Seven-Pass Adversarial Review

Runs seven independent reviewers, each focused on a single lens, then synthesizes their findings into one prioritized revision plan — the fan-out → reduce → judge runtime from orchestrator-protocol.md, applied with seven lenses.

Why seven passes? A single-agent review blends lenses and softens each one. Seven forked agents each approach the paper with full context budget for their own lens, then a synthesizer resolves conflicts and de-duplicates.

When to pick this over /review-paper: This skill costs roughly 7× more tokens than /review-paper (default) and ~2× more than /review-paper --adversarial. Use it when the paper is submission-ready or at R&R stage and you need maximum lens coverage. For early drafts or iterative work, /review-paper is the right tool. For journal-simulation pressure test, use /review-paper --peer <journal> instead.

Inputs

• $0 — manuscript path (.tex, .qmd, .md, or .pdf). Required.

The Seven Lenses

Each lens runs as a forked subagent (context: fork) so the main conversation stays clean.

#	Lens	Focus	Agent type
1	Abstract audit	Does the abstract state the question, method, result, and contribution? Does it match the paper?	general-purpose
2	Intro structure	Does the intro follow Cochrane / Varian framework? Literature placement? Contribution clarity?	general-purpose
3	Methods / identification	Are assumptions stated? Is identification credible? Are alternatives addressed?	domain-reviewer
4	Results + tables	Do tables read standalone? Is magnitude + significance discussed? Units consistent?	general-purpose
5	Robustness	Are obvious threats pre-empted? Is the robustness section convincing or theatrical?	general-purpose
6	Prose quality	Sentence-level clarity, hedging, passive voice, paragraph cohesion	proofreader
7	Citation audit	Invokes /validate-bib --semantic; checks cite-claim direction for top-10 works	general-purpose

Workflow

Phase 0: Pre-flight

1. Resolve manuscript path.
2. Decide if .pdf → extract text first (pdftotext -layout).
3. Create output dir: quality_reports/seven_pass_[stem]/.

Phase 1: Spawn 7 reviewers in parallel

In a single message, spawn 7 Agent tool calls (one per lens). Each subagent gets:

• The manuscript path (to re-read with its own context).
• The lens-specific prompt (below).
• Instructions to write to quality_reports/seven_pass_[stem]/lens_[N]_[lens-name].md.
• A JSON findings array conforming to finding-schema.json, written to quality_reports/seven_pass_[stem]/lens_[N]_[lens-name].json beside the prose report. Severities: blocker | major | minor | nit. Every finding computes its id with python3 scripts/validate-findings.py --id FILE LINE LOCUS and carries rule, evidence, and a failing_case. Phase 2 validates each array first (python3 scripts/validate-findings.py <file> — exit 0 required; a lens whose report does not validate has not reviewed), then reduces over the typed findings — it does not re-read the prose. Because ids are lens-independent, the same defect found by two lenses dedups to one finding automatically.

This is the fan-out primitive from orchestrator-protocol.md; Agent subagents are the portable mechanism (the agents that fill lenses 3/6 are in agent-fleet.md).

Lens prompt rubrics are embedded inline below — one summary paragraph per lens. Each forked subagent receives its lens's rubric plus the manuscript path.

Lens prompt summaries:

• Lens 1 (Abstract): Does the first sentence state the question? Does it name the method? Quantify the headline result? State one-sentence contribution? Cross-check: do these four things match the body?
• Lens 2 (Intro): Does the intro open with the question? Hook → context → contribution → roadmap? Lit review placed correctly (after the hook, not before)? Contribution-counted (1, 2, 3…)? Preview of findings with magnitudes?
• Lens 3 (Methods): Is every assumption stated? Are they strong or weak? Is identification one-liner clear? Are known violations (selection, measurement, reverse causality, SUTVA) addressed? Are instruments / RDD / DiD assumptions explicit and defensible?
• Lens 4 (Results): Does each table read standalone (caption, units, SEs clarified)? Is magnitude interpreted (not just significance)? Are units consistent across tables? Are figures legible at 8pt?
• Lens 5 (Robustness): Does the paper ANTICIPATE a sharp referee's objections? Are robustness checks motivated, or just listed? Power/placebo tests present? Heterogeneity explored where promised?
• Lens 6 (Prose): Sentences under 30 words? Active voice dominant? Hedging proportionate (neither overclaiming nor endless "may suggest")? Paragraph topic sentences?
• Lens 7 (Citations): Invoke /validate-bib --semantic. For top-10 cited works, does the in-text claim match the cited paper's actual finding direction? Are contemporary / competing works cited?

