claude

cscw-skills

Twelve CSCW-specific skills covering the PACMHCI journal-model pipeline from topic routing through R&R response letters to journal-issue publication, grounded in cscw.acm.org CFP and rolling-model pages, ACM DL PACMHCI records, and dblp, checked 2026-07-08.

1,413 on GitHubby Bryce Wangv1.1.0MITupdated 1 month agoacademic
/

Plugin cscw-skills from marketplace awesome-journal-skills in brycewang-stanford/Awesome-Journal-Skills. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (12)

  • cscw-artifact-evaluation

    Use when packaging what stands behind a CSCW paper — systems, analysis pipelines, codebooks, instruments, datasets from real communities — for review-time scrutiny and post-acceptance release, where community-data ethics constrain release more than any badge checklist.

  • cscw-author-response

    Use when a CSCW paper draws Revise and Resubmit or Revise for External Review — triaging reviewer requests, running the weeks-long revision as a project, and writing the point-by-point response letter that the same reviewers will read before they reread anything else.

  • cscw-camera-ready

    Use when a CSCW paper is accepted and enters PACMHCI production — executing minor-change conditions, deanonymizing text and artifacts, ACM journal e-rights and TAPS steps, issue assignment, and the separate track of presenting at the next CSCW conference.

  • cscw-experiments

    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-related-work

    Use when positioning a CSCW paper's related work — engaging the venue's own concept lineage, the PACMHCI-era literature, neighboring HCI venues, and the social sciences CSCW borrows from, with era-correct citations and anonymity-safe self-references for journal-model review.

  • cscw-reproducibility

    Use when strengthening the transparency of a CSCW paper — auditable qualitative analysis trails, documented trace pipelines, codebooks and instruments, and honest data-availability statements when community and participant data cannot ethically be shared.

  • cscw-review-process

    Use when interpreting where a CSCW paper stands in review — the PACMHCI journal model's decision vocabulary (Revise for External Review, Revise and Resubmit, Conditional Accept), same-reviewer re-review, the rolling editorial board, and what each outcome statistically implied in the last cycle.

  • cscw-submission

    Use when readying a CSCW manuscript for upload — confirming the current pathway (rolling via Manuscript Central as of mid-2026), the single-column ACM template, the word-length scrutiny band, anonymization of author and community identifiers, ethics statements, and desk-risk triage.

  • cscw-supplementary

    Use when assembling supplementary materials for a CSCW submission — appendices, codebooks, interview guides, survey instruments, analysis code, and data extracts — deciding what accompanies a journal-model paper, anonymized end to end and consistent across R&R rounds.

  • cscw-topic-selection

    Use when deciding whether a project belongs at CSCW — is the group, community, or organization the real unit of analysis? — and running the routing decision against CHI, ICWSM, DIS, GROUP, TOCHI, and social-science journals before any drafting or platform account setup begins.

  • cscw-workflow

    Use when planning a CSCW submission timeline end to end — the PACMHCI journal model, the retired fixed cycles versus the rolling 2027+ pathway, Revise-and-Resubmit rounds that span months, and back-planning so an acceptance lands before the conference-year presentation cutoff.

  • cscw-writing-style

    Use when drafting or revising CSCW prose — group-level framing, concepts a field can reuse, participant voice handled with care, theory that earns its keep, and length discipline under the 5,000-12,000-word scrutiny band where contribution is weighed against every page.

View source on GitHub copaper.ai

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