quant-findings-writer

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Draft publication-ready Results/Findings sections for quantitative sociology articles. Guides cluster selection, arc construction, paragraph-level moves, and writing techniques based on genre analysis of 83 Social Problems/Social Forces articles across secondary-survey, administrative-data, and content-analysis methods. Use when the user wants to write, draft, or revise a Results section for a quantitative or content-analysis paper. Also use when the user asks for help structuring findings, organizing results, or translating statistical output into publication-ready prose.

1installs

Sourcenealcaren/sociology-skillset

Added on2026-03-08

NPX Install

npx skill4agent add nealcaren/sociology-skillset quant-findings-writer

SKILL.md Content

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Quantitative Findings Writer

Draft Results/Findings sections for quantitative sociology articles using structural patterns discovered in 83 Social Problems and Social Forces articles.

Project Integration

This skill reads from

project.yaml

when available:

yaml

# From project.yaml
type: quantitative  # This skill is for quantitative projects
paths:
  drafts: drafts/sections/
  tables: output/tables/
  figures: output/figures/

Project type: This skill is designed for quantitative projects.

Consumes output from r-analyst or stata-analyst (tables, figures, interpretation memos from Phase 5).

Updates

progress.yaml

when complete:

yaml

status:
  results_draft: done
artifacts:
  results_section: drafts/sections/results-section.md

Connection to Other Skills

Skill	Relationship	Details
r-analyst	Upstream	Produces tables, figures, interpretation memos (Phase 5 output)
stata-analyst	Upstream	Same as r-analyst but for Stata
article-bookends	Downstream	Takes results section as input for framing
methods-writer	Parallel	Methods section written alongside or before results
lit-synthesis	Upstream	Provides theoretical framework for theory-linking
prose-craft	Craft guide	Sentence/paragraph benchmarks (evaluative mode); tone, anti-LLM rules

File Management

This skill uses git to track progress across phases. Before modifying any output file at a new phase:

Stage and commit current state:

git add [files] && git commit -m "quant-findings-writer: Phase N complete"

Then proceed with modifications.

Do NOT create version-suffixed copies (e.g.,

-v2

,

-final

,

-working

). The git history serves as the version trail.

Workflow

Phase 1: Orient

Gather from the user:

Method type: secondary-survey-analysis, administrative-data, or content-analysis
Key results: tables, model output, or thematic findings to present
Theoretical predictions: hypotheses or expectations the results address
Target length: typical is 12-25 paragraphs (2,000-5,000 words)

If the user has already written a draft, read it and assess which cluster it most resembles before suggesting revisions.

Phase 2: Select Cluster

Present the 7 clusters with their canonical arcs. Recommend 1-2 based on method type and analytic strategy:

Cluster	Best for	Arc
Progressive Model Builder	Regression-heavy papers building from simple to complex specs	DESCRIBE → BASELINE → ELABORATE → MECHANISM → ROBUSTNESS
Hypothesis Tester	Papers with numbered H1/H2/H3 predictions	SETUP → BASELINE → ELABORATE → SUBGROUP → SUMMARY
Decomposition Analyst	Gap/disparity papers using Oaxaca-Blinder, mediation	DESCRIBE → BASELINE → DECOMPOSE → MECHANISM → ROBUSTNESS
Subgroup Comparator	Heterogeneity-focused papers (by race, gender, class)	DESCRIBE → BASELINE → SUBGROUP → COMPARISON → ROBUSTNESS
Temporal Tracker	Event studies, trend analysis, periodization	TEMPORAL → BASELINE → TEMPORAL → SUBGROUP → ROBUSTNESS
Thematic Explorer	Content analysis with qualitative themes/frames	THEMATIC → THEMATIC → THEMATIC → SUMMARY
Causal Inference Specialist	DiD, IV, RDD, matching designs	SETUP → BASELINE → ELABORATE → ROBUSTNESS → MECHANISM

Selection heuristics:

Survey data + model progression → Progressive Model Builder
Admin data + quasi-experimental design → Causal Inference Specialist
Admin data + inequality decomposition → Decomposition Analyst
Any method + explicit hypotheses → Hypothesis Tester
Any method + group comparisons as central question → Subgroup Comparator
Content analysis + thematic coding → Thematic Explorer
Panel/longitudinal + change over time → Temporal Tracker

After the user selects a cluster, read the matching guide from

clusters/{cluster-name}.md

for detailed arc, paragraph budget, signature techniques, and exemplar patterns.

