Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
A skill your agent uses when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then…
Install Cav Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-review-process --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-review-process .claude/skills/cav-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-review-process into .claude/skills/cav-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-review-process", 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-review-processType 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-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-review-process --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-review-process .agents/skills/cav-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-review-process into .agents/skills/cav-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-review-process", 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-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-review-process --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-review-process .cursor/skills/cav-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-review-process into .cursor/skills/cav-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-review-process", 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-review-process--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-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-review-process --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-review-process .gemini/skills/cav-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-review-process into .gemini/skills/cav-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-review-process", 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-review-processInstalls 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-review-process -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-review-process .github/skills/cav-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-review-process into .github/skills/cav-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-review-process", 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-review-process -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-review-process --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-review-process .opencode/skills/cav-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-review-process into .opencode/skills/cav-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-review-process", 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-review-process- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 705 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then…
- Reasoning about how a CAV (Computer Aided Verification) submission is evaluated
- SKILL.md covers Process model, Reading a decision against the…, How CAV differs from its… and Who reads you, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the two-stage reviewing process (two reviews then an early-reject filter
What it does
Cav Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then two more reviews with a rebuttal), the partial double-anonymity by category, the accept/reject outcome, the optional non-conditional artifact evaluation, and how CAV differs from TACAS and FMCAD.
Its SKILL.md is about 1.5k 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
- Reasoning about how a CAV (Computer Aided Verification) submission is evaluated
- Covering the two-stage reviewing process (two reviews then an early-reject filter
- Then two more reviews with a rebuttal)
- The partial double-anonymity by category
Example prompts
- “/cav-review-process”
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 Review Process loads about 1.5k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 705 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). 705 words, ~1,541 tokens.
Download SKILL.mdSave it as
.claude/skills/cav-review-process/SKILL.md (or your agent's skills folder).- name
- cav-review-process
- description
- Use when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then two more reviews with a rebuttal), the partial double-anonymity by category, the accept/reject outcome, the optional non-conditional artifact evaluation, and how CAV differs from TACAS and FMCAD.
CAV Review Process
Model the pipeline before interpreting any single review. CAV's process is a two-stage filter: a paper must survive the first two reviews before it reaches a rebuttal and the second pair of reviews. The most consequential mental shift for authors arriving from a single-round rebuttal conference is that a paper can be rejected before the rebuttal ever happens — so the first read has to stand on its own.
Process model
- Submission and review run on the CAV portal (EasyChair or HotCRP — verify the live link) with partial double-anonymity: Regular and Application papers are anonymized; Short Tool and Industrial Experience papers are not.
- Stage 1: each paper receives two reviews. Papers with sufficient support proceed; the rest are rejected early (CAV 2026 tentative first-round outcome ~4 Mar 2026).
- Stage 2: surviving papers receive two additional reviews and an author-response (rebuttal) window (CAV 2026: 30 Mar - 2 Apr 2026).
- Outcome: accept or reject (CAV 2026 notification 17 Apr 2026). Accepted papers publish open access in Springer LNCS, and authors may then submit an artifact to the AEC on its own deadline.
- Reviewers weigh the significance of the verification contribution, the soundness of the technique and its proofs, the fairness and reproducibility of the benchmark evaluation, the novelty/delta against prior verification work, and clarity — and see the artifact-intent declaration.
Reading a decision against the stages
| Signal | What it means | Author move |
|---|---|---|
| Early reject after stage 1 | Two reviewers saw a fatal gap (unsound claim, weak evaluation, thin delta) | No rebuttal exists; reframe or reroute (TACAS/FMCAD/VMCAI) — do not resubmit unchanged |
| Passed to stage 2 | The contribution is plausible; specific concerns remain | Use the rebuttal to fix factual misreadings and supply the missing number/proof detail |
| Accept | Contribution, soundness, and evidence hold | Camera-ready + optional artifact; do not reopen scope |
| Reject after stage 2 | A concern the rebuttal did not resolve | Address it substantively before any resubmission |
The strategic reading: write the submission so its soundness and headline benchmark result are legible in the first two reviews. A contribution that only convinces after the rebuttal may never reach the rebuttal.
