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 agentmods add skills/matthewdigiuseppe/mstack/journal-fitnpx skills add matthewdigiuseppe/MStack --skill journal-fitgit clone --depth 1 https://github.com/matthewdigiuseppe/MStackWrote 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/matthewdigiuseppe/mstack/journal-fit)<a href="https://agentmods.dev/skills/matthewdigiuseppe/mstack/journal-fit"><img src="https://agentmods.dev/badge/skills/matthewdigiuseppe/mstack/journal-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 | $0.00058 | $0.00893 |
| Opus 5 | $0.00029 | $0.00447 |
| Sonnet 5 | $0.00012 | $0.00179 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
journal-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 6d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/mstack:journal-fit
Stage: submit Voice: editor
When to invoke
The manuscript is submission-ready (passed /mstack:results-audit, /mstack:coauthor-review, /mstack:referee-mock). You need to pick a journal — and a backup, and a backup's backup.
Procedure
-
Load. Abstract from
paper/sections/abstract.tex, contribution from.mstack/research-question.md, lit-positioning from.mstack/lit-map.md. -
Generate a candidate set of 6–8 journals. Mix of:
- Aspirational (top-tier general: APSR, AJPS, JOP / IO, BJPS).
- Aspirational field (top-tier IPE / IR / comparative).
- Realistic field (mid-tier with good fit).
- Specialty (a journal where the topic is core).
- Backup (publishes well, lower desk-reject risk).
-
For each candidate, score on a fixed grid. Use WebSearch / WebFetch to look up recent volumes if helpful (look for: published papers similar to the user's; word limits; review timelines).
Dimension Scale Notes Impact 1–5 Generalist / specialist matters; cite-count is a noisy proxy. Fit (recent papers in this conversation) 1–5 If the journal hasn't published in this conversation in 5 years, fit is low. Editor receptivity to method 1–5 Some journals reject quasi-experimental on principle; some prefer it. Word limit fit 1–5 Does the paper's actual length fit the journal's hard cap? Review turnaround 1–5 Median time from submission to first decision (use journal's posted data when available). Desk-reject risk 1–5 (5 = low risk) Editor's stated criteria for desk reject. Open-access / data policy note Does it require pre-acceptance OA? Restricted-data policy? -
Tier the top 3.
- Reach — highest impact among candidates with fit ≥ 3.
- Realistic — highest fit × low desk-reject risk × decent impact.
- Backup — high probability of acceptance; preserves time-to-publication.
-
Write the comparison table to
.mstack/journal-fit-<YYYY-MM-DD>.md.
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.
- 6d ago First seen · 72 lines · 58 tokens per session scan A 6bd95dd4a6ec
journal-fit is a skill published in the GitHub repository matthewdigiuseppe/MStack (14 stars, last pushed 10d ago), licensed MIT. It adds 58 tokens to every session and 893 once invoked, about $0.0003 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 2026-08-30.
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