# Peer Reviewer Sub-Agent Prompt Templates

After you have finished reading ALL splits and your notes are complete, **spawn three sub-agents in parallel using the Task tool**. Send all three Task tool calls in a single message.

## Standard Forbid-List for All Sub-Agents Below

**Paste this block into every sub-agent prompt below** (per `~/.claude/rules/subagent-prompt-discipline.md` § Standard Forbid-List for Write-Capable Sub-Agents). Sub-agents do not inherit global rules — defaults like "found a good source → write it to the bib" leak unauthorised actions unless the prompt negates them affirmatively.

```
## Scope of action — DO NOT do these things

This sub-agent has a narrow scope: produce the JSON / markdown findings
specified below and return them in your final response. Do NOT do any
of the following:

- Do NOT modify the paper under review (any file in the paper directory).
- Do NOT run `git add`, `git commit`, `git push`, or any other git
  write command. The orchestrator handles git.
- Do NOT edit `.context/`, `MEMORY.md`, `CLAUDE.md`, `README.md`, or
  any project-level documentation.
- Do NOT edit the project's `.bib` file. If you discover missing
  references, list them in your findings; the orchestrator decides.
- Do NOT create files outside the assigned output path (a temp file
  or your final response).

If you find yourself wanting to do any of these, stop and include
what you were about to do in your final summary. The orchestrator
decides.
```

The orchestrator pastes this block once into each of the three sub-agent prompts below — the templates do not repeat the block per call.

## Sub-Agent 1: Citation Validator

**Purpose:** Verify that every citation in the paper is real and that the claims attributed to cited papers are accurate.

**Prompt template for the Task tool:**

```
You are a Citation Validator sub-agent for a peer review. Your job is to verify
every citation in the paper under review.

CITATION REGISTRY (extracted from the paper):
[Paste the full citation registry from notes.md]

BIBLIOGRAPHY ENTRIES (from the paper's reference list):
[Paste the bibliography entries you extracted]

KA CONTEXT (if Knowledge Acquisition was run):
If the orchestrator provides file paths to `/tmp/ka-literature-*.json`, read that
file. Cross-reference the paper's reference list against the KA literature context.
Flag any foundational or SOTA papers that are cited in KA but missing from the
paper's references. Papers marked `in_paperpile: true` have been verified in
the user's library with full-text content — these are especially reliable.

For EACH citation, perform these checks:

1. EXISTENCE CHECK — use bibliography MCP tools first, then web search:
   a. Collect all DOIs from the bibliography entries and call `scholarly_verify_dois`
      to batch-verify them across OpenAlex + Scopus + WoS. Papers marked VERIFIED
      (2+ sources confirm) pass the existence check immediately.
   b. For SINGLE_SOURCE or NOT_FOUND DOIs, and for papers without DOIs, use
      `scholarly_search` with the paper title to search across all sources.
   c. Only fall back to WebSearch (Google Scholar, DBLP, publisher sites) for
      papers that MCP tools cannot find.

2. DETAIL MATCH: Do the author names, year, title, and venue match what the
   paper claims? For MCP-verified papers, use `openalex_lookup_doi` to get
   full metadata for comparison.

3. CLAIM VERIFICATION: Where possible, check whether the cited paper actually
   supports the claim being made. Flag misattributions.

PRIORITISATION:
- Focus most effort on papers from the last 3 years (higher hallucination risk)
- Papers you've never heard of
- Papers with suspiciously convenient findings
- Well-known classics (Kahneman & Tversky, etc.) need only a quick confirmation

CLASSIFICATION for each citation:
- ✅ Verified: Paper exists, claim appears consistent
- ⚠️ Exists, claim unverified: Paper exists but couldn't verify the specific claim
- 🟡 Suspicious: Paper may exist but details don't match
- 🔴 Not found: Cannot find evidence this paper exists
- ❌ Hallucinated: Confirmed non-existent

OUTPUT FORMAT:
Produce a structured report with:
1. Summary counts by category
2. A table of ALL citations with their status
3. A highlighted section for any 🔴 or ❌ citations (RED FLAGS)
4. Any misattributions discovered
5. Cross-reference issues (cited but not in bibliography, or in bibliography but never cited)
```

**Sub-agent type:** `general-purpose`

## Sub-Agent 2: Novelty & Literature Assessor

**Purpose:** Independently assess whether the paper's claimed contributions are genuinely novel by searching the existing literature for overlapping, pre-empting, or concurrent work.

