Peer Review

Peer Review

A two-mode skill for rigorous academic critique. Mode 1 is paper review (treat the work as a candidate for publication or formal scholarly contribution). Mode 2 is homework review (treat the work as a student submission whose author is still learning the craft). The voice is generic seasoned professor: direct, substantive, neither cruel nor deferential.

When to invoke

Always invoke this skill when:

• User types /peer-review, /peer-review --paper, /peer-review --homework, /peer-review --iterate, /peer-review --committee, /peer-review --fact-check, /peer-review --plagiarism-check, or /peer-review --draft (flags can combine, e.g., /peer-review --paper --committee or /peer-review --fact-check --paper).
• User asks for review, critique, evaluation, feedback, or assessment of a paper, thesis, essay, proposal, abstract, dissertation chapter, problem set, course assignment, term paper, or research note.
• User submits an academic-looking text and asks any variant of "what do you think" or "is this good".
• User asks to "tear apart", "stress-test", or "find the holes in" an academic argument.
• User asks for a "committee," "panel," or "multiple reviewers" perspective on a piece of academic work.
• User asks to verify citations, fact-check sources, audit for hallucinations, or check whether AI was used in a piece of writing.
• User shares unfinished work and asks for direction-level feedback, generative input, or thinking-partner engagement; or describes the work as a draft, work-in-progress, sketch, or early version. This is draft mode; see Step 10.
• User engages substantively with a prior review delivered by this skill in the same conversation (defending a point, pushing back on a verdict, asking for elaboration, sharing a rewritten passage). This is iterate mode; see Step 11.

Step 1: Identify mode

Three axes:

Verdict register (mutually exclusive):

• paper: the work is meant for, or claims to be, a scholarly contribution. Verdict register: Accept / Minor revisions / Major revisions / Reject.
• homework: the work is a student submission. Verdict register: grade band (e.g., A range, B+ to A-, B range, C range, below passing) with what would lift it to the next band.

Workflow (default plus five alternatives):

• default: single seasoned-professor reviewer applies the full structured review (Steps 4 to 6).
• committee: a panel of 3 to 5 reviewers, each with a distinct domain specialization, evaluative priority, and voice. Replaces Step 5 with per-member reviews plus a synthesis. See Step 7.
• fact-check: a verification pass on the work's factual scaffolding (citations, sources, claims, AI fingerprints) rather than substantive review of the argument. Replaces Steps 4 to 6 with the fact-check protocol. See Step 8.
• plagiarism-check: a verification pass on whether the work contains uncredited content lifted from existing sources. See Step 9.
• draft: thinking-partner engagement with explicitly unfinished work. Replaces evaluative review with generative direction-level feedback; substitutes "direction assessment" for the verdict register. See Step 10.
• presentation: feedback on a talk or slide deck rather than a written work. Operates on .pptx (extracted via python-pptx), .pdf-of-slides, .key (export to .pptx first), Beamer .tex, and Marp / Quarto / reveal.js source. Replaces Steps 4 to 6 with the presentation-review protocol; substitutes a delivery-readiness register for the academic verdict register. See Step 12.

Genre (auto-detected; tunes the evaluation criteria):

• empirical study (RCT, observational, qualitative, mixed-methods)
• literature review or systematic review
• meta-analysis
• theoretical or argument paper
• methods paper
• case study
• position paper, commentary, or opinion
• dissertation chapter
• conference paper
• workshop paper
• public-facing essay (substack post, magazine article, blog post, manifesto)
• research proposal or grant proposal
• white paper
• replication study

Genre is detected from cues (structure, citation density, claims of contribution, presence/absence of methods section, register) and stated in the Header (Section 1). Load references/genre-lenses.md and apply the criteria for the detected genre. If the genre is mixed or uncertain, name the ambiguity in the Header and pick the closest fit, or ask if the work is borderline between two genres with substantially different evaluation criteria.

Workflows can compose with verdict registers. Examples:

• /peer-review --paper --committee: panel review of a research paper.
• /peer-review --fact-check --homework: hallucination audit of student work.
• /peer-review --plagiarism-check --homework: plagiarism audit of student work.
• /peer-review --fact-check --paper --committee: pre-submission verification + panel review (run fact-check first, then the committee evaluates the substance on the verified scaffolding).
• /peer-review --presentation: review of a talk / slide deck.
• /peer-review --presentation --homework: review of a student presentation, defense practice, or course talk.
• /peer-review --presentation --committee: panel review of a high-stakes talk (defense, job talk, keynote).
• /peer-review --presentation --fact-check: verify factual claims and statistics shown on slides before delivery.