Phase 3: Build the Arc

Using the cluster guide, construct a section outline:

Map each major finding/table to a MOVE from the standardized vocabulary
Sequence moves following the cluster's canonical arc
Allocate paragraphs using the cluster's paragraph budget
Identify the opening and closing moves

Standardized move vocabulary:

Move	Function
DESCRIBE	Descriptive statistics, sample overview, bivariate patterns
SETUP	Methodological restatement, analytic strategy recap
BASELINE	Initial/simple models, main effects without interactions
ELABORATE	Add complexity: interactions, nonlinearities, mediators
DECOMPOSE	Formal decomposition (Oaxaca-Blinder, mediation, etc.)
SUBGROUP	Heterogeneity by subgroups (race, gender, class)
MECHANISM	Mediation, mechanism tests, process tracing
ROBUSTNESS	Sensitivity analysis, alternative specs, placebo tests
THEMATIC	Substantive theme/topic analysis
TEMPORAL	Over-time patterns, periodization, event studies
COMPARISON	Cross-group or cross-context comparison
VISUAL	Key figure/visualization driving the narrative
SUMMARY	Brief recap paragraph
TRANSITION	Bridge to discussion section

Present the arc to the user as a numbered outline with paragraph counts per move.

Phase 4: Draft

Write each move following corpus norms. Consult

techniques/techniques.md

for the full technique catalog.

Opening paragraph (choose one based on cluster):

Table reference (58% of corpus): "Table 2 presents results from..."
Sample description (20%): "Before turning to multivariate models, I describe..."
Hypothesis restatement (14%): "Recall that H1 predicted..."
Methodological setup (5%): "To estimate the causal effect, I use..."

Body paragraphs:

Lead with the finding, not the method
Translate every key coefficient into substantive terms (85% of corpus does this)
Use attenuation tracking when adding controls: "the coefficient falls from .34 to .21"
Connect to theory at moderate density: ~1 theory reference per 3-4 paragraphs for most clusters
Report null findings transparently (45% of corpus does this)

Closing paragraph (choose one):

Robustness cascade (18%): "Results are robust to..."
Strongest finding (18%): save the most important result for the end
Subgroup analysis (17%): end with heterogeneity
Supplemental reference (14%): "Additional specifications in Appendix Table A3..."
Summary (11%): brief recap of all findings

Cross-cutting norms:

Median section length: ~18 paragraphs, 3 tables/figures referenced
75% use hybrid table strategy: tables anchor the narrative but prose interprets
55% link results to theory heavily; 40% moderately; only 5% minimally
Distinguish statistical from practical significance when warranted

Phase 5: Calibrate

After drafting, check against cluster norms:

Does the arc match the canonical sequence?
Is the paragraph budget balanced?
Are tables referenced with interpretive guidance, not just pointed at?
Is theory linking at the right density for the cluster?
Are robustness checks present if the cluster expects them?
Are null findings acknowledged rather than buried?

Present the draft with a brief calibration note.

Reference Files

Cluster guides (read the one matching the selected cluster):

```
clusters/progressive-model-builder.md
```
```
clusters/hypothesis-tester.md
```
```
clusters/decomposition-analyst.md
```
```
clusters/subgroup-comparator.md
```
```
clusters/temporal-tracker.md
```
```
clusters/thematic-explorer.md
```
```
clusters/causal-inference-specialist.md
```

```
techniques/techniques.md
```
— 20 writing techniques with descriptions and frequency data
```
references/corpus-statistics.md
```
— summary statistics from the 83-article analysis corpus