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performance-review

Detects time and space complexity hotspots via AST scan Skill: performance-review Owner: athola Summary: Detects time and space complexity hotspots via AST scan Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:19:06.422Z | user Release v1.9.19 v1.9.17 | 2026-07-30T05:39:18.228Z | user Release v1.9.17 v1.9.16 | 2026-07-14T19:56:01.066Z | user Release v1.9.16 v1.9.14 | 2026-06-30T18:04:19.616Z | user Release v1.9.14 v1.9.13 | 2026-06-27T16:22:19.651Z | user Rele

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Downloads

1.0k

Updated

Oct 11, 2026

Version

1.9.19

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About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

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Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.9.19release · observed Aug 26, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17emme0e2m3cpf7k2jvp3a84984b8z9:nm-pensive-performance-review

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

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$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

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144,315 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: performance-review
description: Detects time and space complexity hotspots via AST scan
version: 1.9.8
triggers:
  - performance
  - complexity
  - algorithms
  - ast
  - static-analysis
  - code feels slow
  - before performance-sensitive merges
  - or to find O(n²) regressions
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/pensive", "emoji": "\ud83e\udd9e", "requires": {"config": ["night-market.pensive:shared"]}}}
source: claude-night-market
source_plugin: pensive
---

> **Night Market Skill** — ported from [claude-night-market/pensive](https://github.com/athola/claude-night-market/tree/master/plugins/pensive). For the full experience with agents, hooks, and commands, install the Claude Code plugin.


## Table of Contents

- [Quick Start](#quick-start)
- [When to Use](#when-to-use)
- [When NOT to Use](#when-not-to-use)
- [Required TodoWrite Items](#required-todowrite-items)
- [Workflow](#workflow)
- [Tiered Analysis](#tiered-analysis)
- [Output Format](#output-format)
- [Cross-Plugin Dependencies](#cross-plugin-dependencies)
- [Supporting Modules](#supporting-modules)

# 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

```bash
/performance-review                  # scan changed files
/performance-review path/to/file.py  # scan one file
/performance-review --tier 1         # force Tier 1 only
```

Programmatic use:

```python
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.
- Architecture-level performance (sharding, caching layers,
  queue placement): use `Skill(pensive:architecture-review)`.

## Required TodoWrite Items

1. `perf-review:con

_meta.json

{
  "ownerId": "kn7d107jg9jv602h9ytsegydq184a42s",
  "slug": "nm-pensive-performance-review",
  "version": "1.9.19",
  "publishedAt": 1787750346422
}

modules/gauntlet-integration.md

---
module: gauntlet-integration
description: Tier 2/3 contract via gauntlet tree-sitter and graph
parent_skill: performance-review
category: integration
tags:
- gauntlet
- tree-sitter
- graph
- optional-dependency
---

# Gauntlet Integration

Performance review is a Tier-1 skill out of the box. Tiers 2 and
3 enrich the analysis when gauntlet is installed.

## Optional-import contract

At module load time, `performance_review.py` runs two
try-imports to module-level sentinels:

```python
try:
    from gauntlet.treesitter_parser import parse_file as _gt_parse
except (ImportError, ModuleNotFoundError):
    _gt_parse = None

try:
    from gauntlet.graph import GraphStore as _GraphStore
except (ImportError, ModuleNotFoundError):
    _GraphStore = None
```

The dual-exception catch matches the precedent in
`plugins/leyline/src/leyline/tokens.py:25-32`. It survives the
case where the import fails for a reason other than the module
being absent (e.g., a transitive ImportError deep inside
gauntlet's own stack).

Each tier helper checks its sentinel and early-returns:

```python
def _tier2_findings(self, context, file_path):
    if _gt_parse is None:
        return []
    ...

