Empirical integrity

Bootstrap (first run in this project)

Before applying the rules below, check that this skill's regression tests are installed in the project:

python3 "${CLAUDE_PLUGIN_ROOT:-.}/scripts/setup/check_project_scaffold.py" \
    scripts/test_common.py scripts/test_empirical_integrity.py

If the output lists missing files, install them:

python3 "${CLAUDE_PLUGIN_ROOT:-.}/scripts/setup/install_templates.py" \
    test_common.py:scripts/test_common.py \
    test_empirical_integrity.py:scripts/test_empirical_integrity.py

Then tell the user what was installed and flag that the top of scripts/test_empirical_integrity.py has project-specific settings (paths to manuscript / stats / figures, plus the FORBIDDEN_LITERALS tuple) they should review.

Next, check whether the project's .claude/settings.json has the analysis/results/ deny rules that protect manuscript_stats.json from hand-editing:

python3 "${CLAUDE_PLUGIN_ROOT:-.}/scripts/setup/check_deny_rules.py" \
    "Write(//**/analysis/results/**)" "Edit(//**/analysis/results/**)"

If the output lists missing rules, add them via the shipped helper:

python3 "${CLAUDE_PLUGIN_ROOT:-.}/scripts/setup/add_deny_rules.py" \
    "Write(//**/analysis/results/manuscript_stats.json)" \
    "Edit(//**/analysis/results/manuscript_stats.json)" \
    "Write(//**/analysis/results/**)" \
    "Edit(//**/analysis/results/**)"

The helper is idempotent (no-ops for rules already present) and creates .claude/settings.json if it doesn't exist.

These rules block the agent's Write and Edit tools from touching anything under analysis/results/ (including manuscript_stats.json and coded_papers.csv). Regeneration via Bash(python3 analysis/manuscript_stats.py) is unaffected because the deny rules target only Write/Edit, not Bash.

If the project has no CLAUDE.md yet, suggest using ${CLAUDE_PLUGIN_ROOT:-.}/templates/manuscript_claude_md.md as a starting point — but don't write it without the user's say-so. CLAUDE.md is user-owned.

Core rule

Never write a number, table, statistic, percentage, test result, or quantitative claim that does not come directly from a script reading the underlying data. Do not round, adjust, or approximate data values.

This rule extends to methodological facts: search dates, database names, model names, keyword strings, year bounds, screening counts, and any other statement that describes how the data was produced. A search date typed as "October 2025" is just as much a hallucination risk as a p-value typed as "0.03". Methodological facts must come from a metadata file written by the pipeline (e.g. search_metadata.json) and reach the manuscript through the stats dictionary — never typed in prose, never read directly from the raw metadata file. See Inline expressions below for the pattern.

The authoritative pipeline

Every quantitative claim in a research report must trace back through this chain:

analysis/raw/  →  analysis scripts  →  analysis/results/  →  manuscript
                                                             (code chunks or inline expressions)

analysis/results/ is the single source of truth for numbers cited in the manuscript. The directory is written exclusively by analysis scripts — typically several of them, each owning a slice of the output (e.g. model-fitting, descriptive statistics, figures, and the manuscript_stats.py flat-dict builder). Analysis scripts routinely produce far more files than the manuscript cites — exploratory outputs, supplementary tables, diagnostic plots. That is fine. The subset that ends up in the manuscript is whatever the code chunks and inline expressions actually read; the rest lives in analysis/results/ as a record of the analysis but has no special status until a code chunk or inline expression pulls from it.

The manuscript itself reads from these files — it never reads raw data directly, never modifies them, and never produces numbers of its own.

Project-specific paths: Check CLAUDE.md. If the project defines different locations, use those. Default conventions: raw data in analysis/raw/, authoritative outputs in analysis/results/, analysis scripts in analysis/, stats-dictionary builder at analysis/manuscript_stats.py.

Two acceptable ways to put numbers in the manuscript

1. Code chunks that render tables from authoritative results

A manuscript code chunk can render a table two ways, both acceptable as long as every value traces back to analysis/results/ and the chunk contains presentation logic only, never analysis logic (no model fitting, no recomputation, no data transformation).

Pre-baked CSV or markdown. The analysis script writes a ready-to- print CSV or markdown file, and the chunk reads and emits it. Robust because the manuscript has no dependency on analysis-time packages beyond a minimal reader; portable across render environments; plain text is human-inspectable.

# Quarto / Python
import pandas as pd
df = pd.read_csv("analysis/results/table2_results.csv")
print(df.to_markdown(index=False))

# Quarto / R
df <- read.csv("analysis/results/table2_results.csv")
knitr::kable(df, caption = "...")

Stored estimation result formatted inline. The analysis script writes a stored object — a fitted model, a summary list, an .rds or .pkl file — and the chunk formats it with a publication-quality table package: modelsummary, gt, kable, tinytable, stargazer, or pandas.to_markdown. No CSV intermediate. Use when you want control over table features that are awkward to pre-bake — model-comparison columns, fit-statistic footers, coefficient-star conventions, custom rounding per column — and when the render environment has the same package versions as the analysis environment.

