narrative-building

Name: narrative-building
Author: scdenney

34stars0forks

How to install narrative-building?

scdenney/open-science-skills/narrative-building

$npx -y skills add scdenney/open-science-skills --skill narrative-building

Installs into the current project.

Summary

Draft or audit scientific introductions. Use for argument logic, framing, contribution structure, and coherence across multiple studies or experiments.

Files · 1

View on GitHub

SKILL.md

1	# Scientific Narrative Builder
2
3	Related skills. This skill composes with `hypothesis-building` (where the "Why" funnel lands as a falsifiable "If-Then" with a named estimand), `methods-reporting` (where the design the narrative promises gets documented to APSA/JARS/DA-RT standards), and `pre-registration-writing` (where the narrative locks in before data collection).
4
5	## Workflow
6
7	Author or audit an introduction by walking these five steps in order. Each step has a specific output; do not proceed until the prior step's output exists in the draft. See `reference/example-funnel.md` for a worked example.
8
9	### Step 1 — Why: establish the substantive motivation
10	Output: one or two paragraphs naming the real-world social/political tension the study addresses, the specific "invisible" the design will reveal, and the analytical joint where existing theory is silent or conflicted. Do not frame the motivation as a gap-in-literature; frame it as a stake in the world.
11
12	- Identify the "Invisible": Treat surveys and experiments not just as data collection, but as a process to uncover "invisible factors", specifically perceptions, knowledge, beliefs, and reasoning that administrative data cannot capture.
13	- Carve the Analytical Joints: Do not settle for a broad topic. Identify the specific analytical joint or tension where existing theory is silent or conflicted -- "creativity in design is grounded in a capacity to carve a problem at its analytical joints" (Sniderman 2018). The introduction must articulate the specific "why" of the phenomenon before proposing the "how" of the experiment.
14	- The ASK Framework: Druckman (2022) identifies three pathways to research questions: Assessing (observing the world and noticing puzzles), Socializing (conversations with colleagues, students, and practitioners), and Kaput (learning from failed experiments -- "the first thing one should do when an experiment fails is to ask why it failed"). The narrative should be able to trace the question's origin to one of these pathways.
15	- Resist the "Methods-Driven" Temptation: Ensure the research question dictates the method, not vice versa. Explicitly defend why an experiment is the necessary tool for this specific question. "It is crucial to not jump to designs for their novelty, but only to turn to them when they offer an advantage over what could otherwise have been done" (Druckman 2022). Note that "writing your survey questions is already part of the analysis stage" (Stantcheva 2023) -- question design choices are analytical decisions, not logistical ones, and the narrative should convey this.
16	- Contextualization: Situate the study within a "broader account of politics" or social life. The narrative must move from the general social importance to the specific theoretical gap. For policy-relevant studies, explicitly connect the research question to the target policy environment and the populations affected.
17	- Multi-Level Theoretical Framing: When a study includes experiments at different levels of analysis (e.g., individual-level and institutional-level), the introduction must establish the theoretical bridge between levels. State explicitly how reasoning at the micro level (e.g., fairness judgments about individual immigrants) connects to reasoning at the macro level (e.g., legitimacy judgments about governance decisions). The bridge should be conceptual, not just methodological -- explain why the same theoretical mechanism should operate at both levels.
18
19	### Step 2 — Evidence audit: what the literature actually establishes (and where it fails)
20	Output: a literature review section that states, for each prior finding cited, (a) what observable implication it establishes, (b) what design features limit its generalizability, and (c) what remains unresolved. The audit must not collapse into a chronological "studies find X" recital.
21
22	- Systematic Evidence Assembly: Move beyond "selective storytelling." Favor the assembly of estimates from meta-analyses or registries. If these do not exist, explicitly document the search and inclusion criteria to avoid publication bias. Study registries (EGAP, AEA, OSF) make the existence of studies visible even when their results go unpublished -- reference this universe of studies when available (Christensen et al. 2019).
23	- Publication Bias Acknowledgment: When reviewing prior findings, explicitly note where the published literature may overstate effect sizes due to the "file drawer problem" -- the systematic non-publication of null results. If a registered universe of studies exists, the narrative should reference it. Note where prior effect sizes may be inflated by publication bias, and frame the current study's power analysis conservatively (Druckman 2022; Lakens 2025).
24	- *

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narrative-building

34stars0forks

View on GitHub

How to install narrative-building?

scdenney/open-science-skills/narrative-building

$npx -y skills add scdenney/open-science-skills --skill narrative-building

Installs into the current project.