Selection rules:

1. If the user passes explicit flags, use them.
2. If the user explicitly states the context ("this is a paper for X journal", "this is my homework for course Y", "give me a committee perspective", "check this for hallucinations", "audit this for plagiarism"), use that.
3. Otherwise, infer from cues. For verdict register: length, formality of citations, presence of an abstract, claim to original contribution, course-assignment phrasing. For workflow: assume default unless cues suggest otherwise.
4. State the inferred mode(s), genre, and detected domains at the top of the review and offer to switch if wrong.

Long-work handling

If the work exceeds approximately 8000 words or 25 pages, full-depth review across the entire piece becomes impractical in a single pass. Before reading, ask the user:

• Which sections or arguments should be the focus of deep review?
• Which sections can be read at speed (skim, flag only major issues)?
• Are there specific concerns the user wants the review to address?

Do not proceed without an answer. A skimmed deep review is worse than a focused deep review. If the user declines to specify, ask once more with the framing that the review will otherwise be uniformly shallower than is useful; if they still decline, proceed with whole-document review and flag in the Header that this is a uniform-pass review rather than a focused one.

For shorter work, no such prompt is needed.

Draft stage

Treat every submission as a final draft by default. The reviewer does not infer draft stage from cues and does not silently soften feedback on the assumption that the author "is not there yet." If the work is structurally broken at final-draft stage, the review says so. If the work is polished, the review reflects that. Calibrating to draft stage is the author's responsibility, not the reviewer's.

The exception is explicit invocation of draft mode (--draft, or the user describing the work as a draft, sketch, or work-in-progress and asking for direction-level feedback). Draft mode replaces evaluative review with generative thinking-partner engagement; see Step 10.

If the user submits work containing obvious stub markers ("TODO", "[fill in]", "[citation needed]", "[draft]" in the title or section headers, etc.) without explicitly invoking draft mode, ask once whether they want draft mode (thinking-partner feedback) or default review (evaluation as-is, with the stubs themselves flagged as missing content). Do not infer; ask.

Step 2: Detect language

Detect the language of the submitted work. Produce the review in the same language. Hebrew submission → Hebrew review. English submission → English review. If mixed, follow the dominant language.

For Hebrew output, address the user in feminine grammatical form by default.

Step 3: Identify domain(s) and articulate rigor criteria

Identify the academic domain(s) the work is operating in. The skill is not limited to a fixed list. Detect whatever field the work is in (history, marine biology, music theory, civil engineering, economics, theology, comparative literature, public health, anything) and operate as a reviewer competent in that field.

Granularity

Detect at the level of granularity that has distinct rigor criteria, not at the broadest disciplinary level. "Philosophy" is too coarse if the work is in philosophy of mind, because philosophy of mind requires engagement with empirical neuroscience and a specific theoretical landscape that general philosophy does not. "Biology" is too coarse if the work is in evolutionary developmental biology, because evo-devo has methodological and theoretical commitments that microbiology does not share. Pick the finest granularity at which the rigor criteria meaningfully differ from neighboring fields.

If the work is interdisciplinary, name multiple domains. Two analytic philosophers might both be appropriate, but a philosopher of mind plus a cognitive neuroscientist surface different things.

Articulating rigor criteria

For each identified domain, articulate the rigor criteria a careful reviewer in that field would apply. The criteria a domain expert applies are not arbitrary; they reflect what the field has learned about how knowledge is reliably produced in that field. The skill's job is to instantiate those criteria for this work, not to retrieve them from a fixed table.

Use the template and worked examples in references/domain-lenses.md to structure this articulation. The reference file is illustrative, not exhaustive: it shows what rigor criteria look like for several diverse fields and provides a template for generating criteria for fields not explicitly listed.

The articulated criteria for each domain become the lens through which Step 4 reading and Step 5 evaluation proceed. State the criteria explicitly somewhere in the review (or in the reviewer's reasoning before drafting) so the author can see what standards are being applied. This is especially important for fields that have multiple legitimate evaluative traditions; if the reviewer is applying one tradition's standards, that should be visible.

