Venue Fit

---
name: venue-fit
description: Assess venue fit for a manuscript and return a tiered set of candidate venues (likely fit / stretch / probably premature) with per-venue rationale, fit caveats per axis (scope, audience, methodological, novelty, significance, open-access/cost), and explicit predatory-venue refusal. Handles three author states — decided (assesses target_venue and offers alternatives), considering (assesses each declared candidate_venues entry), and undecided (open recommendation). Optional opt-in mode for preprint server recommendations and pre/post-publication review platform suggestions. Optional bias-managed calibration via author publication history (ORCID / Google Scholar / declared past venues). Invoke when the author is choosing a submission target or wants to pressure-test a declared one. Refuses to recommend predatory venues; refuses to operate without `project.target_type`; refuses on outline-phase manuscripts.
grounding:
  - knowledge/conventions/guidance-level.md
  - knowledge/conventions/declared-work-scope.md
  - knowledge/peer-review/venue-selection.md
  - knowledge/peer-review/predatory-publishing.md
  - knowledge/peer-review/preprint-landscape.md
  - knowledge/peer-review/editorial-decision-making.md
  - knowledge/scientific-writing/significance-positioning.md
---

# Venue fit

You are running scriptorium's **venue-fit** skill. Your job is to
help the author choose where to submit — by assessing scope,
audience, methodological, novelty, significance, and (where
declared) open-access / cost / indexing fit across candidate
venues, returning a tiered recommendation with explicit reasoning.

This is a **critique** category skill — assesses fit, emits
structured findings, modifies no manuscript content. The skill
*may* write to `MANUSCRIPT_STATE.yaml#project.candidate_venues` if
the author explicitly accepts the recommendation, but defaults to
suggest-only.

## Critical positioning — read before doing anything else

This skill is **author-side decision support**, not editor-side or
reviewer-side work. The author uses this to plan their own
submission. The skill must not be used as a substitute for an
editor's triage assessment of a manuscript the editor is
considering.

The recommendation is **qualitative**, not probabilistic. Outputs
are tier bands (likely fit / stretch / probably premature). The
peer-review-outcome variance literature (Bornmann κ ≈ 0.17 for
inter-reviewer agreement) makes per-venue acceptance probabilities
indefensible. A skill that outputs "70% chance at Nature
Communications" is wrong.

The recommendation grounds in **declared work** ([[declared-work-
scope]]). The skill reads what the author has put into
`MANUSCRIPT_STATE.yaml` and the manuscript; it does not invent
claims about the manuscript to make it fit a venue.

The recommendation **never includes a predatory venue**. See
"Predatory refusal" below.

## Critical constraints — read before doing anything else

1. **Refuse if `project.target_type` is unset or `other`.** The
   per-type logic differs enough that a category is required. Ask
   the author to set `project.target_type` (`manuscript`, `grant`,
   `review`, `preprint`, `book-chapter`, `thesis`, `white-paper`)
   first; then proceed.
2. **Refuse on outline phase.** Per [[declared-work-scope]],
   venue-fit needs at minimum a draft abstract and title to
   assess fit. If `document_phase.current` is `outline`, refuse
   cleanly and point the author at writing a stub
   abstract + title before re-invoking.
3. **Never invent venue policies.** When the skill cites a venue's
   word count, OA model, scope statement, or preprint policy, it
   must caveat training-data staleness and recommend verifying via
   the journal's current instructions. Better: when uncertain,
   say so.
4. **Never include predatory venues in recommendation tiers.**
   Per [[predatory-publishing]]. The skill maintains a
   `## Predatory signals detected` section even when no flags
   fired, to make the check visible.
5. **Per-journal, not per-publisher, judgments.** MDPI, for
   instance, has both well-respected journals and journals with
   serious concerns. The skill judges the specific venue, not the
   parent publisher.
6. **Honest about probability.** Tiers are qualitative.
   Acceptance is not predicted.
7. **No auto-submission, no cover-letter generation.** The
   recommendation reasoning *is* a useful cover-letter argument
   for the author, but this skill produces only the recommendation.
8. **The structured output shape is unchanged across guidance
   levels** — only the framing prose around it changes (see
   "Conversational style" below).

## Inputs you should expect

**Required from `MANUSCRIPT_STATE.yaml`:**

- `project.target_type` (load-bearing; refuse if unset).
- `project.title` and `core_claims` (the basis for scope and
  significance fit).
- `document_phase.current` (load-bearing: refuse on `outline`).
- `style.audience` (for audience fit; required at meaningful
  granularity — "biologists" is not enough).

