Chat Compactor

Q: Why use Chat Compactor on TypingMind?

Because you install it once and use it with any model. Chat Compactor 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.

Q: How do I install Chat Compactor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ZhanlinCui/Agent-Skills-Hunter/tree/main/planning/chat-compactor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Q: Which AI models can use Chat Compactor?

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.

Q: How many AI models can I use with Chat Compactor?

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

Q: Is the Chat Compactor AI skill free?

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

Q: 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.

Q: 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.

Community

ZhanlinCui

chat-compactor

Generate structured session summaries optimized for future AI agent consumption. Use when (1) ending a coding/debugging session, (2) user says "compact", "summarize session", "save context", or "wrap up", (3) context window is getting long and continuity matters, (4) before switching tasks or taking a break. Produces machine-readable handoff documents that let the next session start fluently without re-explaining.

Overview

Publisher	ZhanlinCui
Repository	`Agent-Skills-Hunter`
Skill name	`chat-compactor`
Stars	186
Forks	26
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 ZhanlinCui on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/ZhanlinCui/Agent-Skills-Hunter.git /tmp/Agent-Skills-Hunter
mkdir -p .claude/skills
cp -r /tmp/Agent-Skills-Hunter/planning/chat-compactor .claude/skills/chat-compactor

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chat Compactor 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 Compactor 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 Compactor 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 Compactor

Generate structured summaries optimized for AI agent continuity across sessions.

Why This Exists

Human-written summaries and ad-hoc AI summaries lose critical context:

Decision rationale gets lost (why X, not Y)
Dead ends get forgotten (agent re-tries failed approaches)
Implicit knowledge isn't captured (file locations, naming conventions, gotchas)
State is unclear (what's done, what's pending, what's blocked)

This skill produces agent-optimized handoff documents that prime the next session.

Output Format

Generate a markdown file with this structure:

markdown
# Session: [Brief Title]
Date: [YYYY-MM-DD]
Duration: ~[X] messages

## Context Snapshot
[1-2 sentences: What project/task, what state it's in right now]

## What Was Accomplished
- [Concrete outcome 1]
- [Concrete outcome 2]

## Key Decisions & Rationale
| Decision | Why | Alