nw-hotspot
nw-hotspot analyzes git change frequency to identify the most-changed files in a codebase by counting commits per file over a configurable time period. Use it to scope analysis before other code quality checks (analyze mode), prioritize findings from existing reports (rank mode), examine a single file's change history (detail mode), or export ranked file data as JSON for further processing.
git clone --depth 1 https://github.com/nWave-ai/nWave /tmp/nw-hotspot && cp -r /tmp/nw-hotspot/nWave/skills/nw-hotspot ~/.claude/skills/nw-hotspotSKILL.md
# NW-HOTSPOT: Code Crime Scene Hotspot Lens
**Wave**: CROSS_WAVE
**Execution**: Inline (no agent — Claude executes git commands directly)
**Inspiration**: Adam Tornhill's "Your Code as a Crime Scene"
## Overview
Analyze git change frequency to identify the most-changed files in a codebase. Pure git churn — no complexity metrics. Use as a **pre-filter** to scope analysis or a **post-filter** to prioritize existing findings.
## How It Works
Run `git log --name-only` over the configured time period, count commits per file, rank by frequency.
## Modes
### 1. Analyze (default)
Show the most-changed files in the repo. Output is inline only — no files persisted.
```
/nw-hotspot
/nw-hotspot --top=20
/nw-hotspot --since=12m
```
**Steps:**
1. Run: `git log --since={since} --format=format: --name-only | sort | uniq -c | sort -rn`
2. Exclude deleted files (verify each file still exists with `test -f`)
3. For each file, get last changed date: `git log -1 --format="%ar" -- {file}`
4. Display ranked table (top N):
```
Rank │ Commits │ Last Changed │ File
──────┼─────────┼──────────────┼─────────────────────────
1 │ 87 │ 2 days ago │ src/services/payment.ts
2 │ 64 │ 1 week ago │ src/api/controllers/user.ts
3 │ 51 │ 3 days ago │ src/models/order.ts
```
5. Show summary: total files changed in period, time period, top N shown
**Defaults:** `--top`: 15, `--since`: 6 months
### 2. Rank (post-filter)
Overlay churn data on an existing analysis report to prioritize findings.
```
/nw-hotspot --rank code-smell-detector-report.md
```
**Steps:**
1. Run the same git churn analysis
2. Read the specified report file
3. Extract file paths mentioned in the report
4. Annotate each file with its commit count
5. Re-sort findings by churn (highest first). Findings without extractable file paths go at the bottom.
6. Show noise reduction: "X of Y findings are in low-churn files (< 20 commits)"
### 3. Detail (deep-dive)
Deep-dive into a single file's change history.
```
/nw-hotspot --detail src/services/payment.ts
```
**Steps:**
1. Monthly commit breakdown over the period
2. Top contributors to the file
3. Co-change coupling: top 5 files that frequently change alongside this one
4. Last 10 commit messages for context
### 4. JSON export (pre-filter composition)
```
/nw-hotspot --top=10 --json
```
Output: `[{"path": "src/services/payment.ts", "commits": 87, "last_changed": "2 days ago"}, ...]`
## Parameters
| Flag | Default | Description |
|------|---------|-------------|
| `--top` | 15 | Number of files to show |
| `--since` | 6m | Time period (e.g., 3m, 12m, 2024-01-01) |
| `--json` | false | Output as JSON array |
| `--rank` | - | Path to existing report to re-rank by churn |
| `--detail` | - | Path to single file for deep-dive |
## Edge Cases
- **No commits in period**: Display "No commits found since {date}" rather than an empty table
- **File renames**: Counted as separate files (no `--follow`; keeps it simple and fast)
- **Empty repo / not a git repo**: Display error and exit
## Usage from Other Skills
### Pre-filter (scope downstream analysis)
Run `/nw-hotspot --top=10 --json` first, then pass the file list to:
- **code-smell-detector**: "Analyze only these files: {hotspot list}"
- **cognitive-load-analyzer**: "Focus analysis on these files: {hotspot list}"
- **refactoring-expert**: "Prioritize recommendations for these files"
### Post-filter (rank existing findings)
Run `/nw-hotspot --rank {report.md}` after any analysis to prioritize by churn.
### In nWave workflows
- Before `/nw-refactor` — identify which files to refactor first
- Before `/nw-review` — focus review effort on high-churn areas
- Before `/nw-root-why` — check if the problematic area is a known hotspot
## Next Wave
**Usage Context**: Inline utility, composable with any analysis skill
**Handoff To**: Invoking context (no sequential handoff)
## Examples
### Example 1: Quick scan of a legacy codebase
```
/nw-hotspot
```
### Example 2: Scope a code smell analysis to hotspots
```
/nw-hotspot --top=10 --json
```
### Example 3: Prioritize an existing refactoring report
```
/nw-hotspot --rank code-refactoring-report.md
```
### Example 4: Investigate a specific troubled file
```
/nw-hotspot --detail src/services/payment.ts
```
## Expected Outputs
No persistent files. Output is displayed inline. JSON mode outputs a JSON array for composition with other tools.Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance
Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating architecture styles.
Comprehensive architecture patterns, methodologies, quality frameworks, and evaluation methods for solution architects. Load when designing system architecture or selecting patterns.
Canonical AT completeness gate — research-anchored 7-category taxonomy (C1-C7) + 15-item mechanical checklist. Paradigm-neutral. Drives acceptance-designer reviewer verdict deterministically.