Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add seandavi/scriptorium --skill venue-fitgit clone --depth 1 https://github.com/seandavi/scriptoriumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/seandavi/scriptorium/venue-fit)<a href="https://agentmods.dev/skills/seandavi/scriptorium/venue-fit"><img src="https://agentmods.dev/badge/skills/seandavi/scriptorium/venue-fit.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00174 | $0.05830 |
| Opus 5 | $0.00087 | $0.02915 |
| Sonnet 5 | $0.00035 | $0.01166 |
| Haiku 4.5 | $0.00017 | $0.00583 |
Grade A, and why
venue-fit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 576 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Refuse if
project.target_typeis unset orother. The per-type logic differs enough that a category is required. Ask the author to setproject.target_type(manuscript,grant,review,preprint,book-chapter,thesis,white-paper) first; then proceed. - Refuse on outline phase. Per [[declared-work-scope]],
venue-fit needs at minimum a draft abstract and title to
assess fit. If
document_phase.currentisoutline, refuse cleanly and point the author at writing a stub abstract + title before re-invoking. - 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.
- Never include predatory venues in recommendation tiers.
Per [[predatory-publishing]]. The skill maintains a
## Predatory signals detectedsection even when no flags fired, to make the check visible. - 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.
- Honest about probability. Tiers are qualitative. Acceptance is not predicted.
- 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.
- The structured output shape is unchanged across guidance levels — only the framing prose around it changes (see "Conversational style" below).
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 576 lines · 174 tokens per session scan A 00e96a6fccfd
venue-fit is a skill published in the GitHub repository seandavi/scriptorium (3 stars, last pushed 1mo ago), licensed MIT. It adds 174 tokens to every session and 5,830 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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