// Run vet immediately after ANY logical unit of code changes. Do not batch your changes, do not wait to be asked to run vet, make sure you are proactive.
| name | vet |
| description | Run vet immediately after ANY logical unit of code changes. Do not batch your changes, do not wait to be asked to run vet, make sure you are proactive. |
Run vet immediately after ANY logical unit of code changes. Do not batch changes, do not wait to be asked to run vet, make sure you are proactive.
Vet reviews git diffs and conversation history to find issues in code changes and conversation history. It is most effective when run frequently with conversation history, which helps it catch misunderstandings between what was requested and what was implemented. Despite this, vet is not a replacement for running tests.
Install vet using pip, pipx, or uv:
# Using pip
pip install verify-everything
# Using pipx
pipx install verify-everything
# Using uv
uv tool install verify-everything
# Verify installation:
vet --help
Before running vet, determine the correct Python binary:
$(command -v python3 || command -v python)
Use whichever resolves (prefer python3). The examples below use python3, substitute python if that is what your system provides.
OpenCode:
vet "goal" --history-loader "python3 ~/.agents/skills/vet/scripts/export_opencode_session.py --session-id <ses_ID>"
Codex:
vet "goal" --history-loader "python3 ~/.codex/skills/vet/scripts/export_codex_session.py --session-file <path-to-session.jsonl>"
Claude Code:
vet "goal" --history-loader "python3 ~/.claude/skills/vet/scripts/export_claude_code_session.py --session-file <path-to-session.jsonl>"
Gemini CLI:
vet "goal" --history-loader "python3 ~/.gemini/skills/vet/scripts/export_gemini_cli_session.py --session-file <path-to-session.json>"
Without Conversation History
vet "goal"
You should only search for sessions from your coding harness. If a user requests you use a different harness, they are likely referring to vet's agentic mode, not the session.
OpenCode: The --session-id argument requires a ses_... session ID. To find the current session ID:
opencode session list --format json to list recent sessions with their IDs and titles.--session-id.Codex: Session files are stored in ~/.codex/sessions/YYYY/MM/DD/. To find the correct session file:
grep -rl "UNIQUE_MESSAGE" ~/.codex/sessions/ to find the matching session file.
--session-file.Claude Code: Your current session UUID is ${CLAUDE_SESSION_ID}. Session files are stored in ~/.claude/projects/<encoded-path>/ as <session-uuid>.jsonl. Find the session file matching your UUID and verify it belongs to this conversation. If the UUID above was not replaced with an actual value (e.g. older Claude Code versions), fall back to a manual search:
grep -rl "UNIQUE_MESSAGE" ~/.claude/projects/ to find the matching session file.
--session-file.Gemini CLI: Session files are stored in ~/.gemini/tmp/<project-name>/chats/. To find the correct session file:
grep -rl "UNIQUE_MESSAGE" ~/.gemini/tmp/ to find the matching session file.
--session-file.NOTE: The examples in the standard usage section assume the user installed the vet skill at the user level, not the project level. Prior to trying to run vet, check if it was installed at the project level which should take precedence over the user level. If it is installed at the project level, ensure the history-loader option points to the correct location.
Vet analyzes the full git diff from the base commit. This may include changes from other agents or sessions working in the same repository. If vet reports issues that relate to changes you did not make in this session, disregard them, assuming they belong to another agent or the user.
--base-commit REF: Git ref for diff base (default: HEAD)--model MODEL: LLM to use (default: claude-opus-4-8)--list-models: list all models that are supported by vet
vet --help and look at the vet repo's readme for details about defining custom OpenAI-compatible models.--update-models: fetch the latest community model definitions from the remote registry and cache them locally. See "Updating the Model Registry" below for when to run this.--confidence-threshold N: Minimum confidence 0.0-1.0 (default: 0.8)--output-format FORMAT: Output as text, json, or github--quiet: Suppress status messages and 'No issues found.'--agentic: Mode that routes analysis through the locally installed Claude Code, Codex, or OpenCode CLI instead of calling the API directly. Try this if vet fails due to missing API keys. This is slower so it is not the default, but it often results in higher precision issue identification. --model is forwarded to the harness but not validated by vet, as vet doesn't know which models each harness supports.--agent-harness: The three options for this are codex, claude, and opencode. Claude Code is the default.--help: Show comprehensive list of optionsThe vet CLI, skill files, and export scripts can become outdated as agent harnesses and LLM APIs change.
If this happens, try updating them. Run which vet to determine how vet was installed and update accordingly. For the skill files, check which skill directories exist on disk and update them with the latest versions from https://github.com/imbue-ai/vet/tree/main/skills/vet.
Run vet --update-models to fetch the latest community model definitions from the remote registry without upgrading vet itself. This caches model definitions locally so they appear in --list-models and can be used with --model.
You should run vet --update-models when:
vet --list-models does not show a model you or the user expects to be available.Additional information can be found in the vet repo: