Replication Audit
SkillDev toolsMap 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Replication Audit skill
What this skill tells your AI
The instructions your AI receives, as published by flonat/flonat-research in skills/replication-audit/SKILL.md and read by ahel’s review.
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>.mdinside the project, where<scope>is the paper slug (e.g.,paper-jtp) for paper-level reviews or_projectfor 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.mdexists, write a one-line entry under "Latest per source" pointing at the new file. Otherwisereview-recapwill 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-revieweragent - 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:
- Forward citations — use
scholarly scholarly-citationson the original paper's DOI - Keyword search — search for "replication" + key terms from the finding via
scholarly scholarly-search - Replication databases — search for the paper on:
- ReplicationWiki (via web search)
- Many Labs projects
- Replication registries in the relevant field
- 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 |
Signals
- GitHub stars
- 134
- Forks
- 24
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
replication-audit- Source
- github.com/flonat/flonat-research