From codeant
Run a CodeAnt AI code review on local changes (uncommitted, staged, last commit, or against a branch), present findings, and apply safe minimal fixes for all issues
How this skill is triggered — by the user, by Claude, or both
Slash command
/codeant:codeant-reviewThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Run a CodeAnt AI code review on local changes, present all findings clearly, and apply safe minimal fixes for every issue found.
Run a CodeAnt AI code review on local changes, present all findings clearly, and apply safe minimal fixes for every issue found.
Choose the scope based on what the user says:
| User says | Flag to use |
|---|---|
| "staged" or "staged files" | --staged |
| "uncommitted" or "my changes" or "wip" | --uncommitted |
| "last commit" | --last-commit |
| "last N commits" (where N ≤ 5) | --last-n-commits <N> |
| "committed" or "unpushed" | --committed |
| a specific branch name (e.g., "against main") | --base <branch> |
| a specific commit hash | --base-commit <hash> |
| "everything" or "all changes" | --all |
| nothing specific | --uncommitted (default) |
Report that this skill was invoked:
codeant track --event "skill_invoked" --props '{"skill_name": "codeant-review", "source": "claude-code", "scope": "<scope-flag>"}'
codeant review <scope-flag>
Let the user know the review is running — it may take 30–90 seconds depending on the size of the diff.
If the review returns zero issues, tell the user: "CodeAnt found no issues in your changes." and stop. Do not invent problems.
The review output contains code suggestions. Each suggestion has:
| Field | Description |
|---|---|
issue_content | Description of what's wrong |
relevant_file | File path where the issue is |
start_line | Line number where the issue starts |
label | Category: Code Quality, Security, Performance, Maintainability, etc. |
For each issue (grouped by file to minimize re-reading), do the following:
start_line, with 30 lines above and 30 lines below for full context.issue_content carefully. Identify:
For each issue, run through these checks:
Check that the code at the reported line matches the issue description. The code may have changed (e.g., by a previous fix in this session). If it no longer matches, mark as STALE.
Verify the fix is syntactically valid in context:
Verify the fix does not break logic:
Based on the validation, assign one of these verdicts to every issue:
ACCEPT — Safe to apply. Assign this when ALL of these are true:
LIKELY ACCEPT — Looks correct, but verify the callers. Assign this when:
DO NOT ACCEPT — This could break things. Assign this when ANY of these are true:
STALE — Code has changed. Assign this when:
Before making any changes, present a clear summary to the user:
Then list every issue grouped by verdict:
ACCEPT — Safe to apply (N): For each, show:
LIKELY ACCEPT — Verify callers (N): For each, show:
DO NOT ACCEPT — Could break logic (N): For each, show:
STALE — Code changed (N): For each, show:
Highlight Security issues at the top of each verdict group — they deserve immediate attention.
Then ask the user: "I will apply the N ACCEPT fixes now. For the LIKELY ACCEPT fixes, I recommend you review the callers first — want me to apply those too, or skip them for now?"
After the user confirms:
After applying fixes, report the outcome:
codeant track --event "suggestions_applied" --props '{"skill_name": "codeant-review", "source": "claude-code", "scope": "<scope-flag>", "accept_count": <N>, "likely_accept_count": <N>, "do_not_accept_count": <N>, "stale_count": <N>, "total_issues": <N>}'
Use the actual counts from the verdicts assigned in Step 4. For likely_accept_count, only count ones the user chose to apply.
After all fixes are applied, run the review again with the same scope:
codeant review <same-scope-flag>
This confirms:
Initial review:
Applied (N issues):
Not applied — DO NOT ACCEPT (N issues):
Not applied — STALE (N issues):
Verification:
npx claudepluginhub codeant-ai/skills --plugin codeantProvides CDSS development patterns for drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), and alert classification integrated into EMR workflows.