Phase 2: Synthesize (reduce → judge, with the hallucination gate)

Wait for all 7 lens reports. Reduce, don't re-review: stack the seven scorecards and apply the gate predicate from orchestration-schemas.md §3 — the Executive verdict is a function of the typed findings, not a fresh eighth opinion. Then run the post-judge hallucination gate (§4): any CRITICAL the synthesis introduces that no lens raised must be re-verified in a fresh claim-verifier fork, or dropped to [JUDGE-HALLUCINATED] and the verdict recomputed. A synthesis may freely downgrade or de-duplicate lens findings; it may not invent a new blocker.

Then produce:

quality_reports/seven_pass_[stem]/_SYNTHESIS.md

# Seven-Pass Review: [Manuscript]

**Date:** YYYY-MM-DD
**Path:** [manuscript]

## Executive verdict

**Overall state:** [SUBMIT / REVISE-MINOR / REVISE-MAJOR / REJECT-AND-RESTART]

## Cross-lens CRITICAL issues
| # | Lens(es) | Issue | Recommendation |
|---|---|---|---|

## MAJOR issues (second-round)
| # | Lens(es) | Issue |
|---|---|---|

## MINOR polish
[bulleted]

## Per-lens scorecard
| Lens | Critical | Major | Minor | Score/10 |
|---|---|---|---|---|
| 1. Abstract | | | | |
| 2. Intro | | | | |
| 3. Methods | | | | |
| 4. Results | | | | |
| 5. Robustness | | | | |
| 6. Prose | | | | |
| 7. Citations | | | | |
| **Overall** | | | | |

## Revision plan (in recommended order)
1. [Highest-leverage fix — usually a lens with 2+ CRITICALs]
2. …
7. [Lowest-leverage polish]

## Contradictions between lenses
[If two lenses disagree, surface here. E.g., Lens 2 says "expand contribution" but Lens 6 says "trim intro".]

Phase 3: Token-budget report

After synthesis, print:

Seven-pass review complete.
Subagents: 7 (parallel) + 1 synthesizer.
Approx token usage: ~80–120k (vs ~15k for single-pass /review-paper).
Runtime: ~3–5 min wall-clock.
For cheaper alternatives:
  - Single-pass: /review-paper
  - Iterative: /review-paper --adversarial

When to use this skill

• Before first submission to a top journal.
• After a major revision when you want to catch drift.
• R&R when referees disagree — surfaces contradictions your revision must navigate.

When NOT to use

• Early drafts (use /review-paper single-pass first).
• Short notes, comments, or replies (overkill).
• When you've already run this in the last 7 days and nothing substantive changed.

Findings are validated, not just written (v2.5)

This skill's reviewers emit findings under the machine-checked contract in finding-schema.json. Reports are JSON arrays.

Smoke-test the harness before spending review effort — a run that fans out reviewers and then cannot write a valid report has wasted the whole pass:

echo '[]' | python3 scripts/validate-findings.py

Then, before presenting any summary:

python3 scripts/validate-findings.py <report>.json   # exit 0 required

What the contract forces, and why:

• rule — the documented rule or standard violated. A finding citing no rule is an opinion, and opinions do not gate a commit.
• failing_case — a concrete configuration under which the claim breaks, or the exact missing hypothesis. "This could be clearer" does not validate.
• id = sha1("<file>:<line>:<locus>") — deterministic, so dedup across rounds is exact and the two-strikes rule is checkable rather than eyeballed.
• mechanical — true only for fixes that cannot change a result (typo, cross-reference, formatting, label). Never for an estimand, assumption, specification, inference procedure, sample definition, or reporting language: those return to the researcher.

Apply the per-lens evidence burdens and the "does NOT count" filters in orchestration-schemas.md §7 before verification, so known false alarms never reach the judge. The verifier pass is refute-biased: only verdict: "confirmed" findings ship; anything it cannot ground is dropped, not downgraded to a warning.

Cross-references

• .claude/skills/review-paper/SKILL.md — the single-pass and --adversarial modes (cheaper, faster).
• .claude/skills/validate-bib/SKILL.md — invoked by Lens 7.
• .claude/skills/audit-reproducibility/SKILL.md — complementary; numeric-claims side of the audit.
• Workflow guide, Pattern 15 — the narrative explanation of why seven lenses.

Exit behavior

• Exits 0 always (review is informational). The synthesis report's "Executive verdict" is the gate.
• Any CRITICAL at the top of the synthesis should block submission until resolved.

What this skill does NOT do

• Re-run seven lenses if the manuscript hasn't changed — check git diff against last run date in _SYNTHESIS.md, skip unchanged lenses if requested via --incremental (future).
• Auto-apply fixes — that's /review-paper --adversarial's job.
• Replace human judgment. A reviewer who knows your subfield still beats seven LLMs.