git:20261020.2619efe to git:20261020.41fc318

---

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, a Journal

Impact Factor)? 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,

Gates, etc. These are hard filters too (a Wellcome paper

recommended to a subscription-only journal is a wrong

recommendation). See "Funder rules in the hard filter" below.

- **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 in 2021): four journals were

closed in 2023 because of paper-mill interference, and 19 more

in 2024.

- **Solicitations that imitate a real journal** — the ICMJE's

January 2025 joint editorial advises authors who receive a

solicitation that appears to come from a legitimate journal to

forward it to that journal and ask whether it is genuine. Pass

this advice on when a declared venue arrived by solicitation.

**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 (the exception is an HHMI-covered article; see "When

a funder changes the default" below). 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.

### When a funder changes the default

Per [[preprint-landscape]], two declared funders change the ask.

The policies are dated (checked October 2026); tell the author to

confirm against the funder's current text.

- **HHMI — required, not opt-in.** For articles submitted from

1 January 2026, HHMI requires initial and revised preprints

under CC BY 4.0 for original research articles that are major

contributions from a covered HHMI lab (someone from the lab is

(co-)first, (co-)last or (co-)corresponding author; the covered

roles are named in the policy). Reviews, perspectives and

commentaries are exempt. When the declared state shows a covered

article, skip the question and include the preprint section.

Restrict servers to bioRxiv (preferred), or medRxiv, ChemRxiv

or arXiv for out-of-scope work; HHMI says other servers "should

not be used". Note the timing: the initial preprint goes up

promptly after the journal sends the manuscript to review (HHMI

recommends posting by submission); the revised preprint goes up

before journal publication. If coverage is unclear from the

declared state (article type, lab role), ask once rather than

assume.

- **Gates — strong prompt, not enforced.** Gates expects funded

manuscripts to be preprinted "to the extent feasible", except

where the grantee decides a preprint is inappropriate. Ask the

preprint question with that expectation stated; respect a no.

### 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. Do not send an unreviewed review (survey)

or position paper to arXiv CS: since October 2025 that category

accepts them only after completed peer review at a journal or

conference. For HHMI-covered articles, only HHMI's designated

servers (above).

- **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

- **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. It

has an indexing cost: since Web of Science put eLife on hold in

October 2024, eLife is not eligible for a Journal Impact Factor.

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.

- **Never presents a history-driven tier shift as a judgement of

the work's merit.** Editorial-stage decisions at *Science* and

*Science Advances* show selection associated with institutional

prestige, team size, topic and country (Zhang et al. 2026). That is a reason to

calibrate the `Stretch` tier against the author's context; it

says nothing about the manuscript.

### 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 (a sizeable minority of papers reach their final venue

only after a rejection elsewhere; naming the tiers makes a

stretch attempt a deliberate choice);

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. See "Funder rules in the hard filter"

below.

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.

### Funder rules in the hard filter

Per [[venue-selection]] and [[preprint-landscape]], declared

funders bring APC and indexing rules that apply before tiering.

As of October 2026:

- **Wellcome** funds APCs only in fully open-access journals or

platforms indexed in DOAJ (from 1 January 2025).

- **HHMI** budgets no longer pay APCs at hybrid journals.

- **Gates** pays no APCs, and requires immediate CC BY 4.0

availability.

- **NIH** proposed limits on allowable publication costs in July

2025. No final notice had been found as of October 2026; treat

them as proposed, not as a filter.

A declared `indexing_requirements` entry for a Journal Impact

Factor excludes eLife, which is not eligible for one. Funder

policies change; caveat the date and point the author at the

funder's current policy, as with venue policies.

## 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. Bahadoran

et al.'s 2020 journal-selection guide; Solomon and Björk on

OA-venue selection; Bordage 2001 on reviewers' reasons for

rejection (the methodological-fit axis); Calcagno et al. on

submission trajectories (about 75% of published articles went

first to the journal that published them, so a sizeable

minority arrive after a rejection), which motivates the tiered

output. Also funder APC rules, eLife's JIF ineligibility, and

Zhang et al. 2026 on editorial-stage selection, which bounds

how pub history may be used.

- [[predatory-publishing]] — the refusal layer. Beall's

detection heuristics, the per-journal-not-per-publisher

principle (MDPI nuance), the Hindawi-Wiley case study (four

closures in 2023, 19 in 2024), the ICMJE's January 2025

editorial on suspicious solicitations, 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), arXiv CS's rule on

review and position papers, the HHMI requirement and Gates

expectation, 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. *Nature* states that

about 8% of submissions are accepted and most are declined

without being sent out for peer review; the Bornmann, Mutz and

Daniel meta-analysis (mean κ ≈ 0.17 for inter-reviewer

agreement) motivates 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.