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A skill your agent uses when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work…
Install Chi Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-experiments --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-experiments .claude/skills/chi-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-experiments into .claude/skills/chi-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-experiments", 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-experimentsType 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-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-experiments --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-experiments .agents/skills/chi-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-experiments into .agents/skills/chi-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-experiments", 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-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-experiments --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-experiments .cursor/skills/chi-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-experiments into .cursor/skills/chi-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-experiments", 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-experiments--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-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-experiments --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-experiments .gemini/skills/chi-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-experiments into .gemini/skills/chi-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-experiments", 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-experimentsInstalls 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-experiments -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-experiments .github/skills/chi-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-experiments into .github/skills/chi-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-experiments", 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-experiments -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-experiments --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-experiments .opencode/skills/chi-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-experiments into .opencode/skills/chi-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-experiments", 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-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 651 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work…
- Works in 5 steps: Recruitment channel and criteria;… → Ethics approval (IRB or equivalent)… → Demographics reported to the level the… → …
- Auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type
- SKILL.md covers Match the evidence to the…, Quantitative discipline, Qualitative discipline and Participants and ethics are…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Chi Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work rigorous and auditable, reporting participants and ethics properly, and avoiding the ADR-Data and ADR-Method screening grounds.
Its SKILL.md is about 1.5k 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 Development, covering Architecture decision records and Statistics. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type
- Powering quantitative experiments
- Making qualitative work rigorous and auditable
- Reporting participants and ethics properly
Example prompts
- “/chi-experiments”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Recruitment channel and criteria; compensation and its local adequacy.
- Ethics approval (IRB or equivalent) named, or the honest statement of why the
- Demographics reported to the level the claims need: an accessibility claim needs
- Risks and mitigations for sensitive topics; deception disclosed and debriefed.
- Data handling: anonymization, storage, deletion timeline.
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
Chi Experiments loads about 1.5k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 651 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). 651 words, ~1,545 tokens.
Download SKILL.mdSave it as
.claude/skills/chi-experiments/SKILL.md (or your agent's skills folder).- name
- chi-experiments
- description
- Use when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work rigorous and auditable, reporting participants and ethics properly, and avoiding the ADR-Data and ADR-Method screening grounds.
CHI Experiments and Studies
"Experiments" at CHI means human evidence: controlled lab studies, field deployments, interview and diary studies, surveys, log analyses, and mixtures of these. Two of the four assisted desk-reject rubric grounds CHI now screens with — ADR-Data (grossly insufficient data for the claims) and ADR-Method (grossly insufficient methodological detail or transparency) — are study-design judgments made before full review. Evidence design is therefore survival, not polish.
Match the evidence to the claim, not to habit
| Claim shape | Evidence that convinces CHI reviewers | Chronic mismatch seen in reviews |
|---|---|---|
| "Technique X outperforms Y" | Controlled comparison, counterbalanced, powered, effect sizes | Underpowered n=12 with p-values only |
| "Users experience/need Z" | Interviews or diary study to saturation, systematic analysis | Cherry-picked quotes, no analysis method stated |
| "System S is usable/useful in practice" | Field deployment with real tasks over time | One-hour lab walkthrough of a demo |
| "Population P interacts differently" | Sampling strategy that can reach P, comparative design | Convenience sample of students standing in for P |
| "Design guideline G holds" | Multiple probes/instantiations, triangulated methods | Single prototype, single context, universal claim |
| "Measure M captures construct C" | Validation study: reliability, convergent validity | New questionnaire used, never validated |
Mixed methods are a CHI signature: a quantitative result explains that, the paired qualitative strand explains why. If you run both, integrate them in the analysis — a qualitative section bolted after the ANOVA reads as decoration.
Quantitative discipline
- Power before running. Decide the smallest effect worth detecting, then size the study; report the analysis. Post-hoc power excuses convince nobody.
# a priori sample size for a within-subjects comparison (paired t-test)
from statsmodels.stats.power import TTestPower
n = TTestPower().solve_power(effect_size=0.5, alpha=0.05, power=0.8,
alternative="two-sided")
print(round(n)) # ≈ 34 participants for d=0.5 — n=12 detects only d≈0.88- Report effect sizes with confidence intervals alongside test statistics; CHI's methods community has campaigned against naked p-values for a decade.
- State the analysis plan's provenance: preregistered, planned-but-unregistered, or exploratory. Label exploratory findings as such instead of promoting them.
- Check assumptions (normality, sphericity) and name the corrections used; Likert and time data routinely need non-parametric or transformed treatment.
- Counterbalance and report order effects for within-subjects interaction studies.
Qualitative discipline
Qualitative work at CHI is judged on rigor, not sample size. What reviewers audit:
- The named analysis method actually followed — reflexive thematic analysis, grounded-theory procedures, interaction analysis — with its own reporting conventions honored (e.g., do not report inter-rater reliability for reflexive TA while claiming a codebook emerged from consensus; pick a coherent paradigm).
