Jqte Io Cge
franklee16/academic-research-skills
A skill your agent uses when a 《数量经济技术经济研究》 (JQTE) manuscript is built on an input-output table, a CGE model, or a structural decomposition (SDA).
A skill your agent uses when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure…
Install Uai Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-reproducibility --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-reproducibility .claude/skills/uai-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-reproducibility into .claude/skills/uai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-reproducibility", 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-reproducibilityType 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-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-reproducibility --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-reproducibility .agents/skills/uai-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-reproducibility into .agents/skills/uai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-reproducibility", 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-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-reproducibility --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-reproducibility .cursor/skills/uai-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-reproducibility into .cursor/skills/uai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-reproducibility", 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-reproducibility--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-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-reproducibility --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-reproducibility .gemini/skills/uai-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-reproducibility into .gemini/skills/uai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-reproducibility", 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-reproducibilityInstalls 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-reproducibility -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-reproducibility .github/skills/uai-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-reproducibility into .github/skills/uai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-reproducibility", 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-reproducibility -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-reproducibility --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-reproducibility .opencode/skills/uai-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-reproducibility into .opencode/skills/uai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-reproducibility", 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-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 713 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure…
- Works in 5 steps: Clone your own artifact onto a machine… → Reproduce the smallest headline number… → Diff the regenerated figure against the… → …
- Hardening reproducibility evidence for a UAI paper
- SKILL.md covers The UAI-specific bar, Determinism ledger, Disclosure map and Diagnostic quick reference, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Uai Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure, compute reporting, and honest code-availability statements, since UAI strongly encourages released code and data and reviews whether claims are convincingly backed.
Its SKILL.md is about 1.6k 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 Research & Science, covering Reproducible research and Performance reviews. 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
- Hardening reproducibility evidence for a UAI paper
- Including seeds
- Sampler convergence diagnostics
- ELBO and calibration traces
Example prompts
- “/uai-reproducibility”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Clone your own artifact onto a machine that never ran the project; follow only the
- Reproduce the smallest headline number end to end, including the diagnostic that
- Diff the regenerated figure against the paper's; investigate any visible deviation
- Grep the paper for every "we observe/we find/consistently" and confirm each maps to a
- Write the availability statement last, describing what is actually in the ZIP — not
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
Uai Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 713 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). 713 words, ~1,628 tokens.
Download SKILL.mdSave it as
.claude/skills/uai-reproducibility/SKILL.md (or your agent's skills folder).- name
- uai-reproducibility
- description
- Use when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure, compute reporting, and honest code-availability statements, since UAI strongly encourages released code and data and reviews whether claims are convincingly backed.
UAI Reproducibility
UAI's 2026 CFP did not impose a formal reproducibility checklist (one may appear later — 待核实 each cycle), but it strongly encouraged code and data availability and instructed reviewers to judge whether claims are backed up convincingly. At this venue "convincing" has a technical meaning: stochastic-inference results carry diagnostics, not just point estimates. This skill turns that norm into an audit.
The UAI-specific bar
Reproducibility questions at a probabilistic-inference venue go one level deeper than "can I rerun the script":
- A sampler that reproduces the same posterior mean but different tail quantiles has not reproduced the paper — report and check the diagnostics that detect this (R-hat, ESS, divergent transitions where relevant).
- A variational result is reproducible when the ELBO trajectory and the selected restart are recoverable, not merely the final metric; multi-restart selection rules must be stated.
- Calibration claims reproduce only if the binning scheme, the split used for recalibration, and the α grid are all pinned down; empirical coverage moves with all three.
- Causal-discovery results depend on graph generation as much as on the algorithm: publish the SCM sampler, noise families, and intervention protocol.
Determinism ledger
Record every randomness source once, in code, and cite it from the paper:
# repro/ledger.py — imported by every experiment entry point
import json, platform, random, numpy as np
def fix_and_log(seed: int, path: str = "run_manifest.json"):
random.seed(seed)
np.random.seed(seed)
manifest = {
"seed": seed,
"python": platform.python_version(),
"numpy": np.__version__,
"chains": 4, "warmup": 1000, "draws": 2000, # sampler config lives here
"elbo_restarts": 10, "restart_rule": "best final ELBO",
}
json.dump(manifest, open(path, "w"), indent=2)
return manifestThe manifest style matters more than the specific fields: one machine-readable file per run, checked into the artifact, lets a reviewer reconcile the paper's Table 3 with an actual execution.
