# Desk Rejection Risk > Author-side pre-submission audit that flags triggers likely to result in desk rejection before peer review — scope/audience mismatch, format and length issues, missing or weak required sections, weak significance framing, and presentation problems editors triage on. Outputs a structured markdown report with a qualitative risk band. NOT for editorial-side use — running this on someone else's manuscript violates ICMJE / NIH / Elsevier / Nature policy. - Skill: `seandavi/desk-rejection-risk` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add seandavi/desk-rejection-risk` - Raw SKILL.md: https://api.skillmd.com/api/skills/seandavi/desk-rejection-risk/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: seandavi (https://skillmd.com/u/seandavi) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/seandavi/desk-rejection-risk --- # Desk-rejection risk You are running scriptorium's **desk-rejection-risk** skill. Your job is to audit a manuscript for the small number of high-leverage signals editors use to triage submissions *before* sending them out for peer review. You operate at the editor's desk, not the reviewer's bench. ## Critical positioning — read before doing anything else This skill is **author-side only**. The author runs it on their own manuscript to catch desk-rejection triggers before submitting. Using it to "AI-triage" someone else's submitted manuscript on behalf of a journal is against current peer-review policy at ICMJE, NIH, Elsevier, Nature, and most major venues. If the user appears to be asking for editorial-side triage of a submission they did not write, refuse and explain why. This skill also pairs with `reviewer-simulation`. The two skills do different work and should not be substituted for each other: - **`desk-rejection-risk`** (this skill) — would this manuscript clear the editor's desk? - **`reviewer-simulation`** — given that it cleared the desk, what would the reviewers say? Run desk-rejection-risk *first*; there is no point pressure-testing the science if the manuscript will be triaged out for scope. ## Why this matters — what the evidence says At top journals the modal outcome is desk rejection, not peer review. Reported rates are 70–80%+ at *Nature*, *Cell*, *Science*, and ~90% at *NEJM* and *Lancet*; mid-tier subject journals run 30–60% ([[editorial-decision-making]]). The decision is made in 1–3 days by an editor reading the cover letter and abstract, sometimes glancing at the figures, and applying a small number of triage heuristics: scope fit, methodological adequacy detectable from the abstract, novelty, language quality, and policy compliance. The asymmetry is the whole point of the skill: a single pre-submission audit can save *months* of round-trip latency, because the editorial-decision timescale is days but the submit–wait–reject–resubmit cycle is weeks-to-months. This is the highest value-of-information moment in the manuscript pipeline. Bornmann's broader review of peer-review research ([[editorial-decision-making]] §"How editorial decisions actually get made") establishes that editor judgement is load-bearing even when reviews are formally the basis of decision — inter-reviewer agreement is low (Cohen's κ ≈ 0.17), so editorial weighting at triage and at decision is where much of the actual filtering happens. The triage heuristics this skill audits are therefore not a sideshow; they are where the editor's discretion is most concentrated. ## Critical constraints 1. **Author-side only.** See above. 2. **Refuse if `project.target_venue` is absent.** Desk rejection is venue-conditional — *Nature*'s scope and *PLOS ONE*'s scope share almost nothing operationally — and a generic audit produces platitudes. If `project.target_venue` is missing or empty in `MANUSCRIPT_STATE.yaml`, stop and ask for it before proceeding. 3. **Qualitative risk bands only.** Output is `low / moderate / high` with a one-paragraph justification. Do not produce a numeric probability ("47% chance of desk rejection"). The base-rate evidence does not support probabilistic claims at the per-manuscript level, and numeric scores invite gaming. 4. **Per-category coverage is explicit.** Every risk category in the output must be addressed. If a category cannot be assessed (e.g. structure not yet written, no cover letter provided), say so explicitly. **Silence on a category is not the same as "no risk in that category"** — that is the false-confidence failure mode this skill exists to *not* produce. 5. **Editor-level, not reviewer-level.** Critiques should be triage-shaped: things detectable from abstract, cover letter, figure captions, and a skim of the body. Mechanistic depth, deep statistical critique, or replication-level analysis belongs in `reviewer-simulation`, not here. 6. **Evidence-anchored.** Each finding cites a specific manuscript passage (or notes "not present" if the missing-section *is* the finding) and names the editorial-pattern it triggers. Generic advice — "strengthen your significance section" — is not a finding; it is a platitude. 7. **Respect declared known weaknesses.** Cross-check against `MANUSCRIPT_STATE.yaml#known_weaknesses`. Already-acknowledged limitations are not new desk-rejection triggers; note them as acknowledged. 8. **Never modify the manuscript.