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Deslop

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jtaroreh
deslop

Clean AI slop and defensiveness out of code and diffs before commit. Strips narrating comments, redundant guards, speculative error handling, unused abstractions, and dead compatibility code.

Overview

Publisherjtaroreh
Repositoryagystack
Skill namedeslop
Stars
120
Forks
12
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Deslop 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/jtaroreh/agystack/tree/main/skills/deslop
  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/jtaroreh/agystack.git /tmp/agystack
mkdir -p .claude/skills
cp -r /tmp/agystack/skills/deslop .claude/skills/deslop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deslop 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 Deslop 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 Deslop 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.

Deslop

Clean AI patterns, excessive defensiveness, and unnecessary bloat out of code and git diffs before committing.

While /unslop cleans human/agent prose and /no-comments strips comments before review, /deslop operates directly on application code and diffs.

Process

  1. Inspect the diff. Run git diff against your base branch or HEAD~1. Focus on newly added or modified lines.
  2. Scan for code slop patterns (see checklist below).
  3. Refactor and simplify. Delete defensive bloat, eliminate speculative layers, and restore direct, obvious code.
  4. Verify correctness. Run tests, typechecks, or linters to prove the streamlined code still works.

Code Slop Patterns to Detect and Fix

1. Defensive Over-Engineering

  • Redundant null/undefined checks: Checking variables that internal types or preceding guards guarantee cannot be null. Trust internal invariants; concentrate validation at external boundaries (principle-boundary-discipline).
  • Try-catch swallows: Wrapping synchronous or infallible calls in try...catch blocks that swallow errors or return fallback values silently.
  • Speculative error handling: Catching errors that cannot happen in normal flow rather than letting real failures surface (principle-fix-root-causes).

2. Speculative Flexibility

  • Unrequested configuration: Adding options objects, configurable flags, or generalized plugin systems for single-use logic (principle-laziness-protocol).
  • One-caller abstractions: Creating helper functions, interfaces, or wrapper classes used in exactly one place with no likelihood of reuse (principle-minimize-reader-load).
  • Premature type parameterization: Introducing complex generic type parameters where concrete types suffice.

3. Narrating and Placeholder Comments

  • Obvious narration: // check if user exists, // return the result, // loop through items. Delete them immediately.
  • Section banner comments: // ===================== HELPERS =====================. Remove them.
  • Commented-out code: Dead code blocks left "just in case". Delete them; git history preserves past code (principle-subtract-before-you-add).

4. Artificial AI Code Tells

  • Intermediate variable churn: Assigning a value to a temporary variable only to immediately return or pass it once:
    ts
    // Slop
    const result = calculateTotal(items);
    return result;
    
    // Clean
    return calculateTotal(items);
  • Redundant type assertions: Overusing as any, as unknown as T, or unnecessary non-null assertions ! when proper typing or narrowing makes the code type-safe (principle-type-system-discipline).
  • Verbose conditional boilerplate: Using 10-line if/else ladders where a simple ternary, dictionary lookup, or early return is cleaner.

5. Lingering Debug and Scratch Artifacts

  • Leftover console.log, print(), dbg!(), or ad-hoc benchmark timers.
  • Temporary test fixtures or scratch files committed by mistake.

Output / Reply

State:

  • Files cleaned and line delta (e.g. -45 lines).
  • Types of slop removed (e.g., redundant null guards, one-caller helper collapsed, narrating comments stripped).
  • Verification command run to confirm tests and types remain green.

Frequently asked questions

What does the Deslop AI skill do?

Clean AI slop and defensiveness out of code and diffs before commit. Strips narrating comments, redundant guards, speculative error handling, unused abstractions, and dead compatibility code.

Why use Deslop on TypingMind?

Because you install it once and use it with any model. Deslop 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 Deslop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jtaroreh/agystack/tree/main/skills/deslop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deslop?

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 Deslop?

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

Is the Deslop AI skill free?

Yes. It is published on GitHub by jtaroreh 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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