A skill your agent uses when designing or auditing the empirical work behind a CSCW paper — interview and ethnographic rigor, trace and log analysis, surveys, deployments, and mixed methods —…

MITAuto-check passedDevOps & Cloud

Install Cscw Experiments

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cscw-experiments -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cscw-experiments --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/CSCW-Skills/skills/cscw-experiments .claude/skills/cscw-experiments && rm -rf skills-src

Use ~/.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/

Facts

Skill name
cscw-experiments
GitHub stars
1.2k
Token cost
~1.3k tokens
SKILL.md length
576 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 empirical work behind a CSCW paper — interview and ethnographic rigor, trace and log analysis, surveys, deployments, and mixed methods —…

  • Auditing the empirical work behind a CSCW paper — interview and ethnographic rigor
  • SKILL.md covers Rigor, by method family, Group-level design decisions, Ethics as design, not paperwork and Evidence-plan skeleton
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Trace and log analysis

What it does

Cscw Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the empirical work behind a CSCW paper — interview and ethnographic rigor, trace and log analysis, surveys, deployments, and mixed methods — matching each method's own validity standard and the ethics of studying real communities.

Its SKILL.md is about 1.3k 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 DevOps & Cloud, covering Deployment. 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 empirical work behind a CSCW paper — interview and ethnographic rigor
  • Trace and log analysis
  • Mixed methods — matching each methods own validity standard and the ethics of studying real communities

Example prompts

  • “/cscw-experiments”

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cscw Experiments loads about 1.3k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 576 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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). 576 words, ~1,289 tokens.

Download SKILL.mdSave it as .claude/skills/cscw-experiments/SKILL.md (or your agent's skills folder).
name
cscw-experiments
description
Use when designing or auditing the empirical work behind a CSCW paper — interview and ethnographic rigor, trace and log analysis, surveys, deployments, and mixed methods — matching each method's own validity standard and the ethics of studying real communities.

CSCW Empirical Work

"Experiments" is the wrong word for most CSCW evidence, and that is the point. The venue is deliberately methods-pluralist: interview studies, ethnography, large-scale trace analysis, surveys, field deployments, controlled experiments, and mixed designs all publish here — each judged by its own tradition's standard of rigor, not by a quantitative default. The commonest reviewing disaster is a paper that borrows a method without its discipline.

Rigor, by method family

MethodWhat rigor means hereWhat reviewers flag
InterviewsPurposeful sampling with a rationale; saturation or a defended stopping rule; a described analysis process (coding approach, memoing, disagreement handling)"We interviewed 12 people and themes emerged" with no analytic trail
Ethnography / field observationDuration and depth of engagement; researcher's relationship to the setting; thick description that earns the interpretationDrive-by observation labeled ethnography
Trace / log analysisConstruct validity (does the log field measure the practice claimed?); an identification strategy for any causal wording; robustness to platform quirks (bots, deleted content, API sampling)Correlational results narrated causally; metrics inherited from the platform unexamined
SurveysInstrument provenance or validation; sampling frame vs. claimed population; nonresponse handlingConvenience sample generalized to "users"
Deployments / experimentsGenuine group-level conditions; power analysis where inference is statistical; contamination between conditions addressedN = groups treated as N = individuals
Mixed methodsAn explicit integration logic — which strand leads, which bounds, where they may disagreeTwo mini-studies stapled together, each too thin to stand

Two pluralism rules cut across all rows:

  • Do not apply one tradition's checklist to another's method. Demanding inter-rater reliability statistics from an interpretivist analysis, or accepting vibes in place of identification from a causal claim, are the same category of error. State which tradition your analysis works in, then meet that tradition fully.
  • Qualitative sample sizes are justified by purpose, not by envy. Twenty-four well-chosen moderators can ground a concept; two million log rows cannot rescue a construct that measures the wrong thing.
Show full SKILL.md (258 more words)Show less

Group-level design decisions

  • Unit of analysis and unit of observation must be named separately. You may observe individuals (interviewees, accounts) while claiming about collectives (teams, communities); the analysis section must say how the aggregation is licensed.
  • Sample communities, not just people. For multi-community studies, describe how communities were chosen and what variation the set covers — community selection is the qualitative analogue of a sampling frame.
  • Time matters. Cooperative practices are rhythms (shift rotations, release cycles, norm renegotiations). A snapshot design should say what it cannot see.

Ethics as design, not paperwork

CSCW evidence usually comes from real communities with stakes in the findings. Reviewers read the ethics description as part of the method:

  • State IRB/ethics-review status and the consent posture for each data source — including whether "public" trace data was treated as fair game and why that is defensible for this community.
  • Plan quote handling at design time: verbatim quotes from small or hostile-scrutiny communities can be reverse-searched; commit to paraphrase or alteration policies and disclose them.
  • Consider the community's exposure, not only the individual's: naming a small community can harm it even with every user anonymized (see cscw-artifact-evaluation for release-time handling).

Evidence-plan skeleton

text
[Claim]        <the group-level finding this study must support>
[Tradition]    interpretivist / positivist / computational / mixed (lead strand: ___)
[Observation]  who or what is observed, at what unit and timescale
[Aggregation]  how individual observations license collective claims
[Validity]     the ONE threat most likely to sink this design + mitigation
[Ethics]       consent posture per data source; quote policy; community exposure
[Stop rule]    what tells you data collection is done

Draft this skeleton before collecting anything; paste the filled version into the methods section as its outline. Under Revise and Resubmit, new data collection is often infeasible — a design that anticipates the obvious objection is the cheapest insurance the journal model offers.

Method norms are stable venue culture; submission-mechanics facts elsewhere in this pack carry the 2026-07-08 access date and should be re-verified independently.

© 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 CSCW-Skills/skills/cscw-experiments of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Categories

Questions about Cscw Experiments

What does Cscw Experiments do?

A skill your agent uses when designing or auditing the empirical work behind a CSCW paper — interview and ethnographic rigor, trace and log analysis, surveys, deployments, and mixed methods —…. Cscw Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the empirical work behind a CSCW paper — interview and ethnographic rigor, trace and log analysis, surveys, deployments, and mixed methods — matching each method's own validity standard and the ethics of studying real communities.

When should I use Cscw Experiments?

Cscw Experiments fits situations like: auditing the empirical work behind a CSCW paper — interview and ethnographic rigor; trace and log analysis; mixed methods — matching each methods own validity standard and the ethics of studying real communities.

How do I install Cscw Experiments in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cscw-experiments -a claude-code`. Or copy the skill folder (CSCW-Skills/skills/cscw-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cscw-experiments in your project. Claude Code loads it when a task matches its description.

How do I install Cscw Experiments in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cscw-experiments -a codex`. Or copy the skill folder (CSCW-Skills/skills/cscw-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cscw-experiments in your project. Codex loads it when a task matches its description.

Can I use Cscw 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 cscw-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/cscw-experiments, .gemini/skills/cscw-experiments, .github/skills/cscw-experiments and .opencode/skills/cscw-experiments in your project.

What does Cscw Experiments need to run?

SKILL.md names no scripts, command-line tools or credentials: Cscw Experiments is instructions for the agent only.

Does Cscw 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 Cscw 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 Cscw Experiments use?

Cscw 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 Cscw Experiments use?

About 1.3k tokens (SKILL.md is roughly 5.2k 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 Cscw Experiments?

Skills that share tags, products or a category with Cscw Experiments: Kubeshark Installer (kubeshark/kubeshark, 12k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars) and Vercel (remotion-dev/remotion, 63k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cscw Experiments?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.