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 making a UIST paper's results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system, specifying hardware down to…
Install Uist Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uist-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uist-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/UIST-Skills/skills/uist-reproducibility .claude/skills/uist-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 "uist-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UIST-Skills/skills/uist-reproducibility into .claude/skills/uist-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uist-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/UIST-Skills/skills/uist-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 uist-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uist-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/UIST-Skills/skills/uist-reproducibility .agents/skills/uist-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 "uist-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UIST-Skills/skills/uist-reproducibility into .agents/skills/uist-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uist-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 uist-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uist-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/UIST-Skills/skills/uist-reproducibility .cursor/skills/uist-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 "uist-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UIST-Skills/skills/uist-reproducibility into .cursor/skills/uist-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uist-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 UIST-Skills/skills/uist-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 uist-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uist-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/UIST-Skills/skills/uist-reproducibility .gemini/skills/uist-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 "uist-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UIST-Skills/skills/uist-reproducibility into .gemini/skills/uist-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uist-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 uist-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 uist-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/UIST-Skills/skills/uist-reproducibility .github/skills/uist-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 "uist-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UIST-Skills/skills/uist-reproducibility into .github/skills/uist-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uist-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 uist-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 uist-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/UIST-Skills/skills/uist-reproducibility .opencode/skills/uist-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 "uist-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UIST-Skills/skills/uist-reproducibility into .opencode/skills/uist-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uist-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
uist-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 621 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when making a UIST paper's results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system, specifying hardware down to…
- Making a UIST papers results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system
- SKILL.md covers The build ledger: could they…, The measurement ledger: could…, The study ledger: could they… and The availability statement, plus 4 more sections
- Calls python
- Specifying hardware down to parts and calibration
What it does
Uist Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making a UIST paper's results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system, specifying hardware down to parts and calibration, logging technical evaluations deterministically, and writing honest availability statements for interface systems.
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.
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
- Making a UIST papers results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system
- Specifying hardware down to parts and calibration
- Logging technical evaluations deterministically
- Writing honest availability statements for interface systems
Example prompts
- “/uist-reproducibility”
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
Shell commands in SKILL.md call:
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
Uist Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 621 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). 621 words, ~1,661 tokens.
Download SKILL.mdSave it as
.claude/skills/uist-reproducibility/SKILL.md (or your agent's skills folder).- name
- uist-reproducibility
- description
- Use when making a UIST paper's results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system, specifying hardware down to parts and calibration, logging technical evaluations deterministically, and writing honest availability statements for interface systems.
UIST Reproducibility
Reproducibility at UIST means something different from rerunning a training script: the question is whether a competent lab could rebuild the artifact and reproduce its measured behavior. That decomposes into three ledgers — build, measurement, and study — and most UIST papers under-specify a different one than they think. UIST posts no reproducibility checklist (none found for 2026 — 待核实), so this discipline is self-imposed and reviewer-enforced.
The build ledger: could they make one?
Everything the system's behavior depends on, pinned:
- Software: exact framework and driver versions, OS, lockfiles; for anything learned, model checkpoints and training data provenance.
- Hardware: part numbers (not "an IMU" but which IMU), mechanical tolerances that matter, firmware version, and the calibration routine with expected outputs.
- Environment: the physical conditions the system assumes — lighting range, acoustic environment, mounting geometry, surface materials.
- Magic numbers: every threshold, filter coefficient, debounce window, and gain, with how each was set (tuned by hand? on which data?). These constants are where re-implementations actually fail.
The measurement ledger: could they get your numbers?
A latency or accuracy figure is reproducible only with its protocol:
| Reported number | Must be pinned |
|---|---|
| Latency | Measurement boundary (sensor-to-photon? software-only?), instrument, event count, load conditions |
| Recognition accuracy | Dataset splits, per-user vs pooled, session separation, chance level |
| Tracking error | Ground-truth apparatus and its own accuracy, spatial sampling grid |
| Throughput / bitrate | Task, phrase set or corpus, session and rest structure |
| Power / weight / cost | Configuration measured, currency and date for cost |
Automate the protocol: a measurement harness checked into the supplement converts
"trust me" into "run this" (see uist-artifact-evaluation for packaging).
# eval/rerun.sh — regenerate every reported number from raw logs
python analyze_latency.py logs/latency_10k.jsonl --out tables/table1.csv
python analyze_accuracy.py logs/study/ --split per-user --seed 17 --out tables/table2.csv
diff -u tables/table1.csv paper_tables/table1.csv # drift check against the PDFLog at the event level with timestamps and raw sensor values, not just computed
outcomes — future you, rebuttal you (see uist-author-response), and replicating
labs all consume the same logs.