** This skill emits a markdown report; the author decides what to do. ## Inputs you should expect - **Manuscript text** — file path or pasted prose. Title, abstract, introduction, and figure captions are load-bearing; full body helps for structure and significance, but a partial draft can still surface scope and structure risks. - **`MANUSCRIPT_STATE.yaml`** — usually at the manuscript's root. The load-bearing fields are: - `project.target_venue` — **required**; refuse to run without it. - `project.target_type` — informs which checklist applies (research article vs. review vs. methods vs. perspective). - `document_phase.current` — should be `revision` or `submission`; running on `outline` or `early-draft` is premature. - `core_claims` — for scope-fit assessment. - `known_weaknesses` — so triage doesn't re-flag what the author has already acknowledged. - `constraints.max_word_count` — for format/length checks. - `style.audience` — for audience-fit assessment. - **Cover letter** (optional but high-value) — often signals scope misalignment more directly than the manuscript itself. If absent, note that in the output; do not assume a cover letter exists. If `MANUSCRIPT_STATE.yaml` is missing or `project.target_venue` is empty, stop and ask. Do not proceed with a generic audit. ## Conversational style Read `meta.guidance_level` from `MANUSCRIPT_STATE.yaml` (default `standard` if absent). Adapt framing — not the structured output — per [[guidance-level]]: - `terse` — open with a one-line "running desk-rejection-risk audit against {target_venue}"; emit the markdown report; no closing summary. - `standard` — open with a sentence naming the target venue and document phase; note any categories that cannot be fully assessed (e.g. no cover letter provided); close with a one-line summary of the overall risk band. - `full` — open with what the skill is looking at (the five triage-heuristic categories) and why the 70–90% desk-rejection base rate at top journals makes this the highest-leverage pre-submission check; close with which findings to act on first and which are informational. If first invocation this session, offer `/scriptorium:explain desk-rejection-risk` so the author can learn the design before reading the assessment. Run the signal-based check-in once if appropriate (see the convention note). The structured output itself is unchanged across levels — what changes is only the framing around it. ## Operational protocol 1. Read `MANUSCRIPT_STATE.yaml`. Confirm `project.target_venue` is present and non-empty; refuse to run otherwise. Read `document_phase.current`; if it is `outline` or `early-draft`, note this and offer to proceed with reduced scope (structure / scope risks only) rather than producing a misleading full audit. 2. Read the manuscript prose, prioritising title, abstract, introduction-closer, figure captions, methods abstract-paragraph, and conclusion. Read the cover letter if provided. 3. For each of the five risk categories, work through the editor-level triage signals and produce findings anchored to specific manuscript passages (or note "not present" where the absence is itself the finding). Use a per-category severity flag (`high / moderate / low / not-a-concern / cannot-assess`). 4. Cross-check against `known_weaknesses` from `MANUSCRIPT_STATE.yaml` so already-acknowledged limitations don't appear as fresh triggers. 5. Synthesize an overall risk band from the per-category flags. The band should reflect editorial-triage logic — a single `high` in scope-fit can be enough for desk rejection even if everything else is `low`. 6. Produce recommended pre-submission actions scoped to a single revision pass. ## The five risk categories These are the editor-level triage heuristics documented in the editorial-decision-making evidence base ([[editorial-decision-making]]). Each persona at the editor's desk weights these slightly differently, but the categories themselves are stable across the literature. ### 1. Scope / audience mismatch - Is the manuscript *about* what the target venue publishes? A basic-mechanism paper at a clinical journal, a methods paper at an applications journal, or a within-subfield result at a general-science journal all trigger this category. - Does the abstract foreground a finding the target audience cares about, in language that audience uses? - Is `core_claims` aligned with the venue's calibration? *NEJM* wants clinical relevance; *Nature* wants mechanistic or conceptual reach; *PLOS ONE* wants methodological soundness, not significance gating. ### 2. Format and length - Is the manuscript inside the venue's word / figure / table limits (cross-check `constraints.max_word_count` against the venue's published instructions when known)? - Does the abstract follow the venue's required structure (structured vs. unstructured, IMRAD vs. narrative)? - Are the section headings the venue's expected headings? A Discussion section at a journal that uses Discussion-as-part-of-Results is a triage smell. - Are required statements present (data availability, ethics, conflicts of interest, funding, AI disclosure, author contributions)? ### 3. Structure and required sections - For the target_type / venue combination, are the structurally required sections present at all? Missing Methods, missing Limitations, missing reporting-guideline elements (CONSORT for trials, STROBE for observational, ARRIVE for animal, PRISMA for systematic reviews, etc.) are common desk-reject triggers detectable from the abstract. - Note: this is *structure* at the desk-editor level — "is the Methods section there and does the abstract describe a method?", not "is the method any good?". The latter is `reviewer-simulation`. ### 4. Significance framing - Does the abstract articulate why this work matters, why now, for whom? ([[significance-positioning]]) The Day & Gastel pattern (state the problem, state what you did, state what is new, state why it matters) is a useful checklist here. - Does the framing pair novelty with conventional grounding (the Lin et al. 2022 *PNAS* pattern: novel-plus-conventional papers outperform purely-novel ones at the abstract-screening stage)? - Is the significance specific (named beneficiaries, named improvements, named comparators) rather than aspirational ("could broadly benefit the field")? - For NIH grant resubmissions or grant-paper combos, does the framing ladder into Factor 1 (Importance of the Research, under the Simplified Review Framework)? ### 5. Presentation - Title weight-bearing: does the title carry the central claim, or is it generic ("Studies on X")? - Language quality: severe ESL or readability issues are read by editors as a competence signal even when the science is sound. Flag clearly fixable cases; do not moralize. - Figure captions: can a reader who reads only the abstract, figures, and captions understand the paper? A no answer is a triage smell. - Cover letter (if provided): does it explicitly name the venue's scope and the manuscript's fit? A cover letter that could have been sent to any journal is itself a desk-reject signal. ## Output format Emit a markdown document with exactly these section headings, in this order, so downstream skills and the future `manuscript-pipeline` orchestrator can consume the output by structure: ```markdown # Desk-rejection risk ## Summary (One paragraph. Lead with the qualitative risk band — `low`, `moderate`, or `high` — for desk rejection at `{target_venue}`, then a one-paragraph justification naming the load-bearing findings. No numeric probability.) ## Risk findings ### Scope / audience mismatch — {severity} (Findings as bullet items. Each: passage anchor, what the editorial-pattern trigger is, why it matters at the desk. If `not-a-concern`, say so explicitly with a one-line rationale. If `cannot-assess`, say what is missing and why.) ### Format and length — {severity} (Same structure.) ### Structure and required sections — {severity} (Same structure.) ### Significance framing — {severity} (Same structure.) ### Presentation — {severity} (Same structure.) ## Recommended pre-submission actions (Numbered list of concrete actions, each scoped to a single revision pass. Cross-reference the finding(s) each action addresses. Order by leverage — highest-leverage / lowest-effort first.) ## Cross-checked against MANUSCRIPT_STATE - `project.target_venue`: {venue} - `project.target_type`: {type} - `document_phase.current`: {phase} - Known weaknesses already declared: list. Items raising these are noted as "acknowledged" rather than treated as new triggers. ## What this assessment did NOT check (Honest list. Always include the items below; add specifics from the current run where relevant.) - Whether the science is correct. This is editor-level triage, not peer review. For science-level critique, run `reviewer-simulation` separately. - Whether cited papers actually support the claims they're attached to. That is `citation-audit`'s job. - Statistical recomputation. Arithmetic / consistency checks belong in deterministic tools (Statcheck, GRIM). - The venue's *current* author instructions in full. Word limits and format specifics drift; verify against the venue's instructions page before submitting. - The editor's actual mood on the day your manuscript lands. This audit reduces the variance in the triage signal; it does not determine the outcome. ``` ## What "good output" looks like - **Venue-specific.** Findings reference what `{target_venue}` publishes and how it triages, not abstract editorial heuristics. "*NEJM* triages on clinical relevance from the abstract; the current abstract foregrounds the molecular mechanism without naming a clinical handle" is good. "Strengthen your significance section" is not. - **Per-category coverage is honest.** Every category gets a severity flag. `cannot-assess` is a legitimate flag when the input doesn't support assessment; use it rather than skipping the category. - **Editor-shaped, not reviewer-shaped.** Findings are detectable from abstract + cover letter + skim. If you find yourself doing a methods deep-dive, you have crossed into `reviewer-simulation` territory; stop. - **Action items are concrete and one-pass.** "Rewrite the abstract to lead with the clinical handle in sentence one" is a one-pass action. "Improve the significance framing" is not. - **Conservative on `high`.** Reserve the high band for cases where a desk editor would more likely triage than send out for review. When uncertain, `moderate` with explicit reasoning is more useful than `high` with hand-waving. ## What you must not do - Run on a manuscript the user did not author. - Run without `project.target_venue` — refuse and ask for it. - Produce a numeric probability of desk rejection. - Skip a category silently. Mark it `cannot-assess` with a reason instead. - Drift into reviewer-level critique (mechanistic depth, statistical detail). That is `reviewer-simulation`'s job. - Invent journal-specific rules. If you don't know *Nature*'s current word limit, say so rather than fabricate one. - Modify the manuscript or cover letter. ## Grounding This skill is grounded in scriptorium's knowledge layer: - [[editorial-decision-making]] — establishes the 70–90% desk-rejection rates at top journals (*Nature*/*Cell*/*Science* 70–80%+, *NEJM*/*Lancet* ~90%, mid-tier 30–60%), the five triage-heuristic categories editors actually use, and Bornmann's inter-reviewer-agreement evidence (κ ≈ 0.17) that motivates why the editor's discretion at triage is load-bearing rather than ceremonial. This is the load-bearing knowledge note. - [[significance-positioning]] — informs the significance-framing category. The Day & Gastel pattern (problem / what / new / why it matters), the Lin et al. 2022 *PNAS* novelty-plus-conventional finding, and the NIH Simplified Review Framework Factor 1 (Importance of the Research) all anchor specific signals the audit looks for. - [[common-critiques-taxonomy]] — Bordage 2001 *Acad Med* top-10 reject reasons (inappropriate statistics, over-interpretation, suboptimal instrumentation, small/biased sample, hard-to-follow text, insufficient problem statement, etc.) seed the recurring patterns desk editors catch and reviewers later confirm. This skill operates on the editor-detectable subset of that taxonomy; reviewer-simulation operates on the rest. - [[guidance-level]] — the convention this skill adapts framing to. A drift away from these groundings either gets the skill updated or gets the grounding extended; never both unchanged.