A skill your agent uses when an author asks "which journal should I send this to?" or needs the best resubmission target after a reject.

MITAuto-check passed

Install Rt Journal Match

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-journal-match -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rt-journal-match --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Research-Toolkit-Skills/skills/rt-journal-match .claude/skills/rt-journal-match && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rt-journal-match
GitHub stars
1.2k
Token cost
~1.4k tokens
SKILL.md length
592 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when an author asks "which journal should I send this to?" or needs the best resubmission target after a reject.

  • Works in 5 steps: Profile the paper — discipline +… → Shortlist — run the matcher rather than… → Score each candidate on **Fit ×… → …
  • An author asks which journal should I send this to?
  • SKILL.md covers When to trigger, What it does, Hard rules and Output format, plus 1 more section
  • Calls python3

What it does

Rt Journal Match is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when an author asks "which journal should I send this to?" or needs the best resubmission target after a reject. Profiles the paper, shortlists candidates from an index of 743 venues with tools/matchvenues.py, and ranks them into reach / match / safe with a resubmission ladder. Reads live venue facts from each pack's source-map; defers fit judgment to the venue's own topic-selection skill.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的… The licence is MIT.

When your agent uses it

  • An author asks which journal should I send this to?
  • Needs the best resubmission target after a reject

Example prompts

  • “which journal should I send this to?”
  • “s source-map; defers fit judgment to the venue”
  • “/rt-journal-match”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Profile the paper — discipline + subfield, method/design, contribution type,
  2. Shortlist — run the matcher rather than reading the index by eye
  3. Score each candidate on **Fit × acceptance-odds × turnaround × cost/policy ×
  4. Return reach / match / safe (≈2–3 each) with one-line rationales + the live facts,
  5. Cost the ladder with rt-ladder-ev whenever the

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Rt Journal Match loads about 1.4k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 592 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~104
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 592 words, ~1,400 tokens.

Download SKILL.mdSave it as .claude/skills/rt-journal-match/SKILL.md (or your agent's skills folder).
name
rt-journal-match
description
Use when an author asks "which journal should I send this to?" or needs the best resubmission target after a reject. Profiles the paper, shortlists candidates from an index of 743 venues with tools/match_venues.py, and ranks them into reach / match / safe with a resubmission ladder. Reads live venue facts from each pack's source-map; defers fit judgment to the venue's own topic-selection skill.

Journal-Match (rt-journal-match)

The missing front-door question — which venue? — across the whole repository. Full methodology + the stable venue index live in shared-resources/journal-selection/journal-match.md and venue-index.tsv.

Worked end to end, with real output: worked-example.md.

When to trigger

  • The author has a result/draft and no settled target.
  • A paper was rejected and needs the best next venue.
  • A "not a fit" signal means the scope/venue needs rethinking.

What it does

  1. Profile the paper — discipline + subfield, method/design, contribution type, setting/data/region, ambition (be honest). Write it down once, in the shape of paper-profile.yml; every later skill reads the same file instead of re-deriving it.

  2. Shortlist — run the matcher rather than reading the index by eye:

    bash
    python3 tools/match_venues.py \
        --title "..." --abstract "..." \
        --discipline economics/labor --lane empirical --top 15

    --discipline is a prior, not a filter: the discipline and its adjacents (discipline-adjacency.tsv) are boosted, but a strong match elsewhere still surfaces, because Step 1 is a judgement that is sometimes wrong. Add --only-discipline when you are certain, --exclude <venue_id> for venues that have already rejected the paper, --json to pipe it. --list-disciplines prints the vocabulary.

    Every row names where to read more — source_map for a depth pack, profile_path for a breadth profile — and the terms it matched on, so a nonsense hit is visible as a nonsense hit.

    Read the warnings. The matcher flags weak evidence when its leading candidates each rest on one or two shared words — a ranking built on that is close to noise, because words the language reuses ("sensor", "generation", "network") will out-score a genuine subject match. It flags a coverage gap when nothing in the discipline you named scored at all: the prior can only re-rank venues that matched, never conjure one. Either warning means do not pass the list on as a shortlist — add the abstract, re-check the discipline label, or report that the subject area is thin in the index and route to rt-venue-integrity.

