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Colt Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-topic-selection -a codex`. Or copy the skill folder (COLT-Skills/skills/colt-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/colt-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Colt Topic Selection 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-topic-selection -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-topic-selection, .gemini/skills/colt-topic-selection, .github/skills/colt-topic-selection and .opencode/skills/colt-topic-selection in your project.
What does Colt Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Colt Topic Selection is instructions for the agent only.
Does Colt Topic Selection 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 Topic Selection 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 Topic Selection use?
Colt Topic Selection 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 Topic Selection use?
About 1.9k tokens (SKILL.md is roughly 7.4k 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 Topic Selection?
Skills that share tags, products or a category with Colt Topic Selection: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.9k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars) and AI Daily Digest (vigorX777/ai-daily-digest, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Colt Topic Selection?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.