- Recruitment, who the participants are, and what they were paid, in a table.
- Enough quote evidence per theme to show the theme is in the data, attributed with participant IDs (P1–Pn), balanced across participants.
- Researcher positionality where the topic makes the researcher's standpoint analytically relevant (common in accessibility, health, and marginalized-community work) — a norm in parts of CHI, not a universal requirement.
Show full SKILL.md (221 more words)Show less
Participants and ethics are results-page material
CHI reviewers read the participants section as evidence, and screening cites it:
- Recruitment channel and criteria; compensation and its local adequacy.
- Ethics approval (IRB or equivalent) named, or the honest statement of why the
jurisdiction requires none — plus consent procedure for data, recordings, and
any footage reused in the video figure (
chi-supplementary). - Demographics reported to the level the claims need: an accessibility claim needs disability descriptions; a cross-cultural claim needs more than "US and EU".
- Risks and mitigations for sensitive topics; deception disclosed and debriefed.
- Data handling: anonymization, storage, deletion timeline.
Deployment and AI-system studies
For field deployments, report duration, retention, and usage telemetry honestly —
attrition is data. For AI-infused interfaces, evaluate both the model and the human
experience: state model version, prompts/configurations, and failure behavior during
the study window, because "users trusted the system" is uninterpretable without
knowing how often the system was wrong. Pin model versions; a study run on a moving
API is unreplicable by construction (chi-reproducibility).
Pre-submission evidence audit
Walk each headline claim backwards: which figure/table/theme supports it, from which data, collected from whom, analyzed how? Any claim that dead-ends is either cut, scoped down ("in our lab task, for our participants..."), or flagged as future work. This single pass defuses most ADR-Data exposure.
Output format
[Contribution type] <from chi-topic-selection>
[Evidence inventory] <study 1: design, n, analysis> · <study 2: ...>
[Claim-evidence dead ends] <claims without support, or none>
[Quant status] power: <basis> / effect sizes+CIs: yes/no / plan provenance: prereg|planned|exploratory
[Qual status] method named+followed: yes/no / quotes balanced: yes/no
[Ethics] approval: <body or n/a+reason> / compensation: <amount> / consent for footage: yes/no
[ADR exposure] Data: low/med/high · Method: low/med/high — <weakest point>© 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-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Chi Experiments 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 Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | Automated safety check: Pass | MIT | |
| PR Design DocOpenHands/OpenHands | 90k | — | ~2.4k | Automated safety check: Pass | MIT | |
| Review PRapache/shardingsphere | 21k | — | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Cto AdvisorIbrahim-3d/orchestrator-supaconductor | 380 | 4 repos | ~2.4k | Automated safety check: Pass | MIT | |
| Improve Codebase Architectureywwynm/EverythingDone | 144 | 15 repos | ~1.3k | Automated safety check: Pass | GPL-3.0 | |
| Domain Modelingbrim-borium/spotify_sdk | 166 | 5 repos | ~806 | Automated safety check: Pass | Apache-2.0 |
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Review PR
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Review Apache ShardingSphere or user-authorized downstream pull requests and PR discussions from public or authorized repository evidence.
Cto Advisor
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Technical leadership guidance for engineering teams, architecture decisions, and technology strategy.
Improve Codebase Architecture
ywwynm/EverythingDone
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/.
Domain Modeling
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Build and sharpen a project's domain model. An agent skill from brim-borium/spotify_sdk.
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Questions about Chi Experiments
What does Chi Experiments do?
A skill your agent uses when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work…. Chi Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work rigorous and auditable, reporting participants and ethics properly, and avoiding the ADR-Data and ADR-Method screening grounds.
When should I use Chi Experiments?
Chi Experiments fits situations like: auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type; powering quantitative experiments; making qualitative work rigorous and auditable; reporting participants and ethics properly.
How do I install Chi Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-experiments -a claude-code`. Or copy the skill folder (CHI-Skills/skills/chi-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/chi-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Chi Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-experiments -a codex`. Or copy the skill folder (CHI-Skills/skills/chi-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/chi-experiments in your project. Codex loads it when a task matches its description.
Can I use Chi Experiments 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-experiments -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-experiments, .gemini/skills/chi-experiments, .github/skills/chi-experiments and .opencode/skills/chi-experiments in your project.
What does Chi Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Chi Experiments is instructions for the agent only. Our summary lists: Python 3.
Does Chi Experiments 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 Chi Experiments 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 Experiments use?
Chi Experiments 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 Experiments use?
About 1.5k tokens (SKILL.md is roughly 6.2k 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 Experiments?
Skills that share tags, products or a category with Chi Experiments: PR Design Doc (OpenHands/OpenHands, 90k stars), Review PR (apache/shardingsphere, 21k stars), Cto Advisor (Ibrahim-3d/orchestrator-supaconductor, 380 stars) and Improve Codebase Architecture (ywwynm/EverythingDone, 144 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Chi Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.