Sequences
vellum-ai/vellum-assistant
Create and manage automated email drip sequences. An agent skill from vellum-ai/vellum-assistant.
A skill your agent uses when choosing between submission sequences rather than between single venues — "should I try the top journal first, or start one rung down?", or when a tenure/job-market…
MITAuto-check passed
Install Rt Ladder Ev
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rt-ladder-ev --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-ladder-ev .claude/skills/rt-ladder-ev && rm -rf skills-srcUse ~/.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/
Install the "rt-ladder-ev" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Research-Toolkit-Skills/skills/rt-ladder-ev into .claude/skills/rt-ladder-ev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rt-ladder-ev", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Research-Toolkit-Skills/skills/rt-ladder-evType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rt-ladder-ev --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Research-Toolkit-Skills/skills/rt-ladder-ev .agents/skills/rt-ladder-ev && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rt-ladder-ev" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Research-Toolkit-Skills/skills/rt-ladder-ev into .agents/skills/rt-ladder-ev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rt-ladder-ev", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rt-ladder-ev --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Research-Toolkit-Skills/skills/rt-ladder-ev .cursor/skills/rt-ladder-ev && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "rt-ladder-ev" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Research-Toolkit-Skills/skills/rt-ladder-ev into .cursor/skills/rt-ladder-ev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rt-ladder-ev", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path Research-Toolkit-Skills/skills/rt-ladder-ev--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rt-ladder-ev --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Research-Toolkit-Skills/skills/rt-ladder-ev .gemini/skills/rt-ladder-ev && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "rt-ladder-ev" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Research-Toolkit-Skills/skills/rt-ladder-ev into .gemini/skills/rt-ladder-ev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rt-ladder-ev", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rt-ladder-evInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/Research-Toolkit-Skills/skills/rt-ladder-ev .github/skills/rt-ladder-ev && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "rt-ladder-ev" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Research-Toolkit-Skills/skills/rt-ladder-ev into .github/skills/rt-ladder-ev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rt-ladder-ev", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rt-ladder-ev --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Research-Toolkit-Skills/skills/rt-ladder-ev .opencode/skills/rt-ladder-ev && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "rt-ladder-ev" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Research-Toolkit-Skills/skills/rt-ladder-ev into .opencode/skills/rt-ladder-ev/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rt-ladder-ev", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
rt-ladder-ev- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 635 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when choosing between submission sequences rather than between single venues — "should I try the top journal first, or start one rung down?", or when a tenure/job-market…
- Works in 4 steps: **Turnaround and acceptance figures come… → Report the band, not the point. p_accept… → A ladder with no floor is not a plan. If… → …
- Choosing between submission sequences rather than between single venues — should I try the top journal first
- SKILL.md covers When to trigger, What it needs, What it does and Hard rules, plus 2 more sections
- Calls python3
What it does
Rt Ladder Ev is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when choosing between submission sequences rather than between single venues — "should I try the top journal first, or start one rung down?", or when a tenure/job-market clock makes time-to-print the binding constraint. Costs a resubmission ladder in months and in probability of ever placing, using each venue's own turnaround and desk-reject figures. Follows rt-journal-match, which produces the ladder this one prices.
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
- Choosing between submission sequences rather than between single venues — should I try the top journal first
- Start one rung down?
- A tenure/job-market clock makes time-to-print the binding constraint
Example prompts
- “should I try the top journal first, or start one rung down?”
- “/rt-ladder-ev”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- **Turnaround and acceptance figures come from the source map, read at the time of
- Report the band, not the point. p_accept is a judgement, so the tool prints a
- A ladder with no floor is not a plan. If the probability of exhausting the ladder
- Never present the output as a forecast. It is arithmetic over stated assumptions.
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 Ladder Ev loads about 1.4k tokens when it runs. Until then it costs about 110 tokens; SKILL.md has 635 words of instructions outside code blocks.
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). 635 words, ~1,360 tokens.
Download SKILL.mdSave it as
.claude/skills/rt-ladder-ev/SKILL.md (or your agent's skills folder).- name
- rt-ladder-ev
- description
- Use when choosing between submission sequences rather than between single venues — "should I try the top journal first, or start one rung down?", or when a tenure/job-market clock makes time-to-print the binding constraint. Costs a resubmission ladder in months and in probability of ever placing, using each venue's own turnaround and desk-reject figures. Follows rt-journal-match, which produces the ladder this one prices.
Ladder Expected Value (rt-ladder-ev)
rt-journal-match returns a shortlist and an order. This answers the question that
order implies but never states: what does that sequence cost?
Authors compare venues one at a time — is this one worth a shot? — and in isolation the answer is almost always yes. The cost only appears in the sequence, and it is a trade between months and placement probability that nothing else in this repository made visible, so it was settled by optimism.
The size of the trade is whatever your inputs say it is; the point of the tool is that you find out before spending the months rather than after. In the worked example, one reach rung costs about four and a half months and buys about four points of placement probability — and the sensitivity band shows the four points are not distinguishable from zero while the four months are. That shape of answer, rather than a winner, is the usual output.
When to trigger
- Two candidate submission orders and no principled way to choose.
- A clock: job market, tenure case, grant report, a co-author's graduation.
- A paper that has already been rejected twice and needs the remaining ladder costed.
- Someone asks "is it worth trying X first?" — that is a sequence question.
