Pyfixest
brycewang-stanford/Auto-Empirical-Research-Skills
Fast high-dimensional fixed effects: OLS, Poisson, IV with multi-way FE; DiD (TWFE, did2s, Sun-Abraham); clustered SEs; etable/coefplot/iplot.
A skill your agent uses when deciding whether a project belongs at UAI, where uncertainty representation, probabilistic reasoning, graphical models, causal inference, or decision making under…
Install Uai Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-topic-selection .claude/skills/uai-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 "uai-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-topic-selection into .claude/skills/uai-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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/UAI-Skills/skills/uai-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 uai-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-topic-selection .agents/skills/uai-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 "uai-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-topic-selection into .agents/skills/uai-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 uai-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-topic-selection .cursor/skills/uai-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 "uai-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-topic-selection into .cursor/skills/uai-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 UAI-Skills/skills/uai-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 uai-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-topic-selection .gemini/skills/uai-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 "uai-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-topic-selection into .gemini/skills/uai-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 uai-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 uai-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/UAI-Skills/skills/uai-topic-selection .github/skills/uai-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 "uai-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-topic-selection into .github/skills/uai-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 uai-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 uai-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/UAI-Skills/skills/uai-topic-selection .opencode/skills/uai-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 "uai-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-topic-selection into .opencode/skills/uai-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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
uai-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 893 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 project belongs at UAI, where uncertainty representation, probabilistic reasoning, graphical models, causal inference, or decision making under…
- Works in 4 steps: Name the probabilistic object the paper… → Name the guarantee or diagnostic… → Check the evidence shape fits an 8-page… → …
- Deciding whether a project belongs at UAI
- SKILL.md covers The one-question filter, Signal table, Sibling-venue geometry and Sharpening a genuine fit, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Uai Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at UAI, where uncertainty representation, probabilistic reasoning, graphical models, causal inference, or decision making under uncertainty must be the contribution itself, and when to route instead to AISTATS, NeurIPS, ICML, COLT, CLeaR, or a statistics journal before drafting begins.
Its SKILL.md is about 1.8k 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 and Econometrics and empirical research. 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 project belongs at UAI
- Where uncertainty representation
- Probabilistic reasoning
- Graphical models
Example prompts
- “/uai-topic-selection”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Name the probabilistic object the paper contributes: a posterior approximation, a
- Name the guarantee or diagnostic attached to it: consistency, coverage, ESS
- Check the evidence shape fits an 8-page main part with appendices — one decisive
- Scan the current CFP's topic list (2026 spanned inference families, optimization,
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
Uai Topic Selection loads about 1.8k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 893 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). 893 words, ~1,821 tokens.
Download SKILL.mdSave it as
.claude/skills/uai-topic-selection/SKILL.md (or your agent's skills folder).- name
- uai-topic-selection
- description
- Use when deciding whether a project belongs at UAI, where uncertainty representation, probabilistic reasoning, graphical models, causal inference, or decision making under uncertainty must be the contribution itself, and when to route instead to AISTATS, NeurIPS, ICML, COLT, CLeaR, or a statistics journal before drafting begins.
UAI Topic Selection
Use this before writing a line. UAI — the AUAI's annual conference, running since the 1980s — is the venue where uncertainty is the subject, not the seasoning. The 2026 CFP invited novel theory, methodology, and applications spanning AI, machine learning, and statistics, but the reviewer pool and the accepted-paper record reward a specific shape: papers whose contribution is a probabilistic representation, an inference procedure, a causal identification result, or a decision rule under uncertainty.
The one-question filter
If you deleted every probabilistic element from this paper, would anything remain?
- Remains a strong paper → the probability is decoration; route to a general ML venue.
- Nothing remains → you are holding a UAI candidate; now check the evidence shape.
- A weaker but real paper remains → the venue call depends on which half is novel; interrogate with the table below.
Signal table
| Project signal | UAI reading |
|---|---|
| New inference algorithm (MCMC, SMC, variational, belief propagation) with analysis | Home territory since the venue's founding |
| Identifiability or discovery result for causal or graphical structure | Core — UAI is a first-choice causality venue |
| Calibration, conformal, or coverage guarantee for predictive models | Strong fit; growing lane in recent volumes |
| Decision making / planning under uncertainty, Bayesian experimental design | Core, especially with formal treatment |
| Probabilistic programming semantics or inference | Distinctive UAI lane, rare elsewhere |
| Deep architecture, uncertainty used only as an evaluation metric | Re-route: NeurIPS / ICML / ICLR |
| General estimation theory, uncertainty incidental | Often better at AISTATS |
| Pure regret/sample-complexity theory, no probabilistic-modeling core | COLT or ALT |
| Causal ML with an applied-community audience | Compare CLeaR before defaulting to UAI |
| Journal-depth asymptotics needing 40 pages | JMLR or a statistics journal |
Sibling-venue geometry
AISTATS and UAI are the commonly confused pair — both single-deadline, PMLR-published, statistics-adjacent, and of comparable scale. A working separation: AISTATS emphasizes the statistics–ML interface broadly (estimators, rates, high-dimensional methods); UAI concentrates on the representation and use of uncertainty itself — graphical models, causality, Bayesian reasoning, decisions. A debiased estimator with a convergence rate leans AISTATS; an identifiability theorem over MAGs leans UAI; a calibrated-prediction method with finite-sample coverage plays at either, so decide by which reviewer conversation helps the work more.
Timing is a legitimate tiebreaker between honest fits: UAI's cycle (submission ~February, decision ~June) interleaves with AISTATS (~October submission) and NeurIPS/ICML, and a paper genuinely at home in two venues may reasonably pick the calendar that meets it ready. Never let timing overrule fit — a misrouted paper burns a cycle anyway.
