Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
A skill your agent uses when drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage review's first filter, covering how to answer…
Install Cav Author Response
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-author-response -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-author-response --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/CAV-Skills/skills/cav-author-response .claude/skills/cav-author-response && 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 "cav-author-response" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-author-response into .claude/skills/cav-author-response/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-author-response", 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/CAV-Skills/skills/cav-author-responseType 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 cav-author-response -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-author-response --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/CAV-Skills/skills/cav-author-response .agents/skills/cav-author-response && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cav-author-response" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-author-response into .agents/skills/cav-author-response/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-author-response", 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 cav-author-response -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-author-response --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/CAV-Skills/skills/cav-author-response .cursor/skills/cav-author-response && 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 "cav-author-response" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-author-response into .cursor/skills/cav-author-response/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-author-response", 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 CAV-Skills/skills/cav-author-response--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 cav-author-response -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-author-response --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/CAV-Skills/skills/cav-author-response .gemini/skills/cav-author-response && 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 "cav-author-response" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-author-response into .gemini/skills/cav-author-response/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-author-response", 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 cav-author-responseInstalls 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 cav-author-response -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/CAV-Skills/skills/cav-author-response .github/skills/cav-author-response && 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 "cav-author-response" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-author-response into .github/skills/cav-author-response/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-author-response", 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 cav-author-response -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 cav-author-response --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/CAV-Skills/skills/cav-author-response .opencode/skills/cav-author-response && 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 "cav-author-response" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-author-response into .opencode/skills/cav-author-response/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-author-response", 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
cav-author-response- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 487 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage review's first filter, covering how to answer…
- Drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage reviews first filter
- SKILL.md covers Triage, The verification rebuttal,…, Reviewer pushback patterns and Anonymity in the response…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering how to answer soundness/proof objections
What it does
Cav Author Response is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage review's first filter, covering how to answer soundness/proof objections, benchmark-fairness challenges, and novelty-delta doubts with verifiable evidence while preserving double-anonymity for Regular and Application papers.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science. 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
- Drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage reviews first filter
- Covering how to answer soundness/proof objections
- Benchmark-fairness challenges
- Novelty-delta doubts with verifiable evidence while preserving double-anonymity for Regular and Application papers
Example prompts
- “/cav-author-response”
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
No scripts in the folder and no shell commands in SKILL.md.
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
Cav Author Response loads about 1.2k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 487 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). 487 words, ~1,226 tokens.
Download SKILL.mdSave it as
.claude/skills/cav-author-response/SKILL.md (or your agent's skills folder).- name
- cav-author-response
- description
- Use when drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage review's first filter, covering how to answer soundness/proof objections, benchmark-fairness challenges, and novelty-delta doubts with verifiable evidence while preserving double-anonymity for Regular and Application papers.
CAV Author Response
Use this after CAV stage-2 reviews are released. At CAV the rebuttal exists only for papers that passed the stage-1 filter — the two-stage process means a rejected paper never reaches this turn. So the rebuttal is a focused instrument: answer what the two additional reviewers, and the two from stage 1, need in order to advocate for the paper in the PC discussion. For Regular and Application papers, the response must respect double-anonymity — do not reveal authors, the tool's real name, or identity-revealing repositories.
Triage
- Answer what affects the decision: soundness of the theorem/proof, fairness and reproducibility of the benchmarks, novelty/delta against prior verification work, scope, and clarity.
- Use evidence that already exists in the submission or that you can state precisely — a proof step, a number already in a table, a benchmark-configuration clarification. Do not promise unrun experiments as if they were results.
- Correct factual misreadings first; a reviewer who misread a theorem's assumption or a benchmark subset is often persuadable.
- Keep every word anonymous for anonymized categories. (Tool and Industrial papers are not double-blind, so this constraint relaxes — but still avoid gratuitous self-promotion.)
The verification rebuttal, point by point
Treat the response as a claim ledger: for each reviewer concern, either resolve it with concrete evidence or explain precisely why the concern does not apply.
[R1.1] "The soundness proof assumes X, which fails for unbounded inputs."
-> Response: X is not assumed; Lemma 2 holds for unbounded inputs (the bound is only on
the encoding width, §3.2). Pointer: §3.2, Lemma 2.
[R2.1] "The baseline solver was not the latest version / not tuned."
