| 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
- 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.
- 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.
- Confidentiality and isolation. Personas review in isolation. They do NOT
see each other's reviews. Consolidation happens only after every persona
has filed.
- 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.
- 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.
- 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.
- 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.
- 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
- Read the paper. Confirm the PDF exists at the path the user supplied.
Note the page count for the persona prompts.
- 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.
- 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.
- 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.
- 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.