How CAV differs from its siblings
- vs. TACAS: TACAS (at ETAPS) overlaps heavily in scope and also values tools, but is a distinct venue with its own calendar and process; never assume they share a deadline or a review model. CAV's identity is the flagship LNCS proceedings and the two-stage filter.
- vs. FMCAD: FMCAD centers formal methods in hardware/design; its review culture and page model differ. A hardware-methodology paper may be read as more native there.
- vs. a single-round rebuttal conference: the early-reject stage is the key difference — plan for
it (
cav-workflow).
Show full SKILL.md (267 more words)Show less
Who reads you
Expect verification-literate reviewers matched to your subarea (model checking, SMT, theorem proving, program analysis, hardware or NN verification). They check whether the theorem actually holds, whether the baselines and benchmarks are fair and pinned, whether the claim is scoped to what was proved and measured, and — for tool papers — whether the tool is real and usable. Vague algorithm descriptions and unpinned benchmarks get caught, not skimmed.
Where author leverage actually exists
[Before submission] category + topic tags -> reviewer pool and page/anonymity rules (largest lever)
[Stage 1] nothing to do but wait; the paper must defend itself
[Rebuttal (stage 2)] correct factual misreadings; supply a missing number, proof detail, or
benchmark clarification the reviewers can verify
[After reject] no appeal; reroute to a sibling flagship or a journal (FMSD/JAR)A rebuttal moves borderline papers when it corrects a misreading of a theorem or supplies a benchmark clarification a reviewer said was missing; it does not move papers when it argues taste or promises unrun experiments.
Reading a review packet
Weight reviews before answering. A review that engages your theorem statement, checks your assumptions, or questions a specific benchmark was read closely and will be read closely again — its author is your likely advocate if the rebuttal holds. A review that only questions novelty has left soundness and evaluation to the others; answer each reviewer on the axis they raised. Reviewers often end with explicit questions; the rebuttal is scored heavily on whether each got a direct, verifiable answer.
Misreadings to avoid
- Treating stage 1 as a formality — the early-reject filter is real; the first two reviews decide whether a rebuttal ever happens.
- Treating the rebuttal as a debate — the PC decides; your response is evidence for an advocate, not a closing argument.
- Assuming artifact evaluation gates acceptance — it is optional and non-conditional at CAV.
- Projecting a sibling's process — TACAS and FMCAD have their own models; do not carry them over.
Output format
[Process stage] pre-submission / stage-1 / rebuttal / final / accepted / artifact
[Outcome so far] early-reject / passed to stage 2 / accept / reject, with the driving criterion
[Criterion map] each review point -> significance | soundness/proof | evaluation | novelty | clarity
[Leverage plan] the next-stage action that can actually change the outcome
[Forbidden moves] identity leak (anonymized categories) / unsupported new claims / unrun promises© 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-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cav Review Process 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 Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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
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Structured manuscript/grant review with checklist-based evaluation.
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Categories
Questions about Cav Review Process
What does Cav Review Process do?
A skill your agent uses when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then…. Cav Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then two more reviews with a rebuttal), the partial double-anonymity by category, the accept/reject outcome, the optional non-conditional artifact evaluation, and how CAV differs from TACAS and FMCAD.
When should I use Cav Review Process?
Cav Review Process fits situations like: reasoning about how a CAV (Computer Aided Verification) submission is evaluated; covering the two-stage reviewing process (two reviews then an early-reject filter; then two more reviews with a rebuttal); the partial double-anonymity by category.
How do I install Cav Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-review-process -a claude-code`. Or copy the skill folder (CAV-Skills/skills/cav-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cav-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Cav Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-review-process -a codex`. Or copy the skill folder (CAV-Skills/skills/cav-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cav-review-process in your project. Codex loads it when a task matches its description.
Can I use Cav Review Process 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-review-process -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-review-process, .gemini/skills/cav-review-process, .github/skills/cav-review-process and .opencode/skills/cav-review-process in your project.
What does Cav Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Cav Review Process is instructions for the agent only.
Does Cav Review Process 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 Review Process 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 Review Process use?
Cav Review Process 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 Review Process use?
About 1.5k tokens (SKILL.md is roughly 6.2k 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 Review Process?
Skills that share tags, products or a category with Cav Review Process: 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 Review Process?
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.