replication-audit

name	replication-audit
description	Map claims in a literature to independent replications, robustness checks, failures, and unresolved evidence gaps. Use when assessing the empirical reliability of a body of findings rather than reproducing one project's code. For package rerunnability, use $replication-package.
allowed-tools	Read, Write, Edit, Glob, Grep, Bash(uv*), Bash(uv:*), Task, WebSearch, WebFetch, Bash(paperpile*)
argument-hint	[topic, .bib file, or paper directory]
skill-dependencies	["method-audit"]

Replication Audit

Examine which findings in a literature have been replicated, failed to replicate, or never been tested. Flag papers that build on non-replicated foundations.

Don't build your dissertation on a foundation made of sand.

Output Path

Per rules/review-artefact-routing.md (auto-loads in research projects (path-scoped to paper-*/ and paper/)):

• Source slug: replication-audit
• Write reports to: reviews/<scope>/replication-audit/<YYYY-MM-DD-HHMM>.md inside the project, where <scope> is the paper slug (e.g., paper-jtp) for paper-level reviews or _project for project-level reviews. Path is relative to the research project root, not the Task-Management repo.
• Never at project root (./CRITIC-REPORT.md-style filenames are forbidden — pre-rule layout).
• Idempotency: if the run timestamp already exists, append a same-run descriptor ({timestamp}-revision.md, {timestamp}-r2.md) — never overwrite the same <YYYY-MM-DD-HHMM> path.
• Index update: if reviews/INDEX.md exists, write a one-line entry under "Latest per source" pointing at the new file. Otherwise review-recap will rebuild the index next time it runs.
• Infrastructure repos (Task-Management, atlas-workspace, etc.): this section does not apply — the path-scoped rule won't load there.

When to Use

• Before committing to a theoretical framework — is the evidence solid?
• Writing a literature review — need to distinguish robust from fragile findings
• Preparing a replication study — need to know what's already been tested
• Evaluating a paper for peer review — are its cited foundations sound?

When NOT to Use

• Your own results — use the referee2-reviewer agent
• Methodological comparison — use method-audit
• Finding papers — use an installed scholarly-search workflow first

Input

A .bib file, PDF directory, topic, or list of key findings to audit. Works best with a focused set of influential papers whose findings underpin a line of research.

Workflow

Phase 1: Findings Inventory

From the corpus (assembled as in other corpus skills), extract the key empirical findings — not paper summaries, but specific claims with effect sizes where available:

Finding	Paper	Effect	N	Method
"X causes Y"	Author (Year)	d = 0.5	200	RCT
"A predicts B"	Author (Year)	r = 0.3	1,500	Survey

Focus on findings that other papers depend on — the claims that, if wrong, would undermine subsequent work.

Phase 2: Replication Search

For each key finding, search for replication attempts:

1. Forward citations — use scholarly scholarly-citations on the original paper's DOI
2. Keyword search — search for "replication" + key terms from the finding via scholarly scholarly-search
3. Replication databases — search for the paper on:
   • ReplicationWiki (via web search)
   • Many Labs projects
   • Replication registries in the relevant field
4. Author self-replication — check if the original authors replicated in a new sample

Dispatch rule. If ≥5 key findings need auditing, batch findings into groups of 4–5 per sub-agent. Each sub-agent runs steps 1–4 for its batch (scholarly-citations, scholarly-search, web searches) and writes replication evidence to /tmp/replication-audit-<n>.json. Main context merges and proceeds to Phase 3 classification. For <5 findings, sequential searches in main context are fine. See _shared/cli-dispatch-policy.md.

For each replication found, record:

• Result: Successful / Failed / Partial / Conceptual (similar but different design)
• Sample: How does it compare to the original (size, population)?
• Method: Exact replication or conceptual?
• Effect size: Same direction? Same magnitude?
• Published where? (Replication failures in top journals carry more weight)

Phase 3: Classification

Classify each finding:

Status	Criteria
Replicated	Successfully replicated by at least one independent team
Multiply replicated	Replicated 3+ times across different samples/contexts
Failed to replicate	At least one serious replication attempt found null or opposite results
Contested	Some replications succeed, others fail — mixed evidence
Never tested	No known replication attempts (most common and most concerning)
Unreplicable	Data/method too expensive, proprietary, or impractical to replicate

Phase 4: Dependency Mapping

Map which papers in the corpus depend on each finding:

Finding: "X causes Y" (Author, 2015) — STATUS: Failed to replicate
├── Paper A (2017) — builds entire model on this finding
├── Paper B (2019) — uses this as a control variable
└── Paper C (2021) — cites this as motivation but doesn't depend on it

Flag any paper whose core contribution depends on a non-replicated or failed finding.

