Use Sep for high-performance separated-value parsing and writing in .NET, including delimiter inference, explicit parser/writer options, and low-allocation row/column workflows. USE FOR: delimited data needs are performance-sensitive and allocation-aware; project needs explicit control over separator inference, escaping, trimming, and header behavior;. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targete...
Scanned 5/27/2026
Install via CLI
openskills install managedcode/dotnet-skills---
name: sep
description: "Use Sep for high-performance separated-value parsing and writing in .NET, including delimiter inference, explicit parser/writer options, and low-allocation row/column workflows. USE FOR: delimited data needs are performance-sensitive and allocation-aware; project needs explicit control over separator inference, escaping, trimming, and header behavior;. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made."
compatibility: "Requires a .NET project that can reference the `Sep` package and accept span/ref-struct row/column APIs for row-by-row processing."
---
# Sep for .NET separated values
## Trigger On
- delimited data needs are performance-sensitive and allocation-aware
- project needs explicit control over separator inference, escaping, trimming, and header behavior
- reading/writing large or long-lived file pipelines in ML, ETL, or analytics workloads
- startup/perf tests require AOT/trimming-friendly CSV/TSV processing
## Install
- NuGet:
- `dotnet add package Sep`
- `dotnet add package Sep --version <version>`
- XML package reference:
- `<PackageReference Include="Sep" Version="x.y.z" />`
- Verify baseline support by checking the package page:
- [NuGet: Sep](https://www.nuget.org/packages/Sep/)
- Source:
- [GitHub: nietras/Sep](https://github.com/nietras/Sep)
## Workflow
```mermaid
flowchart LR
A[Input source: file/text/stream] --> B[Sep.Reader or Sep.New(...).Reader]
B --> C[SepReaderOptions]
C --> D[Rows -> Cols -> Span/Parse]
D --> E[Transform and validate]
E --> F[SepWriter via SepWriterOptions]
F --> G[To file/text output]
```
1. Decide schema shape
- header present or no header
- separator known (`;`, `,`, tab, custom) or infer from first row
- row/column quoting rules
2. Build reader with `Sep.Reader(...)` and explicit options only where needed:
- `Sep.Reader()` for inferred separator from header-like first row
- `Sep.New(',').Reader(...)` for explicit separator mode
- `Sep.Reader(o => o with { HasHeader = false })` if header is absent
3. Read rows and map columns as `ReadOnlySpan<char>` first, convert only when needed.
4. For output, use `reader.Spec.Writer()` when you need the same separator/culture as input.
5. Control writer behavior with `Sep.Writer(...)` and `SepWriterOptions` (`WriteHeader`, `Escape`, `DisableColCountCheck`).
6. Add async only where it brings value and your runtime is C# 13 / .NET 9+ for `await foreach` over async reader rows.
7. Use `ParallelEnumerate` for CPU-heavy transformations only after benchmarking single-threaded baseline.
### Install and read patterns
```csharp
using var reader = Sep.Reader(o => o with
{
HasHeader = true,
Unescape = true,
Trim = SepTrim.Both
}).FromText(data);
foreach (var row in reader)
{
var id = row["Id"].Parse<int>();
var name = row[1].ToString();
// process row
}
```
### Write patterns
```csharp
using var reader = Sep.Reader().FromFile("input.csv");
using var writer = reader.Spec.Writer().ToFile("output.csv");
foreach (var row in reader)
{
using var writeRow = writer.NewRow(row);
writeRow["Amount"].Format(row["Amount"].Parse<double>() * 1.2);
}
```
### Async reading and writing
```csharp
var text = "A;B\n1;hello\n";
using var reader = await Sep.Reader().FromTextAsync(text);
await using var writer = reader.Spec.Writer().ToText();
await foreach (var row in reader)
{
await using var writeRow = writer.NewRow(row);
var normalized = row["B"].ToString().ToUpperInvariant();
writeRow["B"].Set(normalized);
}
```
### Common configuration patterns
- Header-driven read
- default `HasHeader = true`
- query by name: `row["ColName"]`
- Headerless pipelines
- `HasHeader = false`
- use index-based access: `row[0]`, `row[1]`
- Round-trip output
- start writer with `reader.Spec.Writer()` to preserve inference and formatting contract
- Speed-first processing
- keep default buffer + culture unless profiling proves a need to tune
## Best practices
- Parse to primitive types with `Parse<T>` in hot paths to avoid extra allocations.
- Keep `ToString`/format conversions at the edge (presentational layers), not in inner loops.
- Prefer `Unescape`, `Trim`, and `DisableQuotesParsing` settings deliberately and test with realistic samples.
- For large transforms, isolate heavy CPU work after enumeration and then apply `ParallelEnumerate` where appropriate.
## Limitations to check before production
- `SepReader.Row` and `SepWriter.Row` are `ref struct`s:
- avoid patterns that store rows beyond immediate scope
- materialize if you truly need random async/LINQ-style buffering
- `SepReader` row iteration is row-by-row by design; it is intentionally not the same as a classic collection model.
## Deliver
- installation and usage guide that is ready to copy into a .NET repo
- practical reader/writer configuration patterns
- clear notes on defaults, tradeoffs, and constraints
## Validate
- `dotnet add package Sep` installs correctly and project compiles
- one file-read sample and one file-write sample execute successfully
- header/no-header and explicit-separator cases are covered
- at least one validation sample for quoting/unescaping or async path exists if required by task
## Load References
- [references/overview.md](references/overview.md) - official links and practical decision notes.
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