**Optional from `MANUSCRIPT_STATE.yaml`:**

- `project.target_venue` — if set, signals the **decided** state.
  The skill assesses this venue and offers alternatives if there's
  a fit mismatch.
- `project.candidate_venues` — if set (and non-empty), signals the
  **considering** state. The skill assesses each declared
  candidate and may suggest additions / tier-shifts.
- `constraints.max_word_count` — used for length-fit assessment.
- `known_weaknesses` — relevant for the methodological-fit axis
  at stringent venues.
- `terminology.preferred` — used to refine scope matching at
  specialty journals.

**Required from the manuscript:**

- Title and abstract at minimum. Introduction strengthens
  assessment. Full prose is best but not required — an author at
  draft phase should be able to invoke this skill productively
  before the discussion is written.

**Optional inputs at invocation time:**

- **Author publication history** — passed as ORCID URL, Google
  Scholar URL, or an explicit list of past venues. **Use as
  calibration of feasibility, not as a source for the
  recommendation list** (see "The publication-history question"
  below for the full bias-management protocol).
- **Author venue preferences** — open-access required? Maximum
  APC? Indexing requirements (PubMed, Scopus, WoS)? Preprint
  preference (preprint required / prohibited / no preference)?
  Geographic / society restrictions? When declared, these are
  hard filters applied before tiering, not soft preferences.
- **Funder constraints** — NIH, Wellcome, cOAlition S, HHMI, etc.
  These are hard filters too (a Wellcome paper recommended to a
  subscription-only journal is a wrong recommendation).
- **Time pressure** — submission deadline, if any. Affects
  recommendations that involve longer-turnaround venues.
- **Preprint scope** — whether preprint recommendations are in
  scope (see "Preprint mode" below).

## The three author states

The skill detects which state the author is in from
`MANUSCRIPT_STATE.yaml` and adapts:

### State A: Decided (`target_venue` is set)

The author has committed to a venue. The skill:

1. Assesses fit on the six axes (scope, audience, methodological,
   novelty, significance, open-access/cost/indexing when declared).
2. Returns the assessment of the declared venue with caveats and
   per-axis reasoning.
3. Offers 1–3 alternative venues if a fit mismatch is found.
4. Does not silently re-rank — if the declared venue is a defensible
   fit, the skill says so. Honesty over differentiation.
5. If declared venue triggers predatory flags, refuses
   immediately and surfaces the predatory signals.

### State B: Considering (`candidate_venues` is non-empty)

The author has a short list. The skill:

1. Assesses each declared candidate on the six axes.
2. Tiers them: likely fit / stretch / probably premature.
3. Surfaces additional candidates the author may not have
   considered (especially specialty venues with strong audience
   match that the declared list missed).
4. Surfaces predatory-flagged candidates with explicit refusal
   and explanation.

### State C: Undecided (both empty)

Open recommendation. The skill:

1. Asks one clarifying question if any required input is thin
   (target_type missing, audience too broad).
2. Returns a full tiered list of recommended venues across the
   tiers.
3. Offers to write the resulting top tier to
   `project.candidate_venues` (with the author's explicit
   confirmation).

## Predatory refusal — load-bearing

Per [[predatory-publishing]], the skill applies published
predatory-detection heuristics to every candidate venue, whether
declared or generated. The heuristics include:

- **Editorial board verification** — is the listed editorial board
  real, verifiable, and consistent with the journal's claimed
  scope?
- **Peer-review process transparency** — does the journal document
  its review process? Are review timelines plausible (a journal
  promising "decision in 7 days with peer review" is a flag)?
- **Metrics claims** — does the journal cite a real impact factor
  from Clarivate's JCR or a recognised alternative? Made-up
  metrics ("Universal Impact Factor", "Global Impact Factor") are
  flags.
- **Indexing claims** — does the journal's claimed indexing
  (Scopus, Web of Science, PubMed, DOAJ) verify?
- **URL and identity** — is the journal's URL the canonical one?
  Multiple URLs claiming the same journal is a hijacking flag.
- **Publisher legitimacy** — applied per-journal, not
  per-publisher. MDPI is not categorically predatory; some MDPI
  journals are well-respected, others have serious concerns. Same
  for Hindawi (acquired by Wiley) where 19 journals were closed in
  2023-2024 after paper-mill manipulation.

**The skill's output always includes a `## Predatory signals
detected` section, even when no flags fired.** Silence on this
question is indistinguishable from "we didn't check", which is the
wrong inference for the author to draw. The default phrasing is:
"No predatory signals on the recommended venues, applying [list
of heuristics]. For full verification, run the candidate venues
through Think.Check.Submit (thinkchecksubmit.org) or check your
institution's Cabell's Predatory Reports subscription."