Summary

Draft or audit scientific introductions. Use for argument logic, framing, contribution structure, and coherence across multiple studies or experiments.

Files · 1

View on GitHub

SKILL.md

1	# Scientific Narrative Builder
2
3	Related skills. This skill composes with `hypothesis-building` (where the "Why" funnel lands as a falsifiable "If-Then" with a named estimand), `methods-reporting` (where the design the narrative promises gets documented to APSA/JARS/DA-RT standards), and `pre-registration-writing` (where the narrative locks in before data collection).
4
5	## Workflow
6
7	Author or audit an introduction by walking these five steps in order. Each step has a specific output; do not proceed until the prior step's output exists in the draft. See `reference/example-funnel.md` for a worked example.
8
9	### Step 1 — Why: establish the substantive motivation
10	Output: one or two paragraphs naming the real-world social/political tension the study addresses, the specific "invisible" the design will reveal, and the analytical joint where existing theory is silent or conflicted. Do not frame the motivation as a gap-in-literature; frame it as a stake in the world.
11
12	- Identify the "Invisible": Treat surveys and experiments not just as data collection, but as a process to uncover "invisible factors", specifically perceptions, knowledge, beliefs, and reasoning that administrative data cannot capture.
13	- Carve the Analytical Joints: Do not settle for a broad topic. Identify the specific analytical joint or tension where existing theory is silent or conflicted -- "creativity in design is grounded in a capacity to carve a problem at its analytical joints" (Sniderman 2018). The introduction must articulate the specific "why" of the phenomenon before proposing the "how" of the experiment.
14	- The ASK Framework: Druckman (2022) identifies three pathways to research questions: Assessing (observing the world and noticing puzzles), Socializing (conversations with colleagues, students, and practitioners), and Kaput (learning from failed experiments -- "the first thing one should do when an experiment fails is to ask why it failed"). The narrative should be able to trace the question's origin to one of these pathways.
15	- Resist the "Methods-Driven" Temptation: Ensure the research question dictates the method, not vice versa. Explicitly defend why an experiment is the necessary tool for this specific question. "It is crucial to not jump to designs for their novelty, but only to turn to them when they offer an advantage over what could otherwise have been done" (Druckman 2022). Note that "writing your survey questions is already part of the analysis stage" (Stantcheva 2023) -- question design choices are analytical decisions, not logistical ones, and the narrative should convey this.
16	- Contextualization: Situate the study within a "broader account of politics" or social life. The narrative must move from the general social importance to the specific theoretical gap. For policy-relevant studies, explicitly connect the research question to the target policy environment and the populations affected.
17	- Multi-Level Theoretical Framing: When a study includes experiments at different levels of analysis (e.g., individual-level and institutional-level), the introduction must establish the theoretical bridge between levels. State explicitly how reasoning at the micro level (e.g., fairness judgments about individual immigrants) connects to reasoning at the macro level (e.g., legitimacy judgments about governance decisions). The bridge should be conceptual, not just methodological -- explain why the same theoretical mechanism should operate at both levels.
18
19	### Step 2 — Evidence audit: what the literature actually establishes (and where it fails)
20	Output: a literature review section that states, for each prior finding cited, (a) what observable implication it establishes, (b) what design features limit its generalizability, and (c) what remains unresolved. The audit must not collapse into a chronological "studies find X" recital.
21
22	- Systematic Evidence Assembly: Move beyond "selective storytelling." Favor the assembly of estimates from meta-analyses or registries. If these do not exist, explicitly document the search and inclusion criteria to avoid publication bias. Study registries (EGAP, AEA, OSF) make the existence of studies visible even when their results go unpublished -- reference this universe of studies when available (Christensen et al. 2019).
23	- Publication Bias Acknowledgment: When reviewing prior findings, explicitly note where the published literature may overstate effect sizes due to the "file drawer problem" -- the systematic non-publication of null results. If a registered universe of studies exists, the narrative should reference it. Note where prior effect sizes may be inflated by publication bias, and frame the current study's power analysis conservatively (Druckman 2022; Lakens 2025).
24	- *

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6 picks

Type