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Chat To Skill

Community
dannote
chat-to-skill

Convert current chat session into a reusable skill. Use when user says "/chat-to-skill", "save this as skill", "create skill from chat", "turn this into a skill", or wants to preserve learnings from the conversation as long-term memory.

Overview

Publisherdannote
Repositorydot-pi
Skill namechat-to-skill
Stars
57
Forks
4
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 dannote on GitHub. Read the source before you install it.

Installation

Install the Chat To Skill 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/dannote/dot-pi/tree/master/skills/chat-to-skill
  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/dannote/dot-pi.git /tmp/dot-pi
mkdir -p .claude/skills
cp -r /tmp/dot-pi/skills/chat-to-skill .claude/skills/chat-to-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chat To Skill 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 Chat To Skill 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 Chat To Skill 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.

Chat to Skill

Transform conversation history into reusable skills — long-term memory for Claude.

Process

1. Analyze Dialog

Scan the entire conversation to identify:

  • Primary goal: What was the user trying to achieve?
  • Secondary goals: Any related objectives discovered along the way
  • Errors encountered: Mistakes, dead ends, wrong approaches
  • Successful path: What actually worked

2. Abstract to Reusable Patterns

Critical: Do NOT create skills for specific cases. Abstract to general patterns.

Ask yourself:

  • What CATEGORY of problem was solved? (not the specific instance)
  • What would this look like with different data/context?
  • Would this skill be useful in other projects?

Abstraction levels (from bad to good):

Too specific (BAD)Good abstraction
"Seed users from client Excel""Import spreadsheet data into Rails"
"Parse names into fields"(implementation detail, not a skill)
"Fix pytest in project X""Configure pytest for monorepos"
"Add dark mode to app Y""Implement theme switching in React"

Rules:

  • Remove project names, organization names, specific entities
  • Focus on the TECHNIQUE, not the specific data
  • If something is just an implementation detail (name parsing, date formatting), it's not a separate skill
  • One dialog = usually one skill (the main workflow), not multiple micro-skills

3. Extract Context-Specific Details

Depending on the task type, look for:

Development tasks:

  • Commands and flags that worked
  • Versions and compatibility (what works with what)
  • Configuration that was needed
  • Code patterns and architectural decisions
  • Debugging process (how the root cause was found)
  • Tool/library choices and why

Research/analysis tasks:

  • Sources that proved useful
  • Search strategies that worked
  • How to validate findings

Process/workflow tasks:

  • Order of operations (what must come first)
  • Decision criteria (how choices were made)
  • Stakeholders or dependencies

Any task:

  • Prerequisites that weren't obvious
  • Context that matters for success
  • Signs that indicate the right/wrong path

4. Choose Topic (if multiple found)

If dialog contains several distinct learnable patterns, use AskUserQuestion:

Question: "I found several skill candidates. Which to create?"

Options: List 2-4 abstracted topics (not specific tasks), plus "All of them"

5. Validate Skill Candidate

Before proposing, check:

  • Is this reusable in other projects/contexts?
  • Is this a workflow/technique, not just a one-off fix?
  • Would future-me benefit from having this skill?
  • Is it abstracted enough to apply broadly?

If NO to any — either abstract further or skip skill creation.

6. Propose Topic (if valid)

Present ONE main skill (not a list of micro-topics):

Skill candidate: [Abstracted name]

What it captures: [1-2 sentences about the reusable pattern]

Key learnings:
- [Main insight 1]
- [Main insight 2]
- [Mistake to avoid]

Then ask using AskUserQuestion tool:

Question: "How does this skill proposal look?"

Options:

  1. "Good, create it" — proceed to step 7
  2. "Too specific" — re-abstract: remove project/entity names, find broader pattern
  3. "Wrong focus" — re-analyze: what was the MAIN technique vs implementation details?
  4. "Not reusable" — reconsider: is this a one-off task or a recurring pattern?

7. Revise (if needed)

Based on user's choice, fix the specific issue:

"Too specific":

  • What broader category does this belong to?
  • Would this apply with different tools/platforms? Generalize.

"Wrong focus":

  • What would you google to solve this problem?
  • The answer is probably the real skill name.

"Not reusable":

  • How often would this exact situation repeat?
  • If rarely — maybe no skill needed. Ask user what they hoped to capture.

8. Prepare Knowledge Package

Goal: One sentence — what this skill helps achieve

Trigger: When should this skill activate? (keywords, file types, contexts)

Mistakes to avoid:

  • Specific errors from the dialog
  • Why they were wrong
  • What they cost (time, confusion)

Optimal path:

  • Shortest working sequence
  • No exploration, only essentials
  • Include specific commands/configs if applicable

Key details:

  • Versions/compatibility if relevant
  • Non-obvious prerequisites
  • How to verify success

9. Create Skill

Use /skill-creator with the prepared knowledge package.

Frequently asked questions

What does the Chat To Skill AI skill do?

Convert current chat session into a reusable skill. Use when user says "/chat-to-skill", "save this as skill", "create skill from chat", "turn this into a skill", or wants to preserve learnings from the conversation as long-term memory.

Why use Chat To Skill on TypingMind?

Because you install it once and use it with any model. Chat To Skill 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 Chat To Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dannote/dot-pi/tree/master/skills/chat-to-skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Chat To Skill?

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 Chat To Skill?

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

Is the Chat To Skill AI skill free?

Yes. It is published on GitHub by dannote 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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