# Quarto / R — formatting a stored model fit with modelsummary
library(modelsummary)
fit <- readRDS("analysis/results/fit_main.rds")
modelsummary(fit, output = "markdown", stars = TRUE)

Either way, the chunk must read from the authoritative results directory — not from raw data, not from intermediate files. If the file does not exist yet, run the analysis script that produces it first (check the analysis/ directory for the matching producer — the convention is one script per coherent output or output family).

2. Inline expressions from a pipeline-produced stats dictionary (preferred for prose numbers)

Every prose number comes from a single flat dictionary produced by a project-owned stats module — typically analysis/manuscript_stats.py with a build_stats() function — and written to analysis/results/manuscript_stats.json. The module reads every relevant pipeline output (raw data, screening logs, search_metadata.json, coding tables, model config) and returns one dotted-key mapping:

{
  "search.from_year": 2010,
  "search.databases": "Scopus, Web of Science, OpenAlex",
  "search.unique_dois": 1708,
  "screen.abstract.n_include": 143,
  "screen.fulltext.n_include": 81,
  "provenance.fulltext.model": "claude-sonnet-4-6",
  "provenance.fulltext.prompt_version": "v1-2026-04-14",
  # ...
}

The manuscript loads the dict once in a setup chunk and looks up keys inline:

System	Setup chunk	Prose lookup
Quarto	from manuscript_stats import build_stats; s = build_stats()	`{python} s['search.unique_dois']`
R Markdown	stats <- read_stats()	`r stats$search.unique_dois`
Jupyter	s = build_stats() in a prior cell	variable reference

One dictionary, not two. All pipeline provenance — search dates, database names, model identifiers, prompt versions, year bounds — lives in the same dictionary under namespaced keys (search.*, provenance.*). The manuscript never reads search_metadata.json, search_run.json, or screening logs directly; the stats module is the only consumer of those raw artefacts. If you discover a fact the manuscript needs that isn't in the dict, add it to build_stats() and re-run, not a new variable.

The dictionary is flat on purpose. Nested dicts silently return None on typos (s['screen']['xxx'] when xxx doesn't exist); flat dotted keys fail loudly (s['screen.xxx'] → KeyError). Loud failure at render time is the point — the same invariant that test_empirical_integrity.py catches statically (inline s['…'] keys resolve in build_stats()). Render-time and test-time enforcement of one rule.

Never write a literal number in prose that comes from the data (e.g., "the mean was −2.3%").

Starting point. ${CLAUDE_PLUGIN_ROOT:-.}/templates/manuscript_stats.py is a worked example of build_stats() for an SLR project — it ingests search_metadata.json, search_run.json, the screening logs, and coded_papers.csv and emits the flat dict above. ${CLAUDE_PLUGIN_ROOT:-.}/templates/manuscript.qmd shows the setup chunk and inline-expression patterns end to end.

Ownership and lifecycle

analysis/manuscript_stats.py is project-owned. The plugin ships a worked example under ${CLAUDE_PLUGIN_ROOT:-.}/templates/; you copy it into analysis/ as your starting point and extend build_stats() every time the manuscript needs a new methodology fact or derived number. The plugin has no way to regenerate it for you — it is not a shipped pipeline script you can invoke via ${CLAUDE_PLUGIN_ROOT:-.}/scripts/pipelines/..., it is your code.

Two invariants protect the dictionary's integrity as you extend it:

1. Only python3 analysis/manuscript_stats.py may write analysis/results/manuscript_stats.json. Never Edit or Write the JSON directly, and never hand-write any file under analysis/results/. If a key is missing from the dict, add the derivation to build_stats() and regenerate via the CLI. The project's .claude/settings.json carries a permission deny rule enforcing this at the tool level — see the Bootstrap section.
2. Every value in build_stats()'s return dict must trace to a pipeline artefact, not a typed literal. Acceptable sources: (a) reading a pipeline file (search_metadata.json, screening CSVs, coded_papers.csv, references.bib); (b) file-system metadata (os.path.getmtime, file size); (c) a subprocess call to a pipeline tool (git log, wc -l, a Zotero MCP query). Never a literal value typed inline. A hardcoded result["x.y"] = 42 in build_stats() is the exact same hallucination as a hand-typed number in prose — it's just been pushed one file upstream.

The test_empirical_integrity.py content-integrity test asserts that the on-disk JSON equals build_stats()'s live output, catching both staleness and tampering. The deny rule catches invariant (1) at the tool layer. Invariant (2) is self-policed — the agent must read what build_stats() contains before extending it and refuse to add a literal. The same ownership pattern applies to manuscript/manuscript_tables.py (pandas functions for code chunks) and manuscript/manuscript.qmd (the scaffold) — all three are per-project artefacts the template seeds and the researcher owns.