    The matcher is measured: R@10 = 41.5% from a bare title, on a held-out half of a 1,738-paper gold set (eval/RESULTS.md). That is a floor for one thin query, not the capability — it is why step 3 exists. The per-discipline table there is worth reading before trusting a result: coverage is uneven, and life sciences and natural science are visibly the thinnest.

  3. Score each candidate on Fit × acceptance-odds × turnaround × cost/policy × audience, reading the live facts from each candidate's resources/official-source-map.md. Never quote a fee, acceptance rate, turnaround or page limit from memory.

  4. Return reach / match / safe (≈2–3 each) with one-line rationales + the live facts, then a submit order and resubmission ladder — seed the ladder from ladder.tsv (candidate adjacency, not a ranking) and apply your own fit/odds judgement to it.

  5. Cost the ladder with rt-ladder-ev whenever the author is under a clock or is choosing between two orders. The sequence, not the venue, is what costs a year.

Show full SKILL.md (131 more words)Show less

Hard rules

  • Live facts from the source-map, never from memory (fees, acceptance, turnaround, page limits, data policy).
  • Fit judgment defers to the venue's *-topic-selection / *-contribution-framing.
  • Be honest about odds; don't inflate a paper into a reach it can't clear.
  • Coverage honesty: if a plausible venue is outside the index and its bundle, say so — and hand to rt-venue-integrity before the author submits somewhere unverified.
  • The matcher retrieves; you recommend. Never pass its ranking through as a shortlist: open the packs first.

Output format

【Paper profile】discipline / method / contribution / setting / ambition
【Reach】V — why; key live facts (desk-reject, turnaround, fee)
【Match】V — …
【Safe】V — …
【Submit order & ladder】V_top → if reject → V_next (what to change) → …
【Open questions】facts to re-verify in the source-map before submitting

Anti-patterns

  • Recommending only reaches (wastes the timeline) or only safes (undersells the paper).
  • Ignoring lane — sending a qualitative/theory paper to an empirical-only venue.
  • Treating the tier column as a precise ranking (it is an indicative bucket).
  • Reporting the matcher's top-10 as the answer. It is a reading list.

© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in Research-Toolkit-Skills/skills/rt-journal-match of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Rt Journal Match next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Rt Journal Match

What does Rt Journal Match do?

A skill your agent uses when an author asks "which journal should I send this to?" or needs the best resubmission target after a reject. Rt Journal Match is an agent skill from brycewang-stanford/Awesome-Journal-Skills." or needs the best resubmission target after a reject.

When should I use Rt Journal Match?

Rt Journal Match fits situations like: an author asks which journal should I send this to?; needs the best resubmission target after a reject.

How do I install Rt Journal Match in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-journal-match -a claude-code`. Or copy the skill folder (Research-Toolkit-Skills/skills/rt-journal-match in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/rt-journal-match in your project. Claude Code loads it when a task matches its description.

How do I install Rt Journal Match in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-journal-match -a codex`. Or copy the skill folder (Research-Toolkit-Skills/skills/rt-journal-match in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/rt-journal-match in your project. Codex loads it when a task matches its description.

Can I use Rt Journal Match in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-journal-match -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rt-journal-match, .gemini/skills/rt-journal-match, .github/skills/rt-journal-match and .opencode/skills/rt-journal-match in your project.

What does Rt Journal Match need to run?

Going by SKILL.md and its folder, Rt Journal Match needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Rt Journal Match access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Rt Journal Match safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Rt Journal Match use?

Rt Journal Match is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rt Journal Match use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Rt Journal Match?

Skills that share tags, products or a category with Rt Journal Match: Hermes Agent Skill Authoring (NousResearch/hermes-agent, 252k stars), Configuring Oauth2 Authorization Flow (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Authoring Skills (vercel/next.js, 143k stars) and Send (yc-software/qm, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rt Journal Match?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.