Harness Learn
ruvnet/ruflo
Run a GEPA learning cycle via metaharness learn (upstream ADR-235, metaharness@0.3.0) — optimizes a harness genome against a SWE-bench-style slice manifest.
A skill your agent uses when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under…
Install Chi Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-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/CHI-Skills/skills/chi-reproducibility .claude/skills/chi-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 "chi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-reproducibility into .claude/skills/chi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-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/CHI-Skills/skills/chi-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 chi-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-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/CHI-Skills/skills/chi-reproducibility .agents/skills/chi-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 "chi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-reproducibility into .agents/skills/chi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-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 chi-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-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/CHI-Skills/skills/chi-reproducibility .cursor/skills/chi-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 "chi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-reproducibility into .cursor/skills/chi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-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 CHI-Skills/skills/chi-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 chi-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-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/CHI-Skills/skills/chi-reproducibility .gemini/skills/chi-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 "chi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-reproducibility into .gemini/skills/chi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-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 chi-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 chi-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/CHI-Skills/skills/chi-reproducibility .github/skills/chi-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 "chi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-reproducibility into .github/skills/chi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-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 chi-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 chi-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/CHI-Skills/skills/chi-reproducibility .opencode/skills/chi-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 "chi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-reproducibility into .opencode/skills/chi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-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
chi-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 553 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under…
- Works in 4 steps: Full de-identified dataset in a… → Partial release: quantitative measures… → Aggregate data plus instruments and… → …
- Strengthening research transparency for an ACM CHI paper — protocols
- SKILL.md covers Three layers, three different…, Quantitative transparency, Qualitative transparency and Data sharing under…, plus 3 more sections
- Calls python3, pip and python
What it does
Chi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work.
Its SKILL.md is about 1.4k 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 Architecture decision records. 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
- Strengthening research transparency for an ACM CHI paper — protocols
- Analysis scripts
- Preregistration
- Data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work
Example prompts
- “/chi-reproducibility”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Full de-identified dataset in a persistent repository (OSF, institutional archive).
- Partial release: quantitative measures public, recordings withheld.
- Aggregate data plus instruments and codebook.
- No data, documented reason (consent scope, re-identification risk, community
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:
python3pippython
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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
Chi Reproducibility loads about 1.4k tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 553 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). 553 words, ~1,447 tokens.
Download SKILL.mdSave it as
.claude/skills/chi-reproducibility/SKILL.md (or your agent's skills folder).- name
- chi-reproducibility
- description
- Use when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work.
CHI Reproducibility
Reproducibility at CHI is not "same script, same numbers." Human-subjects research reproduces at the level of protocol and analysis: could a competent lab run your study again, and could a skeptic re-derive your findings from your materials? CHI's screening now names "research transparency" explicitly inside the ADR-Method assisted desk-reject ground, so opacity is a pre-review rejection risk. The working principle for data: as open as consent allows, as documented as possible where it does not.
Three layers, three different obligations
| Layer | What must be true | Typical artifacts |
|---|---|---|
| Protocol | Another lab could run the study | Task descriptions, scripts read to participants, stimuli, apparatus specs, recruitment text, screening criteria, compensation |
| Analysis | A skeptic could re-derive results from your data | Analysis code, codebook + coding decisions, exclusion rules, model specifications, software versions |
| Data | Shared where consent permits; described honestly where not | De-identified quantitative data, aggregate tables, transcript excerpts, or a documented reason why not |
The protocol layer is the cheapest and the most neglected: your consent scripts,
questionnaires, and interview guides already exist — publishing them in the
supplement costs an afternoon and answers half of the methods questions reviewers
would otherwise raise (chi-supplementary).
Quantitative transparency
- Ship the analysis pipeline: raw-to-clean transformation, exclusions with counts and reasons, and the exact statistical models. Pin versions (R/Python, packages).
- Preregistration (OSF, AsPredicted) is increasingly normal for confirmatory CHI
studies; during review, link an anonymized view only — a named OSF project is
an anonymization violation (
chi-submission). - Report every measured variable somewhere, including ones that showed nothing; selective reporting discovered later damages more than a null result ever would.
- Randomization, counterbalancing assignments, and seed-equivalents (trial-order generation) belong in the materials, not in folklore.
Qualitative transparency
Qualitative work cannot ship a replication button; it can ship an audit trail:
- The interview guide or diary prompts, verbatim, including probes.
- The codebook where the method uses one — codes, definitions, example excerpts — or, for reflexive approaches, a documented account of how themes developed.
