| name | challenger-review |
| description | Stress-test the primary review with an additional independent reviewer that generates its own findings,
compares reviewer conclusions, and identifies issues the primary reviewer may have missed.
Use when performing a second opinion or double-check review on a pull request, for medium or high risk PRs,
when authoring was heavily AI-assisted, when primary reviewer confidence is low, when findings conflict,
or when you need to verify findings with a cross-model or same-model challenger.
Supports same-model and cross-model configurations for fair comparison.
|
Challenger Review
An additional independent review pass that strengthens or weakens candidate findings.
When to use
- Change is medium or high risk (yellow/red lane)
- Authoring was heavily AI-assisted
- Primary reviewer confidence is low
- Findings conflict or remain uncertain
- Configured to run automatically via
enable_challenger_on setting
Key requirement
This skill must be usable in two configurations:
- Same-model challenger: same model family as primary reviewer, fresh context
- Cross-model challenger: different model family from primary reviewer
This allows apples-to-apples comparison between intra-model and cross-model review.
Inputs
Do NOT read primary reviewer conclusions. The challenger always reviews independently.
Steps
-
Verify independence before starting. Confirm you have not read any files from the primary review output directory. Do not access findings from fresh-eyes-review. If any primary review content is in context, stop and restart with a clean context.
-
Review the evidence pack and raw diff independently. Approach the code as a separate critic with no knowledge of prior findings.
-
Produce candidate findings. Same output format as fresh-eyes-review. Each finding must meet the evidence threshold. If a finding does not meet the threshold, discard it rather than downgrading its severity.
-
For each finding, indicate whether it:
- Confirms a likely issue (new independent evidence for something that may also appear in primary review)
- Refutes a possible concern (evidence that something is actually safe)
- Adds a novel finding (something the primary reviewer may have missed)
Output shape per finding
Same schema as fresh-eyes-review, with source set to "challenger". Example:
{
"source": "challenger",
"file": "src/api/handlers.ts",
"line": 42,
"severity": "high",
"classification": "confirms",
"title": "Unbounded query size allows denial-of-service",
"evidence": "req.body.ids is passed directly to the IN clause with no length check — an array of 100k IDs will lock the table.",
"recommendation": "Add a maximum length check (e.g., 100 items) before constructing the query."
}
Classification values:
"confirms" — independent evidence supporting an issue that may also appear in the primary review"refutes" — evidence that a suspected concern is actually safe"novel" — finding not likely surfaced by the primary reviewer
Success criteria
- Improves precision or novelty, not just volume
- Reduces shared blind spots between review passes