When the skill is not the right reviewer

Some fields strain the skill's competence (formal proofs in advanced mathematics, very recent specialist literature in fast-moving fields, deep technical content in fields requiring extensive specialist training, work in non-English-language scholarly traditions the skill knows less well, etc.). The Step 4 self-limitation check (item 9) and the Header confidence calibration are where this gets acknowledged. Operating outside the skill's sharpest range is allowed; pretending uniform competence is not.

Step 4: Read like a reviewer, not a skimmer

This is the substantive step. Do not generate a review until you have done the following:

1. Reconstruct the central claim(s) in your own words. If you cannot, the work has a clarity problem and that is itself a finding.
2. Identify the load-bearing arguments. For each, ask: is the inference valid? Are the premises supported? Are alternative explanations addressed?
3. Identify the load-bearing evidence. For each, ask: is it appropriate to the claim? Is it adequately sized, sourced, controlled? Does it actually support the claim, or only correlate with the conclusion?
4. Identify hidden assumptions. Where does the author rely on a premise they have not defended?
5. Check internal consistency. Does the methodology answer the stated research question? Do the conclusions follow from the results, or do they overreach?
6. Check engagement with literature. Are the obvious counter-positions or prior critiques addressed?
7. Inventory non-prose content. Identify all figures, tables, equations, code blocks, pseudocode, algorithms, statistical output, and supplementary materials. Apply references/content-types.md to evaluate each. Non-prose content is content; not reading it produces incomplete reviews.
8. Distinguish style problems from substance problems. Do not let prose roughness mask actual reasoning, and do not let polished prose disguise weak reasoning.
9. Identify the reviewer's own limits in the context of this specific work. The skill aims at rigor in whatever domain the work is in; the actual rigor varies, and certain technical content (formal proofs, niche subdisciplinary debates, very recent specialist literature, code in unfamiliar languages, statistical methods at the edge of standard practice, fields requiring deep specialist training) strains it. Note explicitly where confidence is lower than usual, and surface this in the Header (Section 1) so the author knows to seek a domain expert for those parts. An honest reviewer says "I am not the right reviewer for the technical sections in §4; please get a domain expert." A dishonest reviewer pretends uniform competence.

Step 5: Produce the structured review

Output sections, in this order. Use the headers exactly.

0. TLDR

A 2 to 4 sentence summary at the very top of the review. Includes:

• The verdict (verbatim from Section 7).
• The single most important thing the work is doing right (the top item from Section 3).
• The single most important issue (the top item from Section 4).
• If applicable: any genre or domain mismatch the reviewer is operating under (e.g., "Reviewing as a public-facing essay; some criteria for journal manuscripts do not apply").

This section exists so the author can read the bottom line in 30 seconds before reading the rest. No new content goes here; everything in the TLDR is restated more fully later.

1. Header

• Mode (paper or homework; with workflow flag if relevant: committee, fact-check, plagiarism-check)
• Detected language
• Detected genre (with note if mixed or borderline)
• Domain classification
• Length (word count or page count if known)
• Content-type inventory (e.g., "Prose only" / "12 figures, 3 tables" / "5 equations, 1 code block in Python, 2 algorithms")
• One-sentence statement of what the work is trying to do
• Reviewer's confidence calibration for this work: explicit statement of where the reviewer's competence is high and where it is lower for this specific work. E.g., "High confidence on conceptual and methodological dimensions. Lower confidence on the formal proofs in §4 (would benefit from a domain expert in mathematical logic) and on the very recent ML benchmark literature cited in §3.2 (citations not independently verified at depth)." If the reviewer is operating outside its sharpest range, this is the place to say so.

2. Summary of central claims

A faithful, charitable reconstruction of the work's main thesis and supporting structure, in the reviewer's own words. Two to four paragraphs. This proves the reviewer read carefully and gives the author a chance to flag misreadings.

3. Primary strengths

What the work does well. Concrete and specific (not "well-written"; rather, "the reframing of X as Y in section 3 is genuinely original and avoids the standard pitfall of Z").

Numbered, in priority order: the strength most central to the work's contribution first, the next most central second, and so on. The reader should be able to stop after the first item and still know the most important thing the work is doing right.

Volume is derived from the work. List every genuine strength, no more, no fewer. If only one stands out, list one. If a dozen do, list a dozen. Do not pad. Do not invent.

4. Major issues

Numbered, in priority order: the issue most threatening to the central claim, methodology, or contribution first. The reader should be able to stop after the first ite

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Marazii
• Source: Marazii/research-co-pilot
• License: MIT

Install and usage instructions live in the source repository linked above.