The study ledger: could they rerun the human part?
For any user evaluation: full task instructions and stimuli, counterbalancing scheme, practice/rest structure, apparatus placement (photograph it), inclusion/exclusion criteria, compensation, and the analysis scripts from raw logs to reported statistics. Share instruments even when raw human data cannot leave the IRB envelope — protocol transparency and data availability are separable, and saying so precisely is the honest move.
Show full SKILL.md (267 more words)Show less
The availability statement
Write one even though UIST does not require a template, and make it specific:
GOOD: "Firmware, PCB design files, BOM, and the measurement harness are at
<archive-DOI> (tag: as-published). The gesture corpus (14 of 16
participants consented to release) is included; per-participant raw
video is withheld under IRB #—. The two commercial tracking SDKs
required are named in BUILD.md with tested versions."
BAD: "Code available upon reasonable request."Papers with learned components inherit ML reporting norms too — seeds, training
configs, compute — and can borrow the shared kit's checklists (see
../../resources/code/README.md).
Learned components inherit ML norms
When the system embeds recognition or generation models, the ML reporting conventions stack on top of the systems ledgers:
- Seeds, splits, and training configs pinned; per-user vs pooled evaluation made explicit (interaction data is brutally user-dependent, and pooled splits inflate accuracy).
- For third-party or hosted models: exact model identifiers and versions, the full prompt set, decoding parameters, and dated transcripts — hosted models drift, so the logged behavior is the only permanent record of what reviewers saw.
- Report the interactive costs alongside accuracy: per-inference latency on the deployment hardware, and per-session cost for metered APIs; a technique that is reproducible but unaffordable to run is only half-replicable.
- The shared kit's checklists cover this lane; the adapter in
resources/code/README.mdscopes what it can and cannot check.
The one-page REPRO.md
Compress the three ledgers into a single file at the archive root:
# Reproducing <SystemName> (paper §6-7)
## Rebuild parts: BOM.csv · firmware: v2.3 · calibration: docs/calib.md
## Environment tested: indoor 200-800 lux, 18-26°C · assumes: mounted per Fig 4
## Constants thresholds in config.yaml — tuned on pilot data (n=4), §5.2
## Remeasure eval/rerun.sh regenerates Tables 1-2 from logs/ (or raw capture)
## Study instruments/ · counterbalancing: latin square, §7.1 · IRB #—
## Known drift accuracy drops outdoors (§9); p95 latency sensitive to BLE stackThe "known drift" line is the credibility multiplier: it tells replicators you know where the envelope ends before they find out.
Replication drift and its uses
When a rebuilt system misses the paper's numbers, the causes rank: unstated environmental assumptions, hand-tuned constants, part substitutions, then genuine bugs. Pre-empt the first two by stating the envelope and the tuning story in the paper body — it costs three sentences and buys the paper years of credibility (and Lasting Impact eligibility runs on decade-scale credibility).
Output format
[Build ledger] pinned / gaps: <software · hardware · environment · constants>
[Measurement ledger] protocols pinned for <k>/<n> reported numbers
[Study ledger] instruments · counterbalancing · analysis scripts — present?
[Availability statement] drafted? honest about withholdings?
[Top drift risk] <the unstated assumption most likely to break replication>© 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 UIST-Skills/skills/uist-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Uist 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 |
|---|---|---|---|---|---|---|
| Uist Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | 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 Uist Reproducibility
What does Uist Reproducibility do?
A skill your agent uses when making a UIST paper's results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system, specifying hardware down to…. Uist Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making a UIST paper's results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system, specifying hardware down to parts and calibration, logging technical evaluations deterministically, and writing honest availability statements for interface systems.
When should I use Uist Reproducibility?
Uist Reproducibility fits situations like: making a UIST papers results replicable — reporting implementation parameters and measurement protocols so a lab could rebuild the system; specifying hardware down to parts and calibration; logging technical evaluations deterministically; writing honest availability statements for interface systems.
How do I install Uist Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uist-reproducibility -a claude-code`. Or copy the skill folder (UIST-Skills/skills/uist-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/uist-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Uist Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uist-reproducibility -a codex`. Or copy the skill folder (UIST-Skills/skills/uist-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/uist-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Uist 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 uist-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/uist-reproducibility, .gemini/skills/uist-reproducibility, .github/skills/uist-reproducibility and .opencode/skills/uist-reproducibility in your project.
What does Uist Reproducibility need to run?
Going by SKILL.md and its folder, Uist Reproducibility needs the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Uist 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 Uist 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 Uist Reproducibility use?
Uist 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 Uist Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 6.6k 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 Uist Reproducibility?
Skills that share tags, products or a category with Uist 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 Uist 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.