-> Response: baseline is vA.B (latest release at submission); we used its default portfolio
as recommended in its README; per-instance data in the artifact confirms parity of limits.
[R2.2] "Novelty over <prior technique> is unclear."
-> Response: prior technique shares lemmas only propositionally; ours admits theory lemmas
under a re-derivation check (the soundness contribution), see §3.3 and Table 2.The rule that turns a stage-2 paper into an acceptance: answer the axis the reviewer raised, with something they can verify — a section pointer, a proof step, or a benchmark fact — not a promise.
Show full SKILL.md (233 more words)Show less
Reviewer pushback patterns
| Pushback | What it signals | CAV-ready response |
|---|---|---|
| "The soundness argument is incomplete" | Correctness doubt | Point to the exact lemma/assumption, or concede and scope the claim |
| "The baseline is outdated or untuned" | Evaluation-fairness doubt | Name the version and configuration; show equal resource limits from the artifact |
| "Only easy/self-selected benchmarks" | External-validity limit | Point to the standard set/revision used; state the class not covered as a limit |
| "Delta over prior work X is thin" | Novelty doubt | Name the precise technical difference (what X cannot do that you do) |
| "The tool did not build / is missing" | Reproducibility gap | Clarify the build path; note the artifact plan (AEC is post-acceptance) |
| "Claim generality is over-stated" | Scope objection | Narrow the claim in the response and promise the camera-ready scoping edit |
Anonymity in the response (anonymized categories)
- Refer to your own prior work in the third person, as in the paper.
- Describe tool/benchmark changes without naming the real tool or linking an identity-revealing repository; use the anonymized location.
- Do not thank a named collaborator or funder inside the response.
Calibration
- Respond to the criterion the reviewer actually raised, not the one you would rather defend.
- Length and format norms for the response vary by cycle; confirm the current instructions and the word/character limit before sending.
- The PC discussion decides; write the response as evidence for an advocate, and let the paper — not the rebuttal — carry the argument.
Output format
[Turn] stage-2 rebuttal (only for papers past the stage-1 filter)
[Priority issue] <reviewer concern>
[Decision dimension] soundness/proof / benchmark-fairness / novelty / scope / clarity / tool
[Claim ledger] <concern -> resolved with (proof step / number / pointer) or scoped>
[Anonymity check] <no identity leak for Regular/Application: passed/issues>© 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 CAV-Skills/skills/cav-author-response of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cav Author Response 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 |
|---|---|---|---|---|---|---|
| Cav Author Response this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Last30daysmvanhorn/last30days-skill | 64k | — | ~7.9k | Automated safety check: Notes | MIT |
Similar skills
GitHub Deep Research
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
Content Research Writer
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
Last30days
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
Peer Review
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
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Categories
Questions about Cav Author Response
What does Cav Author Response do?
A skill your agent uses when drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage review's first filter, covering how to answer…. Cav Author Response is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage review's first filter, covering how to answer soundness/proof objections, benchmark-fairness challenges, and novelty-delta doubts with verifiable evidence while preserving double-anonymity for Regular and Application papers.
When should I use Cav Author Response?
Cav Author Response fits situations like: drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage reviews first filter; covering how to answer soundness/proof objections; benchmark-fairness challenges; novelty-delta doubts with verifiable evidence while preserving double-anonymity for Regular and Application papers.
How do I install Cav Author Response in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-author-response -a claude-code`. Or copy the skill folder (CAV-Skills/skills/cav-author-response in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cav-author-response in your project. Claude Code loads it when a task matches its description.
How do I install Cav Author Response in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-author-response -a codex`. Or copy the skill folder (CAV-Skills/skills/cav-author-response in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cav-author-response in your project. Codex loads it when a task matches its description.
Can I use Cav Author Response 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 cav-author-response -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cav-author-response, .gemini/skills/cav-author-response, .github/skills/cav-author-response and .opencode/skills/cav-author-response in your project.
What does Cav Author Response need to run?
SKILL.md names no scripts, command-line tools or credentials: Cav Author Response is instructions for the agent only.
Does Cav Author Response 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 Cav Author Response 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 Cav Author Response use?
Cav Author Response 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 Cav Author Response use?
About 1.2k tokens (SKILL.md is roughly 4.9k 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 Cav Author Response?
Skills that share tags, products or a category with Cav Author Response: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cav Author Response?
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.