Phase 5: Risk Assessment

For each "never tested" finding, estimate replication risk:

Risk factor	Increases concern
Small sample (N < 100)	High
P-value just below 0.05	High
Surprising/counterintuitive result	Medium
Complex interaction effects	Medium
Single study, no robustness checks	High
Author has other failed replications	Medium
Published in a journal with low replication standards	Medium

Phase 6: Output

Write to REPLICATION-AUDIT.md in the project directory.

Output Format

# Replication Audit: [Topic]

**Date:** YYYY-MM-DD
**Corpus:** [N] papers
**Key findings audited:** [N]
**Status breakdown:** Replicated: X | Failed: Y | Never tested: Z | Contested: W

## Summary

[2-3 sentences: overall replication health of this literature]

## Finding-by-Finding Audit

### 1. "[Finding statement]" — Author (Year)

**Original:** N = [X], Effect = [Y], Method = [Z]
**Status:** [Replicated / Failed / Never tested / Contested]

**Replication evidence:**
- [Author (Year)] — [Result] — N = [X], Effect = [Y]
- [Author (Year)] — [Result] — N = [X], Effect = [Y]

**Depends on this:** [Papers in corpus that build on this finding]
**Risk level:** [Low / Medium / High / Critical]

### 2. "[Finding statement]" — Author (Year)
...

## Replication Status Matrix

| Finding | Original (Year) | Replicated? | Times tested | Risk |
|---------|----------------|-------------|-------------|------|

## Dependency Risk Map

Papers building on shaky foundations:

| Paper | Depends on | Status of dependency | Risk to paper's claims |
|-------|-----------|---------------------|----------------------|

## Recommendations

### Safe foundations (build on these)
- [Finding] — multiply replicated, robust across contexts

### Proceed with caution
- [Finding] — replicated once, small samples

### Avoid or re-test
- [Finding] — failed to replicate or never tested despite high risk factors

### Replication opportunities
- [Finding] — never tested, high-impact if confirmed, feasible to replicate with [data/method]

Log to REVIEW-STATE.md (final step)

Write the replication audit to reviews/<scope>/replication-audit/<YYYY-MM-DD-HHMM>.md (mkdir -p reviews/<scope>/replication-audit/ first), where <scope> is the paper slug or _project. Then append a row to the project's REVIEW-STATE.md:

bash <skills-root>/_shared/review-state-log.sh \
  --check replication-audit \
  --paper "<paper-{venue} dir, or — for project-level audits>" \
  --verdict "<PASS|PARTIAL|FAIL>" \
  --score "<replicated-count>/<total-findings-checked>" \
  --open-issues "<failed-or-untested-count>/<total-findings-checked>" \
  --report "reviews/<scope>/replication-audit/<YYYY-MM-DD-HHMM>.md" \
  --notes "<one-line: e.g. '12/15 robust; 2 failed; 1 never tested'>" \
  [--trigger "pre-submission-report|review-cluster"]

• Verdict: PASS if every findings is robustly replicated; PARTIAL if some replicated, some not; FAIL if foundational findings failed to replicate.
• Score: robustly-replicated findings / total checked.
• Open issues: (failed + never-tested) / total at run time.
• Trigger: pass orchestrator name only if invoked as a sub-agent. Otherwise omit.

Schema: the installed shared resource shared/review-state-schema.md.

Cross-References

Skill	When to use instead/alongside
method-audit	For broader methodological comparison (not replication-specific)
weakness-scanner	For logical and argumentative weaknesses (not replication status)
Installed scholarly-search workflow	To find the replication studies identified in this audit
split-pdf	To deep-read any replication study found