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Sip

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LilMGenius
sip

After you create or change an artifact or skill, taste-test it with our own skills instead of trusting your in-session judgment — recursive self-improvement, made automatic. Use right after writing or editing anything, before calling it done, committing, or handing it off.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill namesip
Stars
1.3K
Forks
118
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 LilMGenius on GitHub. Read the source before you install it.

Installation

Install the Sip 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/LilMGenius/paperthin/tree/main/skills/depth/sip
  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/LilMGenius/paperthin.git /tmp/paperthin
mkdir -p .claude/skills
cp -r /tmp/paperthin/skills/depth/sip .claude/skills/sip
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sip 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 Sip 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 Sip 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.

Taste your own cooking: the moment you finish making something, check it with the very skills this repo ships before you serve it.

Goal

A reminder buried in docs ("remember to verify") won't reliably fire in a fresh session. sip makes the recursive self-improvement loop a triggered habit: right after any create or change, run our own skills on the result — the clean checks and the true ones — so quality doesn't ride on the author's biased in-session judgment.

Workflow

  1. Spot the trigger: you just created or changed an artifact or skill and are about to call it done, commit, or hand it off.
  2. Cold-read it — run shower on the artifact (fresh-eyes comprehension / handoff check).
  3. Verify it's true — if the artifact asserts a reality-grounded claim, run factchk; if it defines an eval, metric, or experiment, run mandela. Skip when it has neither.
  4. Check consistency — run ssotize in audit mode across the repo for anything the change duplicated or contradicted; execute its consolidation plan only after approval.
  5. Detool portability claims — run detool only when the artifact claims portability, tool-neutrality, stack-agnostic durability, or cross-agent reuse. Skip when it is provenance, operational notes, a tool-targeted runbook, or does not claim portability.
  6. Tidy — re0 the changed docs so the result reads as a clean v0, not a patch over a draft.
  7. Apply the findings here, then serve it.

Rules

  • Trigger on your OWN output, right after making it — that's when bias is highest and a check is cheapest.
  • Use the skills; don't re-implement them — shower for clarity, factchk/mandela for truth, ssotize for SSOT, detool for portability claims, re0 for cleanup. sip orchestrates and routes findings back to the author session to fix; the skills do the work.
  • Skip what plainly doesn't apply, or any check whose skill isn't installed — run only what's present. factchk/mandela fire only when there is a claim or an eval; detool fires only when the artifact claims portability, tool-neutrality, stack-agnostic durability, or cross-agent reuse; a one-line prose tweak may need only a consistency check. Say what you skipped and why.
  • Stop at the artifact — sip never touches git or makes commits.
  • Chain only model-invoked skills; a user-invoked skill (marked disable-model-invocation) is a human's to fire deliberately, so sip must not call one.

Verification

Before finishing:

  1. The change was actually run through the relevant skills, not eyeballed.
  2. Findings were applied (or consciously deferred with a stated reason).
  3. The artifact actually changed as a result (a diff exists), or every skipped skill has a stated reason.

Frequently asked questions

What does the Sip AI skill do?

After you create or change an artifact or skill, taste-test it with our own skills instead of trusting your in-session judgment — recursive self-improvement, made automatic. Use right after writing or editing anything, before calling it done, committing, or handing it off.

Why use Sip on TypingMind?

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

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

Which AI models can use Sip?

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 Sip?

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

Is the Sip AI skill free?

Yes. It is published on GitHub by LilMGenius 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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