Performance Review

Performance Review

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.

Quick Start

/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}")

When To Use

• Pre-merge review of code that runs on user-scaled inputs.
• Triage of a function that "feels slow" before reaching for a profiler.
• Audit a refactor for newly introduced O(n²) patterns.
• Guardrail for AI-generated code where nested-loop hot spots are common.

When NOT to Use

• The target needs runtime measurement (memory profile, CPU time on real data). Use Skill(parseltongue:python-performance) instead: that skill drives cProfile, py-spy, and benchmarks.
• General refactoring guidance not focused on hotspots: use Skill(pensive:code-refinement) whose algorithm-efficiency module covers broader optimization patterns. This skill detects; that skill teaches.
• Deciding whether a hand-rolled loop transform (unrolling, manual SIMD, strength reduction) is worth keeping: use Skill(leyline:loop-optimization) for the hand-vs-compiler rule. This skill flags hotspot shapes, not transformation choices.
• Architecture-level performance (sharding, caching layers, queue placement): use Skill(pensive:architecture-review).

Required TodoWrite Items

1. perf-review:context-established
2. perf-review:scan-complete
3. perf-review:findings-categorized
4. perf-review:integration-checked
5. perf-review:report-generated
6. perf-review:findings-verified

Workflow

Step 1: Context (perf-review:context-established)

• Identify target files. If invoked with no argument, use git diff --name-only. If invoked with a path, scope to that.
• Note language(s) involved. Tier 1 covers Python; non-Python files need gauntlet for Tier 2 coverage.

Step 2: Tier 1 AST scan (perf-review:scan-complete)

Load modules/time-complexity.md for the time-side patterns and modules/space-complexity.md for space-side. Each module documents the AST shape of every detector.

Alongside the automated scan, load modules/memory-allocation-lenses.md and apply its three manual lenses (unbounded external-source collections, hot-path recompute, serial blocking I/O) by reading the target files.

For each Python target file, call:

from pensive.skills.performance_review import PerformanceReviewSkill

result = PerformanceReviewSkill().analyze(context, path)

The visitor walks the AST once and emits ReviewFinding records.

Step 3: Categorize and rank (perf-review:findings-categorized)

Group findings by severity:

• HIGH: O(n²) or worse on input-sized iterables (T1, T2).
• MEDIUM: Unbounded allocation or per-iteration overhead (T3, T4, S1, S3).
• LOW: Style-level inefficiencies (T5, T6, S2).
• CRITICAL: Reserved for Tier-3 transitive upgrades.

Within a severity, sort by file then line. Suppress findings the user has explicitly marked acceptable (TODO/comment markers) at module-load time of the target.

Step 4: Tier 2/3 enrichment (perf-review:integration-checked)

Load modules/gauntlet-integration.md for the contract.

If gauntlet is installed, run Tier 2 on non-Python files that were skipped at Step 2. If a .gauntlet/graph.db exists in the working tree, run Tier 3 to upgrade severities based on transitive hotspot reachability.

If gauntlet is missing, this step is a no-op and the report notes "Tier 2/3 not available: install gauntlet for multi-language and call-chain coverage."

Step 5: Report (perf-review:report-generated)

Emit a markdown report:

## Performance Review: <target>

### HIGH (<count>)
- src/foo.py:42: Nested loop over the same iterable 'items'.
  Suggestion: sort + two pointers, or hash-set membership.

### MEDIUM (<count>)
- ...

### LOW (<count>)
- ...

Tier coverage: 1 (always) | 2 (gauntlet ✓/✗) | 3 (graph ✓/✗)

The report is informational. Apply fixes via Skill(pensive:code-refinement) or hand-merge.

Tiered Analysis

Output Format

Findings use the shared ReviewFinding dataclass from pensive.skills.base:

ReviewFinding(
    file="src/module.py",
    line=42,
    severity="HIGH",  # LOW | MEDIUM | HIGH | CRITICAL
    category="time",  # time | space
    message="Nested loop over the same iterable 'items'.",
    suggestion="Sort + two pointers, or hash-set membership.",
    anchor="verbatim source text at file:line",
    code_snippet="",
)

This shape matches every other pensive review skill, so the findings can flow into Skill(pensive:unified-review) without translation.

Cross-Plugin Dependencies

The optional-import contract follows the precedent in plugins/leyline/src/leyline/tokens.py:25-32: try-import to a module-level sentinel, then early-return on None inside each tier helper. plugins/gauntlet/hooks/precommit_gate.py:35-40 is the boolean-flag variant of the same shape. See modules/gauntlet-integration.md for the exact code shape.

Supporting Modules

• modules/time-complexity.md: T1-T6 detector patterns and AST shapes.
• modules/space-complexity.md: S1-S3 detector patterns.
• modules/gauntlet-integration.md: Tier 2/3 contract, fallback semantics, examples.
• modules/kuva-visualization.md: Rendering benchmark data as charts with kuva (criterion, pytest-benchmark, ad-hoc tables). Covers when chart evidence satisfies proof-of-work requirements.
• modules/memory-allocation-lenses.md: Manual review lenses (not AST detectors) for unbounded collections fed from external sources, hot-path recompute that should be memoized, and serial blocking I/O over unbounded sets. Apply by reading the code; the detector-test rule in Testing does not cover these because nothing is automated.