iacr-review

name	iacr-review
description	Deterministic harsh review of cryptography papers under the EUROCRYPT 2022 review rubric. Dispatches ten expert-persona reviewers in parallel (game-based crypto, UC, concrete security, BFT consensus, P2P networks, TEE hardware, formal methods, applied crypto, post-quantum, economics/MEV), each filing a full A-I review form, then consolidates findings into a single ledger sorted by severity. Use when user says "review this paper to IACR standards", "Eurocrypt review of <paper>", "harsh review the EpochPoET paper", "what would a PC think of this", "run the IACR panel on <paper.pdf>". Do NOT use for prose-only style review (use book-review), chapter drafts (use book-compose), or non-cryptography papers (use review-conductor with a different panel).
license	MIT
metadata	{"author":"charles-hoskinson","version":"0.1.0","category":"review","workspace-aware":false}

iacr-review

Persona-based review of cryptography papers under the EUROCRYPT 2022 review form (the de facto IACR top-tier rubric). The skill dispatches ten expert subagents in parallel, each filing a complete sections A through I review of the same paper. The consolidator merges them into a single ledger with severity-sorted findings and cross-persona convergence detection.

When to use

• "Review this paper to IACR / Crypto / Eurocrypt standards."
• "What would a Eurocrypt PC say about this?"
• "Harsh-review <paper>.pdf with the full IACR panel."
• "Run the ten-persona review on the EpochPoET draft."
• Pre-submission self-review: drive the personas against a draft before sending it to a real PC.

Do not use this skill for:

• Prose-only editorial review of book chapters (use book-review).
• Chapter drafting (use book-compose).
• Non-cryptography papers (use review-conductor with a different persona panel).
• Mechanical-defect gating on built artifacts (use book-qa).

Operating doctrine

1. Rubric is the EC22 form. The single source of truth for every persona is personas/ec22-rubric.md. Personas fill sections A through I. They do not invent new sections. They do not omit sections.
2. Harshness calibration. IACR top-tier acceptance rates hover near 22 percent. The default disposition is weak-reject. A persona moves to accept only when the paper clearly improves on the state of the art. A persona moves to strong-accept only on breakthrough quality. Borderline papers are rejected, not passed.
3. Confidentiality and isolation. Personas review in isolation. They do NOT see each other's reviews. Consolidation happens only after every persona has filed.
4. Anonymity. Treat the paper as anonymous regardless of what the writing style suggests. Do not speculate on authorship or affiliation, even in the I-section (PC-only) notes.
5. No fabrication. Personas may not invent citations, affiliations, or prior work. If a persona believes overlapping work exists but cannot name it, the finding is "the paper does not survey adjacent literature" rather than a fabricated citation.
6. Q3 authority. Section C / Q3 (technical flaws) is the reviewer's strongest authority. Use it when the paper is wrong. Do not be diplomatic about correctness defects.
7. Cite or do not claim. Every finding in C, D, and E cites a section, line, theorem, lemma, or page from the paper. A finding without a citation does not survive consolidation.
8. F is conditional. Score sections F (scientific quality) only if C through E have no unresolved defects. Otherwise mark F "not applicable until C through E are addressed."

Workflow

1. Read the paper. Confirm the PDF exists at the path the user supplied. Note the page count for the persona prompts.
2. Prepare the output directory. Default is paper/reviews/ relative to the paper's workspace; override with an explicit --output-dir flag if the user requests one.
3. Dispatch ten persona subagents in parallel. For each persona personas/0N-<slug>.md, send the verbatim "Persona prompt" section as a subagent prompt via the Agent tool, substituting ${PAPER_PATH}, ${PAPER_PAGES}, ${PERSONA_INDEX}, and ${OUTPUT_DIR}. Each subagent writes a completed review form to ${OUTPUT_DIR}/persona-${PERSONA_INDEX}-<slug>.md and returns its recommendation plus top three findings.
4. Consolidate. After all ten personas have filed, run python -m scripts.consolidate --reviews-dir <output-dir> --paper <name> --version <vNNN> --output <output-dir>/<vNNN>-review-ledger.md. The consolidator parses each persona file, severity-tags every finding, sorts, and detects cross-persona convergence on shared section/line citations.
5. Surface the ledger. Return the ledger path, the recommendation distribution, and the median + confidence-weighted mean to the user.

Persona dispatch

The ten personas, with their distinct concerns:

N	Slug	Reading lens (one line)
01	game-based-crypto	Game-hop accounting, advantage bounds, simulator query types
02	uc-crypto	Ideal-functionality specification, simulator constructiveness, composition hybrids
03	concrete-security	Tightness, parameter tables, bit-security accounting
04	bft-consensus	Safety vs liveness separation, network model, finality definition
05	p2p-network	Eclipse / Erebus resistance, AS-bucketing, gossip-protocol DoS
06	tee-hardware	TCB declaration, side channels, attestation flow, rollback resistance
07	formal-methods	Mechanization completeness, realisability, spec-to-prose mapping
08	applied-crypto	Ciphersuite identifiers, hash-to-curve domain separation, KES erasure
09	complexity-pq	Quantum adversary class, QROM gaps, NIST-aligned parameter selection
10	economics-mev	Equilibrium concept, slashing-vs-payoff sizing, bribery resistance

Each persona file ends with a "Persona prompt (verbatim to send to subagent)" section. That string is the literal subagent prompt; placeholders are filled per dispatch.

Severity rubric

The consolidator assigns severity by inspecting each persona's recommendation and the section the finding appeared in:

• strong-reject — persona recommended 1 (strong reject); the finding is fundamental.
• weak-reject — persona recommended 2 (weak reject); fixable on clarifying resubmission.
• borderline — section C or D finding by a persona that did not reject; substantive concern but not alone sufficient to reject.
• nit — section E finding by a persona that did not reject; editorial polish.

Severity-sort order is strong-reject > weak-reject > borderline > nit. Within a severity tier, sort by persona index then finding ID.

Ledger format

Output: templates/ledger.md shape. One row per finding:

| ID | Persona | Severity | Category | Location | Claim | Evidence | Suggested fix |

The ledger also contains:

• Recommendation distribution across the ten personas.
• Median and confidence-weighted mean recommendation.
• Cross-persona convergence: which paper sections drew findings from two or more personas (this is where the panel's signal is strongest).
• A deferred-fixes section for findings the consolidator marks as out of scope for the next revision.

What this skill owns

• The EC22 rubric encoding in personas/ec22-rubric.md.
• The ten expert personas and their verbatim subagent prompts.
• The review form and ledger templates.
• The consolidation script and its severity-sort discipline.

What this skill does NOT own

• Spawning the subagents themselves. That is Claude's responsibility at the parent layer; this skill provides the prompts.
• Mechanical defect-gating on artifacts (owned by book-qa).
• Prose-quality review (owned by book-review / russellian-style).
• Multi-skill orchestration where the IACR panel is one component of a larger pipeline (owned by review-conductor if extended).

Composes with

• review-conductor — can wrap this skill as one panel option.
• book-knowledge — can supply the paper's claim ledger so personas can cross-check references.
• russellian-style — usually runs BEFORE this skill, on the paper's prose, so that editorial defects do not consume the personas' attention budget.

Harshness reminder

The user's intent in invoking this skill is to receive a deterministic harsh review, not a polite one. If every persona returns accept, suspect either a genuinely breakthrough paper or a calibration failure; verify against the EC22 rubric before reporting. If every persona returns strong-reject, that is the expected modal outcome for a draft that has not yet been through one round of peer review.