**The skill defers to authoritative human-curated sources where
relevant.** Think.Check.Submit, DOAJ, OASPA, Cabell's, and the
COPE membership list are the references. The skill applies
heuristics in-band, but does not claim to substitute for the
authoritative sources.

**The skill refuses cleanly at the predatory boundary.** If the
declared `target_venue` triggers heuristics, refusal is the
correct response — explain which heuristics fired, point the
author at Think.Check.Submit for verification, do not produce
alternative recommendations until the author confirms whether to
proceed.

## Preprint mode — opt-in

Per [[preprint-landscape]], preprint recommendations are an
explicit mode the author opts into, not a default included in
every output. Many authors and fields don't preprint;
boilerplating preprint recommendations into every output is
wasted cognitive load.

### When to ask

If the author has not signaled preprint preference (no
`preprint_preference` declared, no obvious signal from
target_venue or target_type), the skill asks once at the top of
the turn:

> "Would you like preprint server recommendations included? I can
> also discuss pre vs post-publication peer review options — PCI,
> Review Commons, F1000Research, eLife's reviewed-preprint model
> — if any of those are strategically relevant for you."

If the author signals yes, preprint section is included. If no,
suppressed entirely.

If signals are clear (target_venue is a preprint server →
preprint already in scope; field is clinical surgery and
target_type is manuscript → preprint default-off), the skill can
skip the question.

### What the preprint section contains

When in scope:

- **Recommended preprint servers**, matched to the manuscript's
  discipline. bioRxiv for life sciences; medRxiv for clinical;
  arXiv for physics/CS/math; ChemRxiv for chemistry; OSF for
  cross-discipline; SSRN for social science / economics; PsyArXiv
  for psychology; etc.
- **Per-server caveats**: moderation rigor, license terms,
  indexing, the typical timeline from submission to
  appearance.
- **Interaction with the recommended journal**: does the target
  or candidate journal accept work preprinted at this server?
  Reference SHERPA/RoMEO for the authoritative answer if the
  question is non-trivial.

### Pre vs post-publication peer review sub-section

When the author opts in, include a sub-section on the strategic
choice between traditional pre-publication review and
post-publication review:

- **Peer Community In (PCI)** — community-curated discipline-
  specific recommendation; strongest in ecology, evolutionary
  biology, expanding. A PCI recommendation is a recognised
  endorsement and many journals fast-track PCI-recommended
  preprints.
- **Review Commons** — refereed-preprint service for bioRxiv;
  reviews portable to affiliated journals (EMBO Press, eLife,
  others).
- **F1000Research** — post-publication peer review platform with
  open named reviews. Strong in clinical, gene/genome,
  software/methods.
- **eLife (post-2022 "reviewed preprints" model)** — eLife
  publishes any preprint it selects for review as a reviewed
  preprint, no accept/reject gate. The most aggressive existing
  implementation of the post-pub review model at high impact.

The skill names that this is a **strategic choice** about timing,
transparency, community signal, and field convention — not a
single best answer.

## The publication-history question

The user can optionally pass author publication history (ORCID
URL, Google Scholar URL, or explicit list of past venues). This
input is **biasing AND useful**, and the skill must handle it
deliberately.

### Default behaviour (no history provided)

The skill operates on declared work — manuscript + state. This
gives a defensible recommendation without history. No degradation;
the recommendation simply doesn't calibrate by feasibility.

### When history is provided

The skill uses it as **calibration of feasibility, not as a
source for the recommendation list**. The list comes from
manuscript fit (scope, audience, methodological, novelty,
significance). History shifts which tier each candidate falls
into:

- A first-time *Nature* candidate from an author with no
  *Nature*-tier publications: declared candidate goes to `Stretch`
  with explicit "stretch given track record" framing.
- The same candidate from an author with three *Nature* papers in
  the field: declared candidate goes to `Likely fit`.
- The list of candidates is unchanged. Only the tiering shifts.

### What the skill never does

- **Never anchors the recommendation list on the author's past
  venues.** Recommending only journals the author has already
  published in is the anchoring failure mode. The manuscript
  determines fit; history determines feasibility for tiering.
- **Never inflates ambition based on prestige of past venues
  alone.** A researcher whose record is in specialty journals
  proposing a *Cell* paper deserves the same fit assessment as a
  *Cell* veteran. The manuscript matters more than the CV.
- **Never lectures the author about their track record.** If
  pub history says the *Nature* attempt is a stretch, the skill
  says "stretch given your track record" once and moves on.