Disclosure map
| What must be recoverable | Where it lives at UAI | Common omission |
|---|---|---|
| Model and assumption set | Main part, stated with each theorem/method | Assumptions distributed across three sections |
| Sampler / optimizer settings | Appendix (unlimited, same PDF) | "Default settings" without library version |
| Hyperparameter search space and selection rule | Appendix table | Only the winning configuration reported |
| Seeds and number of repeats | Appendix + artifact manifest | Single-run results with no variance |
| Dataset versions, splits, preprocessing | Appendix + loader script in ZIP | Preprocessing "as in [12]" where [12] is ambiguous |
| Compute (hardware, runtime, memory) | Appendix | Runtime reported only for the proposed method, not baselines |
| Code/data availability statement | Main part or appendix | Silence, which reviewers read as "unavailable" |
Diagnostic quick reference
What "reported convincingly" tends to mean per inference family — as conventions of the field, not venue mandates:
| Family | Minimum reported | Stronger version |
|---|---|---|
| MCMC | R-hat per parameter block, ESS, chain count/length | Rank plots; comparison against a long-run gold standard |
| Variational | Final ELBO, restart count and rule | ELBO traces; posterior-quality check on a tractable case |
| SMC / particle | Particle count, resampling scheme, ESS trajectory | Variance of the marginal-likelihood estimate over repeats |
| Conformal / intervals | Split sizes, α grid, empirical coverage | Conditional coverage slices; width distribution |
| Calibration | Binning scheme, ECE definition used | Reliability diagrams with confidence bands over seeds |
| Causal discovery | Graph generator, noise family, SHD/SID per seed | Sensitivity to assumption violations (unfaithfulness, confounding) |
If a row's "minimum" column is missing for your method family, expect the backing criterion to absorb the damage.
Show full SKILL.md (241 more words)Show less
Honesty over completeness
- If code cannot be released (industrial constraints, licensed data), say so in the paper and compensate: fuller pseudocode, exact hyperparameters, synthetic surrogates for private datasets. The encouraged-not-mandatory wording gives room for honesty, not for vagueness.
- Report failure modes you observed — initializations that collapse, chains that need longer warmup on one dataset. Probabilistic-ML reviewers trust papers that know where their method breaks.
- Never let variance disappear in the retelling: if three of ten seeds underperform, the aggregate table must reflect it (mean ± sd over all ten, or a stated, principled selection rule).
Where reproducibility evidence lives
Split by tier deliberately: the availability statement and diagnostic summaries in the reviewed PDF (body or appendix), where they count toward backing; manifests, loaders, and per-run logs in the ZIP, where they support spot-checks. Never leave the only mention of seeds or repeat counts inside the optional archive — reviewers grade what the PDF says.
Pre-submission reproducibility drill
- Clone your own artifact onto a machine that never ran the project; follow only the README.
- Reproduce the smallest headline number end to end, including the diagnostic that validates it.
- Diff the regenerated figure against the paper's; investigate any visible deviation before a reviewer does.
- Grep the paper for every "we observe/we find/consistently" and confirm each maps to a logged run.
- Write the availability statement last, describing what is actually in the ZIP — not what was planned in January.
Output format
[Repro grade] turnkey / recoverable with effort / under-specified
[Diagnostics reported] <R-hat/ESS/ELBO/coverage/SHD... as applicable>
[Determinism ledger] present / partial / absent
[Disclosure gaps] <items from the map still missing>
[Availability statement] drafted / needs honesty pass / missing© 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-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Uai Reproducibility 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 Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Jqte Io Cgefranklee16/academic-research-skills | 223 | 1 repos | ~419 | Automated safety check: Pass | None | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 |
Similar skills
Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
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Categories
Questions about Uai Reproducibility
What does Uai Reproducibility do?
A skill your agent uses when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure…. Uai Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening reproducibility evidence for a UAI paper, including seeds, sampler convergence diagnostics, ELBO and calibration traces, dataset and hyperparameter disclosure, compute reporting, and honest code-availability statements, since UAI strongly encourages released code and data and reviews whether claims are convincingly backed.
When should I use Uai Reproducibility?
Uai Reproducibility fits situations like: hardening reproducibility evidence for a UAI paper; including seeds; sampler convergence diagnostics; ELBO and calibration traces.
How do I install Uai Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-reproducibility -a claude-code`. Or copy the skill folder (UAI-Skills/skills/uai-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/uai-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Uai Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-reproducibility -a codex`. Or copy the skill folder (UAI-Skills/skills/uai-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/uai-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Uai Reproducibility 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-reproducibility -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-reproducibility, .gemini/skills/uai-reproducibility, .github/skills/uai-reproducibility and .opencode/skills/uai-reproducibility in your project.
What does Uai Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Uai Reproducibility is instructions for the agent only. Our summary lists: Python 3.
Does Uai Reproducibility 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 Reproducibility 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 Reproducibility use?
Uai Reproducibility 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 Reproducibility use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Reproducibility?
Skills that share tags, products or a category with Uai Reproducibility: Jqte Io Cge (franklee16/academic-research-skills, 223 stars), Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars) and Compute Environment Setup (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Uai Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.