Agent Skills
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joc-data-analysis
GitHub用于JoC稿件的数据分析与报告，确保符合匿名评审标准。涵盖诚实报告不确定性、稳健性检验、异质性分析、中介调节及信度评估。强调预注册纪律、计算文本数据的验证以及通过主脚本实现全流程可重复性，避免结果造假。
触发场景
执行主要和辅助分析及构建结果部分
审稿人要求提供稳健性或替代模型检验
协调预注册与探索性分析
提交数据前确保分析可复现
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joc-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "joc-data-analysis",
"description": "Use when executing and reporting the analysis for a Journal of Communication (JoC) manuscript so it survives expert, double-anonymous review — honest uncertainty, robustness, reliability, and triangulation appropriate to quantitative, computational, or content-analytic work. Guides analysis norms; it does not fabricate results."
}
Data Analysis (joc-data-analysis)
JoC reviewers are methodologically sophisticated, and the journal requires a Data Availability
Statement so others can scrutinize how your numbers were produced (see
joc-open-science-and-transparency). Analyze as if both are true — because they are. This skill covers
execution and reporting norms; design decisions live in joc-research-design.
When to trigger
- Running main and supporting analyses; building the results section
- A reviewer asked for robustness, heterogeneity, or alternative specifications
- Reconciling preregistered vs. exploratory analyses
- Making the analysis reproducible before deposit
Analysis norms JoC expects
- Report uncertainty honestly. Confidence/credible intervals and effect sizes, not just stars or p-values; the substantive magnitude and meaning of the estimate.
- Robustness that probes, not decorates. Show specifications that could break the result (alternative measures, samples, estimators, covariate sets), and say what you learn.
- Heterogeneity with discipline. Pre-specify subgroups where possible; correct for multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
- Mediation/moderation done right. For PROCESS/SEM-style models, justify the causal ordering; report indirect effects with bootstrap CIs; acknowledge cross-sectional limits on process claims.
- Measurement and reliability. Report scale reliability (e.g., alpha/omega) and, for content analysis, intercoder reliability; show results are not an artifact of a coding/scaling choice.
- Preregistration discipline. Clearly separate registered from exploratory analyses; reconcile and justify deviations from the plan.
Computational / text-as-data specifics
- Document model/version, hyperparameters, seeds, and validation against human-labeled samples.
- For topic models/embeddings/LLM pipelines: report stability and a validation step; don't treat outputs as ground truth.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from the (raw or constructed) data.
- Set and report seeds for bootstrap, simulation, and any stochastic step.
- Pin software/package versions (
renv.lock,requirements.txt, recorded installs; note Mplus/SPSS versions). - Keep table/figure numbers in the manuscript matched to script outputs.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Journal of Communication spans experiments, surveys, and content analysis; randomization inference for experiments, DiD/IV for observational media-effects claims.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.
Anti-patterns
- Stars-only tables with no effect sizes or intervals
- "Robustness" that only reruns near-identical specs to manufacture stability
- p-hacking / fishing for a significant interaction; HARKing exploratory results into hypotheses
- Reporting a content analysis without intercoder reliability
- A results section whose numbers the code cannot reproduce
Evidence pass for Journal of Communication
Treat this skill as an executable review pass, not a prose hint. First lock the communication process, platform/media setting, construct measurement, and study design; then judge whether the current manuscript answers the venue's real reader: communication reviewers who balance theory, media context, measurement, and social implications.
- Do the pass: Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
- Return a ledger: give
claim / evidence / risk / manuscript locationrows, so the next agent can edit rather than rediscover the issue. - Sibling guard: compare against Communication Research for quantitative communication, New Media & Society for platform focus, Human Communication Research for theory testing; if a sibling owns the contribution, recommend re-routing before polishing format.
- Stop condition: do not give submission-ready advice until the pack's
resources/official-source-map.mdhas been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.
Output format
【Main estimate】magnitude + interval + substantive meaning
【Identification/validity check】(per research-design) result
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Reliability】scale / intercoder reliability reported?
【Registered vs exploratory】clearly separated?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】joc-tables-figures
Supplementary resources
../../resources/external_tools.md— estimation, reliability, mediation/SEM, and text-as-data packages../../resources/official-source-map.md— Data Availability Statement requirement
版本历史
- 1839142 当前 2026-07-05 13:28
