journal-fit
Skill17 starsChanged 3 months agoMITView on GitHub
Scores 6-8 candidate journals drawn from the discipline's outlets by subfield on impact, fit with the paper's conversation, method receptivity, word limits, turnaround, replication and open-access policies, and desk-reject risk, then sets a reach/realistic/backup tier in config. Use when the user asks where to submit or which journal fits the paper.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- crfldrxyz/AuthorOps-Skillstheir own version, first seen Sep 1, 2026
- tansuasici/claude-research-kittheir own version, first seen Jun 2, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- tansuasici/claude-research-kit★ 4same name
- crfldrxyz/AuthorOps-Skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository38 other files it gives its agents.
- abstract-shotgunSkill
- analyzeSkill
- archiveSkill
- carefulSkill
- coauthor-reviewSkill
- codebookSkill
- cover-letterSkill
- data-acquireSkill
- data-cleanSkill
- design-researchSkill
- draft-sectionSkill
- freezeSkill
- guardSkill
- hypothesis-designSkill
- idea-shotgunSkill
- identification-reviewSkill
- learnSkill
- lit-mapSkill
- llm-checklistSkill
- mstack-initSkill
- mstack-upgradeSkill
- paper-statusSkill
- pdf-ingestSkill
- power-analysisSkill
- preregisterSkill
- r-and-rSkill
- referee-mockSkill
- research-questionSkill
- results-auditSkill
- retroSkill
- robustnessSkill
- scope-challengeSkill
- submission-packSkill
- survey-buildSkill
- theory-buildSkill
- title-shotgunSkill
- unfreezeSkill
- vizSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 17
When the manuscript has passed /mstack:results-audit, /mstack:coauthor-review, and /mstack:referee-mock.
- Distinctive: line 46
One-tier strategy: Always reach, realistic, backup.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
/mstack:journal-fit
Stage: submit · Voice: editor
When the manuscript has passed /mstack:results-audit, /mstack:coauthor-review, and /mstack:referee-mock. Pick a journal, a backup, and the backup's backup.
Procedure
- Load the abstract (
paper/sections/abstract.tex), the contribution (.mstack/research-question.md), the positioning (.mstack/lit-map.md, especially where the Foundation and Frontier papers were published), the design (.mstack/config.yaml→design.type), and${CLAUDE_PLUGIN_ROOT}/references/journals.md. - Candidate set of 6–8 from the reference, by subfield: aspirational general (APSR, AJPS, JOP, BJPS); aspirational field (IO, ISQ, World Politics, CPS as the subfield dictates); realistic field with good fit; a specialty journal where the topic is core; a short-format outlet if the paper is one clean result; a backup with low desk-reject risk.
Score each (WebSearch / WebFetch the journal's recent volumes for the Foundation and Frontier authors and for similar designs, and its author guidelines for limits and policies; never trust memory for caps and policies):
Dimension Scale Note Impact 1–5 Generalist vs. specialist matters; cite counts are a noisy proxy Fit 1–5 Nothing in this conversation in 5 years = low; where the lit map's papers appeared = high Method receptivity 1–5 Recent papers using this design; formal, experimental, and qualitative mixes Word-limit fit 1–5 Actual length vs. the hard cap, with the appendix policy Review turnaround 1–5 Median to first decision, from posted data when available Desk-reject risk 1–5 (5 = low) Editor's stated criteria; letters vs. articles Replication and OA policy note Verification before publication (AJPS), Dataverse deposit, pre-acceptance OA, restricted-data rules; each adds time to plan for - Tier the top 3: Reach (highest impact with fit ≥ 3), Realistic (highest fit × low desk-reject risk × decent impact), Backup (high acceptance probability; preserves time-to-publication).
- Write the comparison table to
.mstack/journal-fit-<YYYY-MM-DD>.mdand settarget_journalsin.mstack/config.yamlto the three tiers. - Recommend the submission journal in two sentences, tied to the abstract framing chosen in
/mstack:abstract-shotgunand to the replication timeline the journal's policy implies.
Outputs
.mstack/journal-fit-<date>.md;.mstack/config.yaml(target_journals).- Summary block: three tiers with one-line justifications, the recommended journal, and the policy step (replication verification, deposit) to start now.
Anti-patterns
- One-tier strategy. Always reach, realistic, backup; research is too long to bet on one journal.
- Impact-only ranking. 5/5 impact with 1/5 fit has zero expected value.
- Ignoring desk-reject risk. The user must know it going in.
- Remembered limits and policies. Fetch them.
Next
/mstack:submission-pack, then /mstack:cover-letter for the chosen journal. /mstack:referee-mock editor beforehand stress-tests fit.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.