peer-review-engine — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited peer-review-engine (Agent Skill) and scored it 96/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 1 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Orchestration Log: When this skill is activated, append a log entry tooutputs/orchestration_log.md: ``### Skill Activation: Peer Review Engine **Timestamp:** [current date/time] **Actor:** AI Agent (peer-review-engine) **Input:** [paper source: draft.md / paper.tex, word count, number of sections] **Output:** 2 simulated reviewer reports saved to simulated_reviews.md **Recommendation distribution:** [R1: recommendation, R2: recommendation]``
The best time to discover weaknesses is before reviewers do. This engine generates two independent, simulated double-blind peer reviews that mimic the rigor, tone, and structure of top-tier IS/CS conference reviews (ICIS, ECIS, HICSS) and journal reviews (MISQ, ISR, EJIS, BISE). Each reviewer has a distinct persona and evaluation focus, producing complementary perspectives on the manuscript.
The reviews are actionable, not performative. Every weakness includes a concrete suggestion for improvement. Every strength is specific enough to preserve during revision. The output format is designed to feed directly into /respond-reviewers, creating a pre-submission quality loop: write -> self-review -> revise -> submit.
/review-paperdraft.md or latex/paper.tex exists (at least one)Check for paper sources in this order of preference:
If both exist, compare timestamps. Use the more recent one, but note which version was reviewed.
Read the full paper and extract:
\title{} or first # heading)\begin{abstract} or the abstract section)\citep/\citet or (Author, Year) patternsIf the paper mentions a target venue (in metadata, framing.md, or paper_structure.md), use that venue's specific review criteria. Otherwise, default to ICIS/ECIS-level expectations for IS papers, or top-tier CS conference standards for CS papers.
Before writing individual reviews, perform a systematic assessment across all evaluation dimensions. This ensures both reviewers draw from a consistent quality analysis while emphasizing different aspects.
| Dimension | Weight | What to Check |
|---|---|---|
| 1. Contribution | High | Is the contribution clearly stated? Is it novel? Does it advance theory or practice? Is the gap well-motivated? |
| 2. Theoretical Foundation | High | Is the theory well-chosen? Is it properly applied (not just cited)? Are constructs operationalized? |
| 3. Research Design & Rigor | High | Is the method appropriate for the RQs? Are threats to validity discussed? Is the approach replicable? |
| 4. Literature Coverage | Medium | Is the related work comprehensive? Are key papers cited? Is the positioning accurate? |
| 5. Argumentation & Logic | Medium | Does the paper flow logically? Are claims supported by evidence? Are there logical gaps? |
| 6. Writing Quality | Medium | Is the paper clearly written? Is it within page limits? Are figures/tables well-designed? |
| 7. Practical Relevance | Low-Med | Are practical implications specific and actionable? Would practitioners find this useful? |
Positive signals:
Red flags:
Assign an internal rating:
These ratings are for your internal use to calibrate the reviews. They do NOT appear in the reviewer output (real reviewers don't use rubrics this explicitly).
Profile: Senior IS/CS researcher, 15+ years experience. Associate Editor at a top journal. Known for methodological rigor. Has published extensively on research methods in IS. Reviews 15-20 papers per year for ICIS, ECIS, MISQ.
Evaluation focus:
Tone: Constructive but demanding. Points out methodological gaps precisely. Acknowledges methodological strengths explicitly. Uses phrases like "The authors should clarify...", "It is unclear how...", "A stronger approach would be to..."
Bias tendencies (realistic):
Generate the review in this exact format:
## Reviewer 1
### Summary
[3-5 sentences summarizing the paper's objective, approach, and main findings.
Write as if the reviewer genuinely read the paper. Reference specific sections.]
### Strengths
[3-5 bullet points. Each strength must be SPECIFIC — reference a particular
section, argument, or methodological choice. Not generic praise.]