def _tier3_findings(self, context, existing, file_path):
    if _GraphStore is None:
        return []
    ...
```

This is the same pattern proven in
`plugins/pensive/hooks/pr_blast_radius.py:52-56`, where
gauntlet's blast-radius graph is consulted only when the
plugin is installed.

## Tier 2: Tree-sitter coverage

When `_gt_parse` is set, `_tier2_findings` invokes
`parse_file(path)` and receives `(nodes, edges)` describing the
target file's AST in gauntlet's neutral graph format.

Languages currently parsed: Python, JavaScript, TypeScript, Go,
Rust, Java, C, C++, C#, Ruby, PHP, Kotlin, Swift, Scala (per
gauntlet's `_EXT_TO_LANG` map).

The patterns translated to Tier 2 are the language-agnostic
ones:

- T1 (nested loop over same iterable): present in every
  imperative language.
- T2 (membership in list): adapts to language idioms (e.g.,
  `Array.includes` in JS, `slices.Contains` in Go).
- S1 (append in nested loops): `arr.push(...)` in JS,
  `append(slice, ...)` in Go.

Patterns that do NOT translate (skipped at Tier 2):

- T3 (`re.compile` in a loop): Python-specific call shape.
- T6 (list comprehension passed to a reducer): Python-specific
  syntax.
- T4 (string `+=`): many languages have language-level string
  builders that handle this; the cost model differs.

## Tier 3: Transitive call analysis

When both `_GraphStore` is set AND a `.gauntlet/graph.db` file
exists in the working tree, `_tier3_findings` opens the graph
and queries `impact_radius()` for each existing finding's
function.

If a function reachable from a Tier-1/2 hotspot is itself a
hotspot, the original finding's severity is upgraded one step:

| Original | Upgraded |
|----------|----------|
| LOW      | MEDIUM   |
| MEDIUM   | HIGH     |
| HIGH     | CRITICAL |

This catches cases where the surface code look

modules/kuva-visualization.md

---
module: kuva-visualization
category: output
dependencies: [Bash, Read]
estimated_tokens: 350
---

# Visualizing Performance Findings with kuva

**When a performance review produces before/after benchmark data,
render it as a chart.** Text comparisons like "380ms → 60ms" are
correct but hard to scan across multiple hotspots. A scatter or
bar chart makes regressions and wins immediately visible.

[kuva](https://github.com/Psy-Fer/kuva) is a Rust scientific
plotting library (and CLI binary) that renders directly from TSV/CSV
input to SVG, PNG, or the terminal. Install once; pipe benchmark
data in without modifying project source.

## Install

```bash
cargo install kuva --features cli
```

## Rendering a before/after benchmark comparison

### criterion (Rust)

criterion writes per-benchmark timing samples to
`target/criterion/<name>/new/estimates.json`. Extract the mean and
pipe to kuva:

```bash
# Collect before/after means for all criterion benchmarks
python3 - <<'EOF'
import json, pathlib, sys

rows = ["benchmark\tstage\tns"]
for est in pathlib.Path("target/criterion").rglob("estimates.json"):
    bench = est.parts[-3]
    data = json.loads(est.read_text())
    mean_ns = data["mean"]["point_estimate"]
    # Distinguish before/after by tag; adjust to your workflow.
    rows.append(f"{bench}\tafter\t{mean_ns:.1f}")

print("\n".join(rows))
EOF | kuva bar /dev/stdin --x benchmark --y ns --color-by stage \
      --title "Before vs After" --terminal
```

For a paired comparison where you have both runs saved:

```bash
# before.tsv and after.tsv each contain: benchmark<TAB>ns
kuva scatter before.tsv after.tsv \
    --x ns --y ns --color-by stage \
    --title "Hotspot timing (lower is better)" \
    -o perf-comparison.svg
```

### pytest-benchmark (Python)

```bash
pytest --benchmark-json=bench.json tests/

# Convert to TSV
python3 -c "
import json, sys
d = json.load(open('bench.json'))
print('name\tns')
for b in d['benchmarks']:
    print(b['name'] + '\t' + str(b['stats']['mean'] * 1e9))
" | kuva bar /dev/stdin --x name --y ns \
      --title "Benchmark means (ns)" -o bench.svg
```

### Ad-hoc timing table

If you are capturing timings manually (e.g., from production traces
as in the mlock war story):

```tsv
stage	p50_ms	p99_ms
before_mlock	180	380
after_mlock	35	60
```

```bash
kuva bar timings.tsv --x stage --y p99_ms \
    --title "p99 barge-in latency (ms)" -o latency.svg
```

## Terminal output (no file required)

For quick CI feedback without writing an SVG artifact, add
`--terminal` to any kuva command. The chart renders as Unicode
block characters directly in the shell, visible in CI logs.

