pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Translating technical findings for non-technical audiences. Narrative frameworks (Pyramid Principle, SCQA), plain-language translation, executive summaries, policy briefs, causal language. Use when presenting to stakeholders or reviewing deliverables
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill science-communication --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/science-communicationContext preview
The summary Claude sees to decide when to auto-load this skill.
Translating technical findings for non-technical audiences. Narrative frameworks (Pyramid Principle, SCQA), plain-language translation, executive summaries, policy briefs, causal language. Use when presenting to stakeholders or reviewing deliverables
name: science-communication description: >- Translating technical findings for non-technical audiences. Narrative frameworks (Pyramid Principle, SCQA), plain-language translation, executive summaries, policy briefs, causal language. Use when presenting to stakeholders or reviewing deliverables metadata: audience: research-writers domain: research-communication
Translating technical data science findings for non-technical audiences. Covers audience analysis, narrative frameworks (Pyramid Principle, SCQA, AIDA), plain-language translation, executive summaries, policy briefs, causal language guidance, hedging and uncertainty communication, and accessibility standards. Complements data-scientist visualization references — handles what story to tell and to whom, not how to build charts. Use when presenting findings to stakeholders, writing executive summaries or policy briefs, communicating statistical results to non-statisticians, or reviewing a draft deliverable for clarity and audience fit.
Guidance for translating rigorous data science work into clear, compelling communication for non-technical audiences. This skill is **additive** to data-scientist's visualization references — it covers *what story the chart tells and to whom*, not *how to build the chart*.
**Boundary with data-scientist:** The data-scientist skill handles chart construction, encoding, color palettes, and export standards. This skill handles audience adaptation, narrative structure, plain-language translation, deliverable formatting, and communication quality review.
| File | Purpose | When to Read | |------|---------|--------------| | `audience-analysis.md` | Five audience types with strategy tables | Identifying who you're writing for | | `narrative-frameworks.md` | Six narrative structures with selection guide | Choosing how to structure your story | | `plain-language.md` | Jargon translation, hedging, uncertainty, causal language | Writing findings in accessible language | | `deliverable-templates.md` | Executive summary, policy brief, presentation, talking points | Formatting a specific deliverable type | | `communication-review.md` | 10-point checklist, common pitfalls, seven deadly sins | Reviewing a deliverable before finalization | | `accessibility-equity.md` | WCAG standards, people-first language, equity-aware framing | Ensuring inclusive, accessible communication |
1. **Writing a report or brief?** Start with `audience-analysis.md`, then `narrative-frameworks.md`, then `deliverable-templates.md` 2. **Translating technical findings?** Read `plain-language.md` first 3. **Reviewing a draft?** Go straight to `communication-review.md` 4. **Concerned about equity or accessibility?** Read `accessibility-equity.md`
Identifying your audience?
├─ Academic researchers or peer reviewers
│ └─ ./references/audience-analysis.md (Academic section)
├─ Policymakers or legislative staff
│ └─ ./references/audience-analysis.md (Policy section)
├─ Executives or board members
│ └─ ./references/audience-analysis.md (Executive section)
├─ General public or community members
│ └─ ./references/audience-analysis.md (Public section)
├─ Journalists or media
│ └─ ./references/audience-analysis.md (Media section)
└─ Mixed or unclear audience
└─ ./references/audience-analysis.md (Assessment checklist)Choosing a narrative structure?
├─ Need to deliver a recommendation quickly
│ └─ Pyramid Principle → ./references/narrative-frameworks.md
├─ Framing a problem that needs solving
│ └─ SCQA → ./references/narrative-frameworks.md
├─ Walking through a discovery journey
│ └─ Three-Act Data Story → ./references/narrative-frameworks.md
├─ Translating findings into action
│ └─ "So What?" Framework → ./references/narrative-frameworks.md
├─ Presenting data with narrative and visuals together
│ └─ Data-Narrative-Visual Triad → ./references/narrative-frameworks.md
├─ Persuading stakeholders to act
│ └─ AIDA → ./references/narrative-frameworks.md
└─ Not sure which to use
└─ Selection guide table → ./references/narrative-frameworks.mdTranslating technical language?
├─ Statistical jargon (p-value, confidence interval, etc.)
│ └─ Jargon translation table → ./references/plain-language.md
├─ Expressing how certain you are
│ └─ Hedging language scale → ./references/plain-language.md
├─ Using calibrated uncertainty terms
│ └─ IPCC uncertainty framework → ./references/plain-language.md
├─ Describing causal vs correlational findings
│ └─ Causal language guide → ./references/plain-language.md
├─ General readability improvement
│ └─ Reading level guidance → ./references/plain-language.md
└─ Replacing formal/bureaucratic words
└─ Word replacement list → ./references/plain-language.mdChoosing a deliverable format?
├─ One-page summary for decision makers
│ └─ Executive summary → ./references/deliverable-templates.md
├─ Informing policy decisions
│ └─ Policy brief → ./references/deliverable-templates.md
├─ Presenting to a room
│ └─ Stakeholder presentation → ./references/deliverable-templates.md
├─ Talking to a journalist
│ └─ Media talking points → ./references/deliverable-templates.md
└─ Full research report
└─ (Use REPORT_TEMPLATE.md from agent_reference/)Reviewing before finalization? ├─ Comprehensive quality check │ └─ 10-point checklist → ./references/communication-review.md ├─ Statistical interpretation errors │ └─ Seven deadly sins → ./references/communication-review.md ├─ Common communication pitfalls │ └─ Pitfall catalog → ./references/communication-review.md ├─ Accessibility compliance │ └─ WCAG checklist → ./references/accessibility-equity
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
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