Sharpening a genuine fit
- Name the probabilistic object the paper contributes: a posterior approximation, a graph class, an identification condition, an interval, a policy. No object, no UAI framing.
- Name the guarantee or diagnostic attached to it: consistency, coverage, ESS efficiency, SHD improvement, regret bound. This becomes the page-1 promise.
- Check the evidence shape fits an 8-page main part with appendices — one decisive exact-truth experiment beats six benchmark sweeps here.
- Scan the current CFP's topic list (2026 spanned inference families, optimization, probabilistic programming, and application areas from computational biology to robotics) to phrase your subject areas in the venue's own vocabulary.
Show full SKILL.md (388 more words)Show less
Borderline vignettes
Three recurring hard calls, with the reasoning that resolves them:
- "We add a Bayesian layer to a transformer and report accuracy plus ECE." The deletion test partially survives (the architecture stands alone), and the uncertainty content is evaluation-grade, not contribution-grade. As described: NeurIPS/ICML material. It becomes UAI-shaped only if the paper's center of gravity moves — e.g., a posterior-approximation scheme with analyzed bias, or a calibration guarantee — and the architecture becomes the test bed.
- "We prove a regret bound for Thompson sampling in a structured bandit." Probabilistic object (posterior sampling policy) and guarantee (regret) both present. The split: if the novelty is the analysis technique, COLT/ALT readers value it most; if the novelty is the modeling of uncertainty in the structure (priors over graphs, correlated arms), UAI is the natural room.
- "We identify treatment effects under a new missingness mechanism, with an estimator and simulations." Identification result + estimator + coverage simulations is simultaneously UAI-core and AISTATS-core. Decide by conversation: UAI if the paper leans on graphical criteria and reasoning about the mechanism; AISTATS if it leans on estimation theory and rates. Either way, do not split the difference in the framing — pick one audience and write for it.
Calendar map for re-routes
If the verdict is "route elsewhere", the practical question becomes when. Approximate rhythm of the neighbors (always verify each venue's current dates):
- AISTATS: submissions historically in early autumn, conference in spring — the natural next stop after a February UAI miss.
- NeurIPS: late-spring deadline; ICML: winter deadline close to UAI's own — an ICML reject can often turn around for UAI in the same season, and vice versa.
- COLT: winter deadline, summer conference; CLeaR: autumn-ish deadline for the causality community.
- JMLR / statistics journals: rolling — the release valve when the contribution outgrew 8 pages, not a consolation prize.
Keep the re-route target written down before the UAI decision arrives; deciding while disappointed produces prestige-chasing, not fit-chasing.
Anti-signals worth trusting
- The draft's introduction cites no UAI, AISTATS, or CLeaR papers at all — the community you are addressing does not include the one you are submitting to.
- The strongest section is a systems/scaling result and the probabilistic section defends design choices rather than proving properties.
- The reviewers you would wish for are all from one distant subfield.
- You cannot fill the
[Attached guarantee]line below without the word "hope".
Output format
[Deletion test] survives without probability? yes / partially / no
[Probabilistic object] <posterior / graph / interval / policy / condition>
[Attached guarantee] <the formal or diagnostic promise>
[Verdict] UAI-first / UAI-viable / route to <AISTATS | NeurIPS/ICML | COLT | CLeaR | journal>
[Next step] <framing fix, missing experiment, or venue switch>© 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 UAI-Skills/skills/uai-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Uai 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 |
|---|---|---|---|---|---|---|
| Uai Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Pyfixestbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~3.5k | Automated safety check: Pass | Custom licence | |
| Statsbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~2k | Automated safety check: Pass | Custom licence | |
| Research Analysis Routerwentorai/Research-Claw | 858 | — | ~169 | Automated safety check: Pass | Custom licence | |
| Review Of Economics And Statisticsfranklee16/academic-research-skills | 223 | 1 repos | ~1.6k | Automated safety check: Pass | None | |
| Data Profileraspi6246/Claude-Code-Skills-for-Academics | 159 | — | ~2k | Automated safety check: Pass | None |
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A skill your agent uses when targeting Review of Economics and Statistics (REStat) or deciding whether an applied econometrics manuscript fits this venue.
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Systematic dataset profiling protocol for empirical research.
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Questions about Uai Topic Selection
What does Uai Topic Selection do?
A skill your agent uses when deciding whether a project belongs at UAI, where uncertainty representation, probabilistic reasoning, graphical models, causal inference, or decision making under…. Uai Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at UAI, where uncertainty representation, probabilistic reasoning, graphical models, causal inference, or decision making under uncertainty must be the contribution itself, and when to route instead to AISTATS, NeurIPS, ICML, COLT, CLeaR, or a statistics journal before drafting begins.
When should I use Uai Topic Selection?
Uai Topic Selection fits situations like: deciding whether a project belongs at UAI; where uncertainty representation; probabilistic reasoning; graphical models.
How do I install Uai Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-topic-selection -a claude-code`. Or copy the skill folder (UAI-Skills/skills/uai-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/uai-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Uai Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-topic-selection -a codex`. Or copy the skill folder (UAI-Skills/skills/uai-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/uai-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Uai 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 uai-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/uai-topic-selection, .gemini/skills/uai-topic-selection, .github/skills/uai-topic-selection and .opencode/skills/uai-topic-selection in your project.
What does Uai Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Uai Topic Selection is instructions for the agent only.
Does Uai 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 Uai 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 Uai Topic Selection use?
Uai 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 Uai Topic Selection use?
About 1.8k tokens (SKILL.md is roughly 7.3k 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 Uai Topic Selection?
Skills that share tags, products or a category with Uai Topic Selection: Pyfixest (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Stats (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Research Analysis Router (wentorai/Research-Claw, 858 stars) and Review Of Economics And Statistics (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Uai 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.