**This is the most important sub-agent.** Novelty is the hardest thing to assess from within the paper itself — the authors will naturally present their work as new. This sub-agent acts as an independent literature investigator.

**Prompt template for the Task tool:**

```
You are a Novelty & Literature Assessor sub-agent for a peer review. Your job is
to independently assess whether this paper's contributions are genuinely novel.

PAPER'S CLAIMED CONTRIBUTIONS:
[Paste the exact claimed contributions from notes.md, with page references]

RESEARCH QUESTION:
[Paste the research question]

KEY METHODS USED:
[Paste the methodology summary]

FIELD/DOMAIN:
[Specify the field — e.g., "human-AI collaboration", "causal inference", etc.]

PAPERS THE AUTHORS CITE AS RELATED:
[List the key related work the authors themselves identify]

KA CONTEXT (if Knowledge Acquisition was run):
If the orchestrator provides file paths, read ALL of these before starting:
- `/tmp/ka-literature-*.json` — pre-built literature context with 20-30 papers
- `/tmp/ka-narrative-*.md` — domain narrative (arc of progress, open problems, positioning)
- `/tmp/ka-baselines-*.json` — missing baselines and datasets analysis

Papers marked `in_paperpile: true` have been verified in the user's library with
full-text content — these are especially reliable for novelty assessment. Use these
to ground your assessment in verified external evidence rather than parametric
knowledge alone. The KA context supplements but does not replace your own search —
it provides a head start, not a ceiling.

YOUR TASK:

1. PRIOR WORK SEARCH: For each claimed contribution, search the literature
   (using WebSearch, supplemented by KA literature context) to find:
   a. Papers that have already made the SAME contribution (pre-empting)
   b. Papers that have made a VERY SIMILAR contribution with different data/context
   c. Concurrent/simultaneous work making the same point
   d. Papers the authors SHOULD have cited but didn't

2. NOVELTY ASSESSMENT for each claimed contribution:
   - 🟢 NOVEL: No prior work found that pre-empts this
   - 🟡 INCREMENTAL: Prior work exists in a different context; this is an extension
   - 🟠 OVERLAP: Substantial overlap with existing work, unclear what is truly new
   - 🔴 PRE-EMPTED: An existing paper has already made this contribution

3. LITERATURE GAP ANALYSIS:
   - Are there important papers the authors should have cited?
   - Are there entire streams of literature the authors seem unaware of?
   - Is the paper correctly positioned in its field?

4. HONEST ASSESSMENT:
   - What is genuinely new here?
   - Is the contribution sufficient for the target venue?
   - If the contribution is incremental, is it a meaningful increment?

SEARCH STRATEGY:
- Start with bibliography MCP tools for structured cross-source search:
  a. Call `scholarly_search` with the paper's research question as query — this
     searches OpenAlex + Scopus + WoS with automatic deduplication
  b. Call `scholarly_similar_works` with the paper's title or abstract to find
     closely related work the keyword search might miss
  c. Call `scholarly_search` with the specific methodology + domain combination
- Then supplement with WebSearch for:
  - Working papers and preprints (SSRN, arXiv, NBER) not fully indexed in MCP sources
  - Very recent papers (last 3 months) that may not yet be in databases
  - Adjacent fields that might use different terminology
- Search for the exact research question with different author names
- Search for the claimed finding in survey/review papers
- Check top venues in the field for recent papers on this topic

OUTPUT FORMAT:
Produce a structured report with:
1. Overall novelty verdict (Novel / Incremental / Overlapping / Pre-empted)
2. Per-contribution novelty assessment with evidence
3. Key prior work found (with full citations and URLs)
4. Missing citations the authors should include
5. Literature streams the authors may have overlooked
6. Honest assessment of whether the contribution is sufficient
```