- **S1:** [Specific strength with section reference]
- **S2:** [Specific strength with section reference]
- **S3:** [Specific strength with section reference]
### Weaknesses
[3-5 bullet points. Each weakness must be ACTIONABLE — include what should be
done to address it. Focus on methodological issues.]
- **W1:** [Specific weakness + concrete suggestion for improvement]
- **W2:** [Specific weakness + concrete suggestion for improvement]
- **W3:** [Specific weakness + concrete suggestion for improvement]
### Minor Comments
[3-7 specific, localized comments with section/paragraph references.
These are "nice to have" improvements, not structural issues.]
- [Section X.Y]: [specific comment]
- [Section X.Y]: [specific comment]
### Questions for Authors
[1-3 questions that the reviewer genuinely wants answered. These should probe
methodology choices or unclear aspects of the research design.]
1. [Question about method/design]
2. [Question about validity/generalizability]
### Overall Assessment
**Recommendation:** [Strong Accept / Accept / Minor Revision / Major Revision / Reject]
**Confidence:** [High / Medium / Low] — [1-sentence justification for confidence level]
[2-3 sentence summary of overall assessment. Be direct.]Profile: Mid-career IS/CS researcher, Associate Professor. Active in theory development and conceptual contributions. Publishes on theoretical frameworks and their application in IS research. Reviews for ICIS, ECIS, EJIS. Has strong opinions about contribution clarity and theoretical grounding.
Evaluation focus:
Tone: Intellectually engaged. Challenges the paper's positioning and theoretical choices. Asks probing questions about "why this theory?" and "what does this add?" Uses phrases like "The contribution would be strengthened by...", "I am not convinced that...", "The authors need to more clearly articulate..."
Bias tendencies (realistic):
Generate the review in the same format as Reviewer 1, but with different focus:
## Reviewer 2
### Summary
[3-5 sentences. Focus on the paper's theoretical positioning and contribution
claim. Note how the paper fits into the broader discourse.]
### Strengths
[3-5 bullet points focusing on contribution, theory, and positioning.]
- **S1:** [Specific strength — contribution/novelty]
- **S2:** [Specific strength — theoretical grounding]
- **S3:** [Specific strength — literature positioning]
### Weaknesses
[3-5 bullet points. Focus on theoretical/conceptual issues.]
- **W1:** [Specific weakness + concrete suggestion for improvement]
- **W2:** [Specific weakness + concrete suggestion for improvement]
- **W3:** [Specific weakness + concrete suggestion for improvement]
### Minor Comments
[3-7 specific, localized comments.]
- [Section X.Y]: [specific comment]
- [Section X.Y]: [specific comment]
### Questions for Authors
[1-3 questions probing contribution and theoretical choices.]
1. [Question about contribution/positioning]
2. [Question about theory application]
### Overall Assessment
**Recommendation:** [Strong Accept / Accept / Minor Revision / Major Revision / Reject]
**Confidence:** [High / Medium / Low] — [justification]
[2-3 sentence overall assessment.]The two reviewers MUST arrive at their recommendations INDEPENDENTLY. Do not harmonize them. In real conferences, reviewers disagree ~40% of the time. This disagreement is informative — it reveals where the paper is strong (agreement on strengths) and where it's controversial (disagreement on weaknesses).
For a typical AI-generated first draft from this pipeline, expect:
| Paper Quality | R1 (Methodologist) | R2 (Theorist) | Typical Scenario |
|---|---|---|---|
| Strong draft, clear method | Minor Revision | Minor Revision | Well-executed SLR or DSR |
| Good draft, theory gaps | Minor Revision | Major Revision | Strong method, weak positioning |
| Early draft, structural issues | Major Revision | Major Revision | Needs work across the board |
| Conceptual paper, no empirics | Reject | Minor Revision | R1 wants data, R2 values the idea |
| Data-rich, theory-thin | Accept | Major Revision | R1 satisfied, R2 wants more theory |
| Recommendation | Criteria |
|---|---|
| Strong Accept | Exceptional. Top 10% of submissions. Clear contribution, rigorous method, excellent writing. Rare for AI-generated drafts. |
| Accept | Solid work. Minor issues only. Ready for publication with small tweaks. |
| Minor Revision | Good foundation but needs targeted improvements. 1-2 revision rounds expected. Typical for strong first drafts. |
| Major Revision | Significant issues that require substantial rework. Structure, method, or theory needs fundamental revision. |
| Reject | Fundamental problems. Contribution unclear, method inappropriate, or paper not suitable for venue. Only assign if genuinely warranted. |
If the paper contains placeholders:
simulated_reviews.mdSave the complete output to simulated_reviews.md with this structure:
# Simulated Peer Review
**Paper:** [title]
**Source reviewed:** [draft.md / paper.tex]
**Date:** [ISO 8601]
**Simulated venue level:** [ICIS / ECIS / MISQ / ...]
**Word count:** ~[N] words
**References:** [N]
**Sections:** [N]
> **Note:** These reviews are AI-generated simulations designed to identify
> potential weaknesses before formal submission. They follow the structure and
> tone of real peer reviews at top IS/CS venues. Use `/respond-reviewers` to
> process this feedback systematically.
---
## Reviewer 1
[Full Reviewer 1 report — see Step 3]
---
## Reviewer 2
[Full Reviewer 2 report — see Step 4]
---
## Meta-Reviewer Summary
### Consensus Strengths
[2-3 points where both reviewers agree the paper is strong]
### Consensus Weaknesses
[2-3 points where both reviewers identify the same issue]
### Divergent Views
[Points where the reviewers disagree — these reveal the paper's most
debatable aspects and should receive special attention during revision]
### Priority Revision Checklist
[Ordered list of the most impactful changes, synthesized from both reviews.
Start with items that would address multiple reviewer concerns simultaneously.]
1. [ ] [Highest priority item — addresses W from both reviewers]
2. [ ] [Second priority — addresses major W from one reviewer]
3. [ ] [Third priority — ...]
4. [ ] [...]
### Recommended Next Steps
- Run `/respond-reviewers` with `simulated_reviews.md` to systematically address feedback
- Focus on items classified as MAJOR or CRITICAL by either reviewer
- Address consensus weaknesses first (both reviewers flagged them)Save to the project working directory: simulated_reviews.md
If outputs/ directory exists, also save a copy as outputs/simulated_reviews.md.
IS conferences emphasize:
Review length: 500-1000 words per reviewer.
Journal reviews are more detailed:
Review length: 800-1500 words per reviewer.
CS conferences have different norms:
Adapt review structure and emphasis based on detected venue type.
appears to be an early-stage draft. Reduce minor comments. Focus on structural and conceptual feedback.
Review the completed sections. Note which placeholders are most critical to fill.
formulating explicit research questions.
should recommend a specific method based on the RQs.
conventions (Gutachten-Stil). Evaluation dimensions remain the same.
| Scenario | Integration |
|---|---|
| After review → implement changes | Output feeds into review-engine via /respond-reviewers |
| Reviewer flags citation concerns | Recommend running /verify-citations (verification-engine) |
| Reviewer requests additional literature | Recommend running /search-papers (literature-engine) |
| Reviewer requests better figures | Recommend running /generate-figure (figure-engine) |
| Reviewer flags writing quality issues | writing-engine templates for specific sections |
The intended workflow is:
/write-paper (Phase 1-6)
↓
/review-paper (this engine)
↓
simulated_reviews.md
↓
/respond-reviewers simulated_reviews.md (review-engine)
↓
Revised paper.tex + paper.pdf
↓
(optional) /review-paper again → iterate until satisfied
↓
Submit to venueThis creates a pre-submission quality loop that catches most common reviewer objections before the paper reaches actual reviewers.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.