What is never acceptable

• A static Markdown table with typed-in data values.
• A literal number in prose not driven by an inline expression.
• A code chunk reading from raw data or intermediate files instead of analysis/results/.
• Rounding or adjusting a value from the script output.
• A hand-typed methodological fact (search date, database name, model name, keyword string) that exists as a pipeline-generated metadata entry. If the pipeline knows it, the manuscript must read it.
• Edit or Write on analysis/results/manuscript_stats.json (or any file under analysis/results/) from the agent's tools. The project's .claude/settings.json denies these — see the Bootstrap section. Only python3 analysis/manuscript_stats.py may produce these files.
• A hardcoded literal value inside build_stats() — e.g., result["x.y"] = 42. Every returned value must trace to a pipeline artefact, file metadata, or a subprocess call. Hardcoded literals in the producer are the same hallucination as hand-typed numbers in prose, just pushed one file upstream.

Before writing any results or discussion

1. Run every analysis script whose output feeds the chapter — typically analysis/manuscript_stats.py plus whichever table / model / figure producers are involved. The goal is that analysis/results/ is fully up to date for the claims you are about to write.
2. Confirm the output files exist in analysis/results/ before writing any code chunk or inline expression that reads from them.
3. After writing or editing a chapter with numbers, re-run the relevant analysis scripts and verify every figure in the prose still matches. test_empirical_integrity.py catches the subset that goes through the stats dictionary; hand-verify the rest.

Dynamic cross-references

Never hard-code table, figure, or section numbers — they break when content is reordered. Use the rendering system's native labelling mechanism:

Concept	Quarto	R Markdown
Assign table label	{#tbl-label} on the table	\\label{tab:label} inside kable()
Reference table	@tbl-label	\\ref{tab:label}
Assign figure label	{#fig-label} on the figure	fig.cap + \\ref
Assign section label	{#sec-label} on heading	not native

Jupyter does not have native cross-reference support; use a numbering convention defined in CLAUDE.md.

Test suite

Run the project's test suite before each milestone (chapter completion, submission, supervisor review). Check CLAUDE.md for the exact test command.

The plugin ships three fine-grained test templates under ${CLAUDE_PLUGIN_ROOT:-.}/templates/, each mapped to one skill. Copy into your project's scripts/ directory:

Template	Skill	Catches
test_empirical_integrity.py	this skill	Forbidden methodology literals; unique Quarto / Rmd labels; inline s['…'] key resolution against the live build_stats() dict; figure-file existence; manuscript_stats.json content matches build_stats()
test_citations.py	grounded-citations, fact-check	@citekey resolution in references.bib; no bare Author (YYYY) without @key; BBT-key uniqueness in coded_papers.csv
test_systematic_review.py	systematic-review	PRISMA arithmetic; search_run.json ↔ dedup CSV; decision-state whitelists; temperature=0 pinning; screening_config round-trip

All three depend on templates/test_common.py — copy it alongside.

Pure manuscript-editing projects (no SLR pipeline) copy only test_empirical_integrity.py, test_citations.py, and test_common.py. Full SR projects copy all four.

Rendering is a further integrity gate — a broken s['key'] or an unresolved @citekey crashes the renderer louder than any grep. See critic-loop's Rendering: what and why subsection. test_empirical_integrity.py --with-render invokes the renderer explicitly; critic-loop renders on every iteration regardless.

Any test failure is a blocking issue. Do not proceed until resolved.

Grow the suite with the project. When you discover a new hallucination or drift pattern this skill's rule would prevent — a new forbidden literal, a new inline-expression convention, an overlooked label or figure-path form — add a test to scripts/test_empirical_integrity.py before closing out the task. The failure becomes the sentinel so the same class of mistake can't silently return.

Qualitative tables

Tables that are purely descriptive (no counts or percentages) are exempt from the data-pipeline rule but must be marked:

<!-- qualitative table — not data-derived -->

Qualitative research with coded data

The pipeline still applies:

• Coded excerpts live in a single authoritative CSV (e.g., analysis/raw/coded_excerpts.csv).
• All counts and percentages derive from that CSV via the table generation script.
• Code chunks read the output CSVs from analysis/results/, not the raw coding CSV directly.

Red flags

• You are about to write a literal number in prose without an inline expression.
• A code chunk reads from raw data or intermediate files instead of analysis/results/.
• A static Markdown table contains data values.
• A number in the manuscript does not appear in the authoritative summary statistics file.
• A test reports a mismatch between a manuscript table and its authoritative CSV.
• You are about to Edit or Write analysis/results/manuscript_stats.json — never. Regenerate via python3 analysis/manuscript_stats.py. If a key is missing, extend build_stats() in analysis/manuscript_stats.py and re-run the CLI.
• You are about to add a literal value inside build_stats() — e.g. result["screen.n_included"] = 81. Never. Every value must trace to a pipeline artefact, file metadata, or a subprocess call. If the pipeline doesn't know the number, extend the pipeline first.