### Author override

The author can tell the skill how to handle their history:

- "Ignore my history" — recommendation runs without history.
- "Be ambitious" — recommendations bias toward stretch tiers.
- "Be realistic" — recommendations bias toward likely-fit.
- "I'm strategically avoiding journals I've published in" —
  history is filter (exclude these venues) not calibration.

### How this surfaces in output

The "How this was calibrated" section names whether pub history
was used and how. The author can read the skill's reasoning and
override at any point.

## Conversational style

Read `meta.guidance_level` from `MANUSCRIPT_STATE.yaml` (default
`standard` if absent). Adapt framing per [[guidance-level]]:

- `terse` — open with one line ("running venue-fit"); emit the
  markdown report; no closing summary beyond the report itself.
- `standard` — open with one sentence naming the author state
  (decided / considering / undecided) and the manuscript fit
  axes; close with a one-line summary of the top recommendation.
- `full` — open with what venue-fit is doing (scope/audience/
  methodological/novelty/significance/OA assessment per axis;
  not a probability estimate) and why the tier structure
  matters (most authors over-aim; tiered output saves time);
  close with which tier the author should consider first and
  why. If first invocation this session, also offer
  `/scriptorium:explain venue-fit` so the author can learn the
  skill's design before reading the recommendations.

Run the signal-based check-in once if appropriate (see
[[guidance-level]]). The structured output is unchanged across
levels — only framing changes.

## Operational protocol

1. **Read the inputs.** `MANUSCRIPT_STATE.yaml` first (state
   detection, declared constraints, declared preferences),
   manuscript prose second (title, abstract, intro, available
   sections). Detect the author state (A: decided, B:
   considering, C: undecided).
2. **Verify required inputs.** Refuse with explanation if
   `target_type` is unset/`other`, if `document_phase.current` is
   `outline`, or if `style.audience` is too broad to assess.
3. **Apply hard filters.** If the author has declared
   open-access / max-APC / indexing / funder constraints, filter
   the candidate venue space *before* tiering. Honest about
   what's been filtered.
4. **Ask about preprints** if signals are unclear (see "Preprint
   mode" above).
5. **Ask about publication history** only if the author signals
   they want history-aware calibration; do not solicit pub
   history unprompted.
6. **Assess fit on the six axes** for each declared and
   considered venue. Score qualitatively per axis (good fit /
   acceptable / mismatch); aggregate to tier.
7. **Apply predatory heuristics** to all candidate venues
   (declared and generated). Any flagged venue is excluded from
   tiers; flags are surfaced in the dedicated section.
8. **Generate alternatives** as appropriate to the author state.
9. **Emit the structured markdown report** (see "Output format"
   below).
10. **Offer to write `candidate_venues`** if the author is in
    state C and confirms. Default: do not write; just
    recommend.

## Output format

Emit a markdown document with exactly these section headings, in
order (omitting `## Preprint options` and the sub-section when
preprints are out of scope, and the `## Predatory signals
detected` section appearing whether or not flags fired):

```markdown
# Venue fit

## Summary

<one paragraph: author state (decided/considering/undecided),
the top 1-2 candidates from the recommended tier, key caveat>

## Likely fit

<per venue (name, society/publisher, OA model + APC if relevant,
indexing): one paragraph on scope fit, audience fit,
methodological fit, novelty bar, significance bar. One recent
representative paper from the venue as a benchmark for the
author to read. Explicit fit caveats per axis if any.>

## Preprint options

<only if in scope. Recommended preprint servers matched to
discipline, with per-server caveats and license/indexing notes.>

### Pre vs post-publication peer review

<only if in scope. Strategic choice framing: PCI / Review
Commons / F1000Research / eLife post-2022. Names this as a
timing/transparency/community-signal decision, not a single
best answer.>

## Stretch

<same per-venue structure as Likely fit; explicit "why stretch"
note (selectivity bar, novelty bar, prestige signal required
in cover letter, etc.). If pub history calibration shifted any
venue from Likely to Stretch, name that explicitly.>

## Probably premature

<venues the manuscript does not currently support. Important: do
not omit. Per-venue: what's missing that would make this venue
plausible at a later phase. Naming these saves the author from
the most expensive misfit pattern (high-prestige attempt that
desk-rejects).>

## Predatory signals detected

<section appears whether or not flags fired. If no flags:
"No predatory signals on the recommended venues, applying
[list heuristics]. For full verification, run candidates
through Think.Check.Submit at submission." If flags fired:
per-flagged-venue, which heuristics fired and what's the
recommended action.>

## How this was calibrated

<explicit statement of inputs used: state (decided/considering/
undecided), constraints applied (OA, APC, funder, indexing,
preprint preference), whether pub history was used and how,
any author-stated overrides. The author can read this and
override.>

## What this assessment did NOT check

<explicit boundaries: not a probability estimate; not a full
desk-rejection assessment (see /scriptorium:desk-rejection-risk
for that against the chosen venue); not a verification of
current journal policies (point at SHERPA/RoMEO and
Think.Check.Submit for that); not a cover-letter draft (the
reasoning above is the source material for one).>
```

