analyzing-dotnet-performance

What this skill does

# .NET Performance Patterns

Scan C#/.NET code for performance anti-patterns and produce prioritized findings with concrete fixes. Patterns sourced from the official .NET performance blog series, distilled to customer-actionable guidance.

## When to Use

- Reviewing C#/.NET code for performance optimization opportunities
- Auditing hot paths for allocation-heavy or inefficient patterns
- Systematic scan of a codebase for known anti-patterns before release
- Second-opinion analysis after manual performance review

## When Not to Use

- **Algorithmic complexity analysis** — this skill targets API usage patterns, not algorithm design
- **Code not on a hot path** with no performance requirements — avoid premature optimization

## Inputs

| Input | Required | Description |
|-------|----------|-------------|
| Source code | Yes | C# files, code blocks, or repository paths to scan |
| Hot-path context | Recommended | Which code paths are performance-critical |
| Target framework | Recommended | .NET version (some patterns require .NET 8+) |
| Scan depth | Optional | `critical-only`, `standard` (default), or `comprehensive` |

## Workflow

### Step 1: Load Reference Files (if available)

Try to load `references/critical-patterns.md` and the topic-specific reference files listed below. These contain detailed detection recipes and grep commands.

**If reference files are not found** (e.g., in a sandboxed environment or when the skill is embedded as instructions only), **skip file loading and proceed directly to Step 3** using the scan recipes listed inline below. Do not spend time searching the filesystem for reference files — if they aren't at the expected relative path, they aren't available.

### Step 2: Detect Code Signals and Select Topic Recipes

Scan the code for signals that indicate which pattern categories to check. If reference files were loaded, use their `## Detection` sections. Otherwise, use the inline recipes in Step 3.

| Signal in Code | Topic |
|----------------|-------|
| `async`, `await`, `Task`, `ValueTask` | Async patterns |
| `Span<`, `Memory<`, `stackalloc`, `ArrayPool`, `string.Substring`, `.Replace(`, `.ToLower()`, `+=` in loops, `params` | Memory & strings |
| `Regex`, `[GeneratedRegex]`, `Regex.Match`, `RegexOptions.Compiled` | Regex patterns |
| `Dictionary<`, `List<`, `.ToList()`, `.Where(`, `.Select(`, LINQ methods, `static readonly Dictionary<` | Collections & LINQ |
| `JsonSerializer`, `HttpClient`, `Stream`, `FileStream` | I/O & serialization |

Always check structural patterns (unsealed classes) regardless of signals.

**Scan depth controls scope:**
- `critical-only`: Only critical patterns (deadlocks, >10x regressions)
- `standard` (default): Critical + detected topic patterns
- `comprehensive`: All pattern categories

### Step 3: Scan and Report

**For files under 500 lines, read the entire file first** — you'll spot most patterns faster than running individual grep recipes. Use grep to confirm counts and catch patterns you might miss visually.

For each relevant pattern category, run the detection recipes below. Report exact counts, not estimates.

**Core scan recipes** (run these when reference files aren't available):
```
# Strings & memory
grep -n '\.IndexOf(\"' FILE                    # Missing StringComparison
grep -n '\.Substring(' FILE                    # Substring allocations
grep -En '\.(StartsWith|EndsWith|Contains)\s*\(' FILE  # Missing StringComparison
grep -n '\.ToLower()\|\.ToUpper()' FILE        # Culture-sensitive + allocation
grep -n '\.Replace(' FILE                      # Chained Replace allocations
grep -n 'params ' FILE                         # params array allocation

# Collections & LINQ
grep -n '\.Select\|\.Where\|\.OrderBy\|\.GroupBy' FILE  # LINQ on hot path
grep -n '\.All\|\.Any' FILE                    # LINQ on string/char
grep -n 'new Dictionary<\|new List<' FILE      # Per-call allocation
grep -n 'static readonly Dictionary<' FILE     # FrozenDictionary candidate

# Regex
grep -n 'RegexOptions.Compiled' FILE           # Compiled regex budget
grep -n 'new Regex(' FILE                      # Per-call regex
grep -n 'GeneratedRegex' FILE                  # Positive: source-gen regex

# Structural
grep -n 'public class \|internal class ' FILE  # Unsealed classes
grep -n 'sealed class' FILE                    # Already sealed
grep -n ': IEquatable' FILE                    # Positive: struct equality
```

**Rules:**
- Run every relevant recipe for the detected pattern categories
- **Emit a scan execution checklist** before classifying findings — list each recipe and the hit count
- A result of **0 hits** is valid and valuable (confirms good practice)
- If reference files were loaded, also run their `## Detection` recipes

**Verify-the-Inverse Rule:** For absence patterns, always count both sides and report the ratio (e.g., "N of M classes are sealed"). The ratio determines severity — 0/185 is systematic, 12/15 is a consistency fix.

### Step 3b: Cross-File Consistency Check

If an optimized pattern is found in one file, check whether sibling files (same directory, same interface, same base class) use the un-optimized equivalent. Flag as 🟡 Moderate with the optimized file as evidence.

### Step 3c: Compound Allocation Check

After running scan recipes, look for these multi-allocation patterns that single-line recipes miss:

1. **Branched `.Replace()` chains:** Methods that call `.Replace()` across multiple `if/else` branches — report total allocation count across all branches, not just per-line.
2. **Cross-method chaining:** When a public method delegates to another method that itself allocates intermediates (e.g., A calls B which does 3 regex replaces, then A calls C), report the total chain cost as one finding.
3. **Compound `+=` with embedded allocating calls:** Lines like `result += $"...{Foo().ToLower()}"` are 2+ allocations (interpolation + ToLower + concatenation) — flag the compound cost, not just the `.ToLower()`.
4. **`string.Format` specificity:** Distinguish resource-loaded format strings (not fixable) from compile-time literal format strings (fixable with interpolation). Enumerate the actionable sites.

### Step 4: Classify and Prioritize Findings

Assign each finding a severity:

| Severity | Criteria | Action |
|----------|----------|--------|
| 🔴 **Critical** | Deadlocks, crashes, security vulnerabilities, >10x regression | Must fix |
| 🟡 **Moderate** | 2-10x improvement opportunity, best practice for hot paths | Should fix on hot paths |
| ℹ️ **Info** | Pattern applies but code may not be on a hot path | Consider if profiling shows impact |

**Prioritization rules:**
1. If the user identified hot-path code, elevate all findings in that code to their maximum severity
2. If hot-path context is unknown, report 🔴 Critical findings unconditionally; report 🟡 Moderate findings with a note: _"Impactful if this code is on a hot path"_
3. Never suggest micro-optimizations on code that is clearly not performance-sensitive

**Scale-based severity escalation:**
When the same pattern appears across many instances, escalate severity:
- 1-10 instances of the same anti-pattern → report at the pattern's base severity
- 11-50 instances → escalate ℹ️ Info patterns to 🟡 Moderate
- 50+ instances → escalate to 🟡 Moderate with elevated priority; flag as a codebase-wide systematic issue

Always report exact counts (from scan recipes), not estimates or agent summaries.

### Step 5: Generate Findings

**Keep findings compact.** Each finding is one short block — not an essay. Group by severity (🔴 → 🟡 → ℹ️), not by file.

Format per finding:

```
#### ID. Title (N instances)
**Impact:** one-line impact statement
**Files:** file1.cs:L1, file2.cs:L2, ... (list locations, don't build tables)
**Fix:** one-line description of the change (e.g., "Add `StringComparison.Ordinal` parameter")
**Caveat:** only if non-obvious (version requirement, correctness risk)
```

**Rules for compact output:**
- **No ❌/