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mock-review

Use before submission (about 3 days and again 1 day before) or before a rebuttal to simulate venue-calibrated reviews.

Skill by Noi1r

IndexedMIT★ 3
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/noi1r-mock-review

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About

Use before submission (about 3 days and again 1 day before) or before a rebuttal to simulate venue-calibrated reviews. Runs falsify-first reviewer personas (expert-skeptic, generalist, implementer, plus optional data-auditor and desk-reject auditor), verifies their findings, and outputs scored reviews plus an executable fix list.

Use cases: data

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Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.

Tools
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/mock-review

What's inside

Skill name
mock-review
Description
Use before submission (about 3 days and again 1 day before) or before a rebuttal to simulate venue-calibrated reviews. Runs falsify-first reviewer personas (expert-skeptic, generalist, implementer, plus optional data-auditor and desk-reject auditor), verifies their findings, and outputs scored reviews plus an executable fix list.
License
MIT
Instructions
99 lines in SKILL.md · view on GitHub

Before installing

  • Instructions call the network

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Mock review

## Principles

1. **Falsify first.** Prepare a list of falsifiable statements
   (from `CLAIMS.md` and the contribution list: "the packing bound k ≤ N/(2tw) is
   tight", "the distinguisher holds for every key", "hypothesis H is necessary"). Reviewers are told to break them, not to praise.
2. **Personas are views of one analysis, not independent experts.** Say so in the
   output. Do not present simulated scores as a prediction of the real outcome.
3. **Only findings that can be located in the source, data or a script are accepted.**
   Reviewer agents produce false positives; the coordinating agent verifies each
   finding (reproduce the counterexample, open the log, recompute the cell).
4. **Read-only.** Mock reviewers never edit the paper; they write `REVIEWS.md`.
5. **Evidence-based and constructive.** Each weakness cites a page/section/equation
   and proposes a fix.

## Personas

| persona | stance | checks |
|---|---|---|
| **Expert-skeptic** (theory) | senior expert in the exact subfield, assumes the result is wrong | counterexamples on small parameters; hypotheses vs the paper's own parameter table; sufficient-vs-necessary confusions; body vs appendix formula mismatches; proof gaps; overclaimed proof status (heuristic stated as theorem) |
| **Generalist** (novelty/writing) | good cryptographer outside the subfield | can I restate the idea after the overview? is the advance over the closest work explicit and supported? are the main statements and assumptions understandable, with clear pointers to detailed support? |
| **Implementer** (experiments) | builds the same thing in practice | baseline strength/config; same machine/session; scope of ranges; excluded instances; overheads disclosed; ablation present; artifact runs; security estimates per instance |
| Data auditor (optional) | accountant | recomputes every ratio, sum and derived parameter in every table from EVIDENCE rows and logs |
| Desk-reject auditor (optional) | PC chair | page limit, template, fonts, page numbers, anonymity (run `anonymize-check`), links resolve, references complete |
| Links/anonymity (optional) | | curl every URL; grep identity strings in PDF text and binary; artifact README wording |

Pick 3 (default) to 5 personas; weights and venue anchors from
[venue-calibration](../venue-calibration/SKILL.md).

## Procedure

1. **Brief** (`reviews/REVIEW-BRIEF.md`): venue, paper path + hash, falsifiable
   statement list, persona assignments, what is out of scope (e.g. "we do not rerun the
   full benchmark; historical logs are not presented as fresh reproduction").
2. **Review** from the selected viewpoints within the current Codex task, using
   the brief to try to refute the claims first. Use actual subagents only when
   the author requests them and delegation is available; choose their models
   and reasoning effort according to the task and applicable project guidance.
3. **Verify** each finding: reproduce counterexamples with a script, recompute numbers,
   open cited logs. Mark each finding `confirmed | false-positive | unverifiable`.
4. **Score** with the six-point scale + confidence 1–4 per persona, plus dimension
   scores (problem value, novelty, technical soundness, experimental persuasiveness,
   precision of statements, reproducibility). Dimension scores are not averaged into
   an acceptance probability.
5. **Fix list** (`reviews/SUBMIT-FIXES.md`): items graded **must-fix** (compliance,
   integrity, confirmed errors) / **should-fix** / **won't-fix (with reason)**, each
   with file:line, old text, new text, and why. Include "good news" items (what was
   checked and found correct) so authors do not second-guess sound parts.
6. **Rebuttal prep**: list the 3–6 questions most likely to be asked and the evidence
   that answers each (feeds the `rebuttal` skill).
7. Human author decides which fixes to apply; after fixes, rerun the implementer
   (data) and desk-reject personas.

## Output format (`REVIEWS.md`)

```
## Round <k> — <date> — venue profile: <venue> — paper hash <sha256[:12]>
| persona | score | confidence | top-3 weaknesses | action item → agent |
|---|---|---|---|---|
Scope and limits of this review: <what was read, what was not rerun>
Personas are views of one analysis, not independent experts.

## Reviewer A — <persona>
Summary: 2–3 sentences.
Strengths:  S1 ... S2 ... S3 ...
Weaknesses: W1 (severity high|med|low, location, evidence, suggested fix) ...
Questions:  Q1 ... Q2 ...
Scores: overall x/6, confidence y/4; dimensions: ...
Recommendation: ...

## Reviewer B ...
## Reviewer C ...

## Meta-review (simulated discussion)
Score table | consensus points | disagreements | decisive issues |
subjective band with conditions (not a statistic) | top preparation items for rebuttal

## Verified findings
| id | finding | status confirmed/false-positive | evidence (script/log) |
```

## Frequent catches (generic)

| category | catch | prevention |
|---|---|---|
| anonymity | footnote URL to a personal code host account | anonymous mirror only; run `anonymize-check` |
| theory | body formula differs from appendix formula | copy from the proof source, never retype |
| theory | unconditional statement with a small counterexample | exhaustive search on toy parameters |
| theory | theorem hypothesis excludes some of the paper's own parameter sets | run the parameter table through each theorem |
| data | headline scope includes instances below the claimed security level | generate scope sentences from the security table |
| data | two incommensurable counters in one column | state each column's counter source in the caption |
| writing | composite gain credited to one component | label each number with its configuration |
| writing | "rigorous guarantee" for an asymptotic/heuristic result | "asymptotic, certified per instance" |
| citations | fabricated or wrong metadata, wrong author count | `cpw-lit` verifies each entry against DOI/ePrint |
| layout | overprinting after negative vspace near floats | PDF overlap scan |
| strategy | best evidence (an ablation) not in the paper | ask "which experiment would convince a skeptic?" and check it is in the body |

From Noi1r/crypto-paper-writing, MIT.

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Last commit (this folder)
Sep 29, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

mock-review skill. Agent Mart. https://agentmart.dev/skills/noi1r-mock-review (last updated 2026-10-24)