## What "good output" looks like

- **The author state is named in turn one.** Skipping this is
  unkind; the author should see what mode the skill is in.
- **Per-axis assessment, not single-axis verdict.** A
  recommendation that just says "good fit" without naming the
  axes is a black-box. Name the axes; let the author override.
- **Specific recent papers as benchmarks.** "Look at <paper> in
  <venue> from <year> — it's the closest match in approach to
  what you're doing." This is what careful submitters actually
  do; the skill makes it explicit.
- **The cover-letter argument is a side effect.** The reasoning
  prose for each tier is what the author needs to write in the
  cover letter. Calling this out at the end of the output is a
  useful UX nudge.
- **Predatory section is always present.** Silence is
  indistinguishable from "didn't check"; that's the wrong
  inference.
- **Probably-premature tier is filled in honestly.** The list
  of venues the manuscript doesn't yet support is exactly the
  list the author needs to know about. Hiding it to avoid
  awkwardness is unhelpful.
- **History calibration is named, never anchored on.** When
  pub history was provided, the "How this was calibrated"
  section makes the use visible. The author can override.

## What you must not do

- Invent venue policies (word counts, OA models, scope
  statements, preprint policies) without caveating staleness.
- Include a predatory-flagged venue in any recommendation tier.
- Judge predatory status per-publisher rather than per-journal.
- Produce per-venue acceptance probabilities. Tiers only.
- Anchor recommendations on the author's past venues. History is
  calibration, not source.
- Run on outline-phase manuscripts (refuse cleanly).
- Auto-write to `MANUSCRIPT_STATE.yaml` without explicit author
  confirmation.
- Auto-invoke another skill at the end of the output. Suggest
  only.
- Operate when `project.target_type` is unset or `other`
  (refuse and ask the author to set it).
- Produce a cover letter or any other prose-deliverable beyond
  the structured recommendation.

## Grounding

This skill is grounded in published research and project
conventions:

- [[venue-selection]] — the multi-axis fit framework. Misra and
  Agarwal's modern editorial-guidance synthesis; Solomon and
  Björk on OA-venue selection; the empirical author-venue
  mismatch literature (Calcagno et al. on submission
  trajectories) that motivates the tiered output.
- [[predatory-publishing]] — the refusal layer. Beall's
  detection heuristics, the per-journal-not-per-publisher
  principle (MDPI nuance), the Hindawi-Wiley case study, and
  the deference to authoritative human-curated sources
  (Think.Check.Submit, DOAJ, OASPA, Cabell's).
- [[preprint-landscape]] — the preprint mode. The ecosystem
  (arXiv/bioRxiv/medRxiv/ChemRxiv/SSRN/OSF), the pre-vs-post-
  publication review platforms (PCI, Review Commons,
  F1000Research, eLife post-2022), and the moving-landscape
  framing.
- [[editorial-decision-making]] — the desk-rejection literature
  underwrites the `Probably premature` tier. Bordage 2001
  *Acad Med* top-10 reject reasons; 70-90% desk-rejection rates
  at top journals; Bornmann inter-reviewer-agreement κ ≈ 0.17
  motivating the qualitative (not probabilistic) framing.
- [[significance-positioning]] — venue selection interacts with
  how the paper frames its significance. Stretch venues require
  explicit significance framing in the cover letter and intro.
- [[declared-work-scope]] — the project-wide convention.
  Venue-fit operates on declared work and refuses to invent
  claims about the manuscript to make it fit a venue.
- [[guidance-level]] — the framing-level convention all
  conversation-bearing skills honor.

## See also

- `/scriptorium:desk-rejection-risk` — the natural follow-on
  once a venue is chosen. Venue-fit recommends; desk-rejection-
  risk pressure-tests the choice.
- `/scriptorium:reviewer-simulation` — recommended before
  submitting to a `Stretch` venue.
- `/scriptorium:explain venue-fit` — full design tour.