```bash
kuva bar timings.tsv --x stage --y p99_ms --terminal
```

## When to attach a chart as evidence

The `Skill(imbue:proof-of-work)` discipline requires evidence
references `[E1]`/`[E2]` for before/after claims. A kuva-rendered
SVG in the PR description or comments is a valid `[E2]` when it
shows the post-fix benchmark result alongside the pre-fix b

modules/space-complexity.md

---
module: space-complexity
description: AST patterns for space-complexity hotspot detection
parent_skill: performance-review
category: code-quality
tags:
- space-complexity
- memory
- ast
- python
---

# Space Complexity Detectors

Three AST patterns that signal likely space-complexity hotspots.
Each detector cites the AST node it matches, the heuristic, and
a concrete fix.

## S1: Unbounded `.append()` inside nested loops (MEDIUM)

**AST shape**: `ast.Call` whose `func` is `ast.Attribute` named
`append`, found while the loop stack has depth >= 2.

**Why it matters**: A single-loop accumulator is bounded by the
input size, which is usually fine. A nested-loop accumulator
grows multiplicatively (n×m or n²) and is the typical "result
explosion" pattern that drives memory exhaustion.

**Note**: The detector deliberately does not flag single-loop
appends. They are common, expected, and rarely a hotspot. If
single-loop accumulation becomes a problem, that is a runtime
profiling concern handled by
`Skill(parseltongue:python-performance)`.

**Fix**: If the consumer can iterate, yield instead of
materialize:

```python
def all_pairs(xs):
    for x in xs:
        for y in xs:
            yield (x, y)  # streaming, O(1) space
```

When the full list is genuinely needed, document the size
bound:

```python
# Bounded: |xs| <= 100, so output <= 10000 pairs.
out = [(x, y) for x in xs for y in xs]
```

## S2: List wrapping a generator inside a reducer (LOW)

**AST shape**: `ast.Call` to one of `sum`, `max`, `min`, `any`,
`all`, `sorted`, `set`, `frozenset`, where the first arg is
itself an `ast.Call` to `list`, `dict`, `tuple`, or `set` with
an `ast.GeneratorExp` as its first argument.

**Why it matters**: `max(list(g))` allocates the full list, then
walks it. The wrapper is redundant: reducers accept generators
directly.

**Fix**:

```python
# Before
return max(list(x * 2 for x in xs))

# After
return max(x * 2 for x in xs)
```

For `sorted` / `set` the wrapper is sometimes intentional (to
force evaluation), but it's still cheaper to let `sorted` /
`set` consume the generator directly.

## S3: Per-iteration allocation inside a loop (MEDIUM)

**AST shape**: `ast.Call` inside a loop body where either:

- The `func` is an `ast.Attribute` with name `copy`, or
- The `func` is an `ast.Name` of `dict`, `list`, or `tuple`
  with a non-comprehension first argument (the comprehension
  case is a builder, not a copy).

**Why it matters**: `base.copy()` per iteration allocates a new
container N times. If only one or two fields change per
iteration, a single allocation outside the loop with selective
mutation costs less.

**Fix**: Hoist when possible.

```python
# Before
for x in items:
    snapshot = base.copy()
    snapshot["key"] = x
    out.append(snapshot)

# After (when downstream tolerates shared dict identity):
shared = {**base}
for x in items:
    shared["key"] = x
    out.append(dict(shared))  # explicit copy at the boundary
```

When the snapshots must be inde

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