**Sub-agent type:** `general-purpose`

## Sub-Agent 3: Methodology Reviewer

**Purpose:** Deep assessment of the paper's methods — adapted to whatever methodological paradigm the paper uses.

**Prompt template for the Task tool:**

```
You are a Methodology Reviewer sub-agent for a peer review. Your job is to
assess the rigour and appropriateness of this paper's methods.

RESEARCH QUESTION:
[Paste from notes]

METHODOLOGY:
[Paste detailed methodology extraction from notes]

METHODOLOGICAL PARADIGM(S):
[Identify which paradigm(s) the paper uses — e.g., "experiment + survey",
 "causal inference (DiD)", "agent-based simulation", "ML/NLP", "MCDM",
 "qualitative case study", "theoretical/mathematical", etc.]

DATA / INPUT DESCRIPTION:
[Paste data details from notes — could be datasets, experimental stimuli,
 simulation parameters, interview transcripts, etc.]

ANALYTICAL SPECIFICATIONS:
[Paste any equations, estimators, model specifications, algorithms from notes]

KA CONTEXT (if Knowledge Acquisition was run):
If the orchestrator provides a file path to `/tmp/ka-baselines-*.json`, read it
before starting. It contains pre-identified missing baselines, missing datasets,
and baseline performance concerns. Use these as concrete technical evidence when
assessing methodological gaps — missing comparisons backed by external evidence
carry more weight than hunches.

YOUR TASK — adapt to the paper's paradigm(s):

1. METHOD APPROPRIATENESS:
   - Is the chosen method appropriate for the research question?
   - Are there better-suited alternatives the authors should have considered?
   - If multiple methods are combined, is the integration coherent?

2. IDENTIFICATION / VALIDITY (adapt to paradigm):
   For causal inference: source of variation, identifying assumptions, threats
   For experiments: randomisation, blinding, demand effects, ecological validity
   For simulations: parameter calibration, validation, sensitivity analysis
   For ML/NLP: train/test split, leakage, baselines, appropriate metrics
   For surveys: construct validity, sampling, common method bias
   For qualitative: sampling logic, saturation, reflexivity, triangulation
   For MCDM: criteria justification, weight sensitivity, rank reversal
   For theoretical: assumption necessity, proof correctness, generality

3. DATA / INPUT QUALITY:
   - Is the data/input appropriate for the question?
   - Are key constructs/variables well-measured or well-specified?
   - Sample size / parameter space adequate?
   - Selection bias / data limitations?

4. ROBUSTNESS AND SENSITIVITY:
   - Are robustness checks / sensitivity analyses adequate?
   - What additional checks would strengthen the paper?
   - Are the results fragile or robust?

5. ALTERNATIVE EXPLANATIONS:
   - What alternative stories could explain the same results?
   - What would falsify the authors' hypothesis?
   - What single additional analysis would most strengthen the paper?

6. MAGNITUDE / PLAUSIBILITY:
   - Are effect sizes / results reasonable given priors?
   - How do they compare to related work?

7. PARADIGM-SPECIFIC PITFALLS:
   Flag any known pitfalls for this paradigm:
   - Causal: TWFE bias, bad controls, weak instruments
   - Experiments: underpowered, multiple comparisons, demand effects
   - Simulations: overfitting parameters, insufficient runs, no validation
   - ML: data leakage, benchmark gaming, prompt sensitivity
   - Surveys: common method variance, unvalidated scales
   - MCDM: rank reversal, unjustified weights
   - Qualitative: insufficient rigour, over-generalisation

OUTPUT FORMAT:
Produce a structured assessment with:
1. Methodological paradigm(s) identified
2. Overall methodology rating (Strong / Adequate / Weak / Fundamentally Flawed)
3. Method appropriateness assessment
4. Identification / validity assessment (paradigm-specific)
5. Data / input quality assessment
6. Robustness gaps
7. Alternative explanations to consider
8. Paradigm-specific pitfalls found
9. Recommended additional analyses
```

**Sub-agent type:** `general-purpose`

## Launching Sub-Agents

**CRITICAL: Launch all three sub-agents in a SINGLE message using three parallel Task tool calls.** This is the whole point of the multi-agent architecture — they run concurrently.

```
# In a single message, make three Task tool calls:

Task 1: Citation Validator
- subagent_type: general-purpose
- description: "Validate paper citations"
- prompt: [filled citation validator template]

Task 2: Novelty & Literature Assessor
- subagent_type: general-purpose
- description: "Assess paper novelty"
- prompt: [filled novelty assessor template]

Task 3: Methodology Reviewer
- subagent_type: general-purpose
- description: "Review paper methodology"
- prompt: [filled methodology reviewer template]
```

## Collecting Sub-Agent Results

After all three sub-agents return, read their reports carefully. Look for:
- **Convergent findings** — issues flagged by multiple sub-agents are high-confidence
- **Contradictions** — if sub-agents disagree, investigate and use your own reading to arbitrate
- **New information** — the literature sub-agent may find prior work you didn't know about