- Analysis-process notes: who coded, how disagreements were handled, memo samples.
- Transcript excerpts beyond those quoted in the paper, where consent allows — reviewers increasingly distrust papers whose only visible data is ten quotes.
Show full SKILL.md (200 more words)Show less
Data sharing under human-subjects constraints
Never promise what consent cannot deliver. The honest ladder, top rung you can reach:
- Full de-identified dataset in a persistent repository (OSF, institutional archive).
- Partial release: quantitative measures public, recordings withheld.
- Aggregate data plus instruments and codebook.
- No data, documented reason (consent scope, re-identification risk, community agreements — common and respected in work with vulnerable populations), plus a contact path for mediated access if any exists.
For AI-infused systems add: model name and version/date, prompts and parameters, and cached model outputs from the study window, because the hosted model your participants used will not exist next year. A CHI study of "the assistant" without a pinned version is unreplicable by construction.
The availability statement
State per artifact class what is available, where, and why not where not:
Availability. Study protocol, interview guide, questionnaires, and the full
codebook: <repository DOI>. De-identified quantitative data and analysis
scripts (R 4.4, renv lockfile): same repository. Audio recordings and raw
transcripts are not shared, per the consent agreement; extended anonymized
excerpts appear in the supplement. LLM condition: <model+version>, prompts
and all cached outputs included.During review this statement appears with anonymized links; at camera-ready it flips
to named archives (chi-camera-ready). Write both versions on the same day so the
promises match.
Verification before the claim
# The availability statement is a claim; test it like one.
ls protocol/ instruments/ codebook/ data/ analysis/ # inventory vs statement
grep -rEin 'available (upon|on) request' paper/ && echo "WEAK: replace or justify"
python3 -m venv /tmp/repro && /tmp/repro/bin/pip install -r analysis/requirements.txt \
&& /tmp/repro/bin/python analysis/reproduce_tables.py # cold-start the pipeline
grep -rEil 'participant|P[0-9]+_(name|email)' data/ | head # de-identification sweep"Available upon request" earns no credit at CHI — studies of such promises across fields show most requests go unanswered, and reviewers know it. Either deposit the artifact or explain the genuine constraint.
Output format
[Protocol layer] complete / gaps: <missing instruments>
[Analysis layer] pipeline runs cold: yes/no · codebook/audit trail: yes/no
[Data rung] 1-4 on the ladder + one-line justification
[Anonymized-review versions] links safe for PCS: yes/no
[ADR-Method exposure] low/med/high — <the opaquest spot in the methods>
[One-day fixes] <cheapest transparency wins available now>© 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 CHI-Skills/skills/chi-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Chi 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 |
|---|---|---|---|---|---|---|
| Chi Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Harness Learnruvnet/ruflo | 74k | — | ~800 | Automated safety check: Notes | MIT | |
| Research Initbrycewang-stanford/Auto-Empirical-Research-Skills | 4.6k | — | ~839 | Automated safety check: Pass | Custom licence | |
| 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 |
Similar skills
Research Init
brycewang-stanford/Auto-Empirical-Research-Skills
Scaffold a new research project with full reproducibility infrastructure in R and/or Python.
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.
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Questions about Chi Reproducibility
What does Chi Reproducibility do?
A skill your agent uses when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under…. Chi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work.
When should I use Chi Reproducibility?
Chi Reproducibility fits situations like: strengthening research transparency for an ACM CHI paper — protocols; analysis scripts; preregistration; data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work.
How do I install Chi Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-reproducibility -a claude-code`. Or copy the skill folder (CHI-Skills/skills/chi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/chi-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Chi Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-reproducibility -a codex`. Or copy the skill folder (CHI-Skills/skills/chi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/chi-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Chi 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 chi-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/chi-reproducibility, .gemini/skills/chi-reproducibility, .github/skills/chi-reproducibility and .opencode/skills/chi-reproducibility in your project.
What does Chi Reproducibility need to run?
Going by SKILL.md and its folder, Chi Reproducibility needs the command-line tools its instructions call (python3, pip and python). Our summary lists: Python 3.
Does Chi Reproducibility access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Chi 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 Chi Reproducibility use?
Chi 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 Chi Reproducibility use?
About 1.4k tokens (SKILL.md is roughly 5.8k 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 Chi Reproducibility?
Skills that share tags, products or a category with Chi Reproducibility: Harness Learn (ruvnet/ruflo, 74k stars), Research Init (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars) and CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Chi 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.