Sep logo

Sep

Organization
managedcode
sep

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.

Overview

Publishermanagedcode
Repositorydotnet-skills
Skill namesep
Stars
492
Forks
38
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by managedcode on GitHub. Read the source before you install it.

Installation

Install the Sep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/managedcode/dotnet-skills/tree/main/catalog/Libraries/Sep/skills/sep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/managedcode/dotnet-skills.git /tmp/dotnet-skills
mkdir -p .claude/skills
cp -r /tmp/dotnet-skills/catalog/Libraries/Sep/skills/sep .claude/skills/sep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:
  • Source:

Workflow

  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 structs:
    • 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sep AI skill do?

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.

Why use Sep on TypingMind?

Because you install it once and use it with any model. Sep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/managedcode/dotnet-skills/tree/main/catalog/Libraries/Sep/skills/sep. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sep?

As many as you like. As long as a model supports skills, you can use Sep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sep AI skill free?

Yes. It is published on GitHub by managedcode under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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