What it needs
The paper-profile.yml
(for ambition, constraints.clock, history) plus, for each rung, three numbers:
| Input | Where it comes from |
|---|---|
| months to first decision | the venue's resources/official-source-map.md — live-checked, never from memory |
| desk-reject / acceptance rate | same source map, same rule |
p_accept for this paper | your judgement, conditioned on the paper — see below |
p_accept is not the published acceptance rate. A venue's 6% is computed over a
submission pool that includes everything sent to it. A clean design with a
general-interest result is not a random draw from that pool, and neither is a thin one.
Start from the published rate, then move it with the venue's own
*-topic-selection fit judgement and rt-desk-reject-risk output, and say which way you
moved it and why.
What it does
python3 tools/ladder_ev.py \
--rung "Journal of Finance:0.05:4.5" \
--rung "Review of Financial Studies:0.08:5.0" \
--rung "JFQA:0.20:3.5" \
--rung "Journal of Banking and Finance:0.35:2.5"Walks the ladder top-down carrying the probability the paper is still unplaced, and returns: time until the ladder resolves, time to print conditional on placing, the probability of placing at all, and — the number that changes minds — the probability of running the ladder out and having nowhere left to go.
Then run the alternative sequence and compare. The comparison is the deliverable, not either number on its own.
Show full SKILL.md (232 more words)Show less
Hard rules
- Turnaround and acceptance figures come from the source map, read at the time of use. They are volatile; this skill stores none of them.
- Report the band, not the point.
p_acceptis a judgement, so the tool prints a ±40% sensitivity band by default. If two ladders' bands overlap, say they are indistinguishable — do not rank them anyway on the third decimal place. - A ladder with no floor is not a plan. If the probability of exhausting the ladder
exceeds ~25%, the shortlist is missing a credible home; go back to
rt-journal-matchfor a safe rung rather than reporting a number. - Never present the output as a forecast. It is arithmetic over stated assumptions. State the assumptions next to the answer.
Output format
【Ladder A】V1 → V2 → V3 resolves in N months · places P% · exhausts E%
【Ladder B】V2 → V3 resolves in N months · places P% · exhausts E%
【Difference】what B buys or costs vs A, in months and in placement probability
【Sensitivity】whether the difference survives the ±40% band
【Assumptions】each p_accept, and why it differs from the published rate
【Recommendation】which sequence, and the one fact that would change itAnti-patterns
- Using published acceptance rates as
p_accept— that is the pool's number, not the paper's. - Costing a ladder whose rungs were never checked for fit; a fast rung that will desk-
reject the paper on scope is not a rung. Run
rt-journal-matchfirst. - Optimising time-to-print alone. A worse-placed paper can cost more career-years than
the months it saved —
ambitionin the profile is what balances that, and it belongs in the write-up. - Reporting one decimal place of expected months as though it were measured.
Follows rt-journal-match (which builds the ladder) and
rt-venue-reframe (which prices the rewrite each rung
needs). Method: journal-match.md
step 6.
© 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-ladder-ev of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Rt Ladder Ev 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Rt Ladder Ev this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Sequencesvellum-ai/vellum-assistant | 1.4k | — | ~589 | Automated safety check: Pass | MIT | |
| Sequence Psychologistsickn33/agentic-awesome-skills | 47k | 2 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Seedance SequenceEmily2040/seedance-2.0 | 7.6k | 1 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Sequenceexon-research/genomi | 484 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Biopython Sequence Ioaipoch/medical-research-skills | 1.9k | — | ~2.1k | Automated safety check: Pass | MIT |
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Questions about Rt Ladder Ev
What does Rt Ladder Ev do?
A skill your agent uses when choosing between submission sequences rather than between single venues — "should I try the top journal first, or start one rung down?", or when a tenure/job-market…. Rt Ladder Ev is an agent skill from brycewang-stanford/Awesome-Journal-Skills.", or when a tenure/job-market clock makes time-to-print the binding constraint.
When should I use Rt Ladder Ev?
Rt Ladder Ev fits situations like: choosing between submission sequences rather than between single venues — should I try the top journal first; start one rung down?; A tenure/job-market clock makes time-to-print the binding constraint.
How do I install Rt Ladder Ev in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a claude-code`. Or copy the skill folder (Research-Toolkit-Skills/skills/rt-ladder-ev in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/rt-ladder-ev in your project. Claude Code loads it when a task matches its description.
How do I install Rt Ladder Ev in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rt-ladder-ev -a codex`. Or copy the skill folder (Research-Toolkit-Skills/skills/rt-ladder-ev in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/rt-ladder-ev in your project. Codex loads it when a task matches its description.
Can I use Rt Ladder Ev 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-ladder-ev -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-ladder-ev, .gemini/skills/rt-ladder-ev, .github/skills/rt-ladder-ev and .opencode/skills/rt-ladder-ev in your project.
What does Rt Ladder Ev need to run?
Going by SKILL.md and its folder, Rt Ladder Ev needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Rt Ladder Ev 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 Ladder Ev 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 Ladder Ev use?
Rt Ladder Ev 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 Ladder Ev use?
About 1.4k tokens (SKILL.md is roughly 5.4k 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 Ladder Ev?
Skills that share tags, products or a category with Rt Ladder Ev: Sequences (vellum-ai/vellum-assistant, 1.4k stars), Sequence Psychologist (sickn33/agentic-awesome-skills, 47k stars), Seedance Sequence (Emily2040/seedance-2.0, 7.6k stars) and Sequence (exon-research/genomi, 484 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Rt Ladder Ev?
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.