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Detects time and space complexity hotspots via AST scan.
Passed 17 of 20 test cases with the skill, and 6 of 20 without it.
and 15 more in the full report
13 fixed7 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before outputting findings to the final report, what external script and command arguments must be executed to verify finding citations against the repo root?
| Checked | Without skill | With skill |
|---|---|---|
| Run python plugins/imbue/scripts/citation_verifier.py --findings .review/findings.json --repo-root . | Missed | Passed |
The instructions your model reads when the skill loads.
Static-analysis review of time and space complexity hotspots.
The skill runs in three escalating tiers. Tier 1 uses Python's
stdlib ast and always runs. Tier 2 uses gauntlet's tree-sitter
parser to extend detection across languages when gauntlet is
installed. Tier 3 uses the gauntlet code graph to upgrade
severity when hotspots reach other hotspots transitively. If
gauntlet is missing, Tiers 2 and 3 no-op and Tier 1 still
produces useful findings on Python source.
/performance-review # scan changed files
/performance-review path/to/file.py # scan one file
/performance-review --tier 1 # force Tier 1 only
Programmatic use:
from pensive.skills.performance_review import PerformanceReviewSkill
skill = PerformanceReviewSkill()
result = skill.analyze(context, "src/module.py")
for f in result.issues:
print(f"[{f.severity}] {f.file}:{f.line} {f.message}")
test cases passed, vs 6 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.