Analyzing Dotnet Performance

.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 | | `Span10x 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 Dictionary10x 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 ❌/✅ code blocks** for trivial fixes (adding a keyword, parameter, or type change). A one-line fix description suffices.
- **Only include code blocks** for non-obvious transformations (e.g., replacing a LINQ chain with a foreach loop, or hoisting a closure).
- **File locations as inline comma-separated list**, not a table. Use `File.cs:L42` format.
- **No explanatory prose** beyond the Impact line — the severity icon already conveys urgency.
- **Merge related findings** that share the same fix (e.g., all `.ToLower()` calls go in one finding, not split by file).
- **Positive findings** in a bullet list, not a table. One line per pattern: `✅ Pattern — evidence`.

End with a summary table and disclaimer:

```markdown
| Severity | Count | Top Issue |
|----------|-------|-----------|
| 🔴 Critical | N | ... |
| 🟡 Moderate | N | ... |
| ℹ️ Info | N | ... |

> ⚠️ **Disclaimer:** These results are generated by an AI assistant and are non-deterministic. Findings may include false positives, miss real issues, or suggest changes that are incorrect for your specific context. Always verify recommendations with benchmarks and human review before applying changes to production code.

Validation

Before delivering results, verify:

• [ ] All critical patterns were checked (from reference files or inline recipes)
• [ ] Topic-specific recipes run only when matching signals detected
• [ ] Each finding includes a concrete code fix
• [ ] Scan execution checklist is complete (all recipes run)
• [ ] Summary table included at end

Common Pitfalls

| Pitfall | Correct Approach | |---------|-----------------| | Flagging every Dictionary as needing FrozenDictionary | Only flag if the dictionary is never mutated after construction | | Suggesting Span in async methods | Use Memory in async code; Span only in sync hot paths | | Reporting LINQ outside hot paths | Only flag LINQ in identified hot paths or tight loops; LINQ is acceptable in code that runs infrequently. Since .NET 7, LINQ Min/Max/Sum/Average are vectorized — blanket bans on LINQ are misguided | | Suggesting ConfigureAwait(false) in app code | Only applicable in library code; not primarily a performance concern | | Recommending ValueTask everywhere | Only for hot paths with frequent synchronous completion | | Flagging new HttpClient() in DI services | Check if IHttpClientFactory is already in use | | Suggesting [GeneratedRegex] for dynamic patterns | Only flag when the pattern string is a compile-time literal | | Suggesting CollectionsMarshal.AsSpan broadly | Only for ultra-hot paths with benchmarked evidence; adds complexity and fragility | | Suggesting unsafe code for micro-optimizations | Avoid unsafe except where absolutely necessary — do not recommend it for micro-optimizations that don't matter. Safe alternatives like Span, stackalloc in safe context, and ArrayPool cover the vast majority of performance needs |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dotnet
• Source: dotnet/skills
• License: MIT

Install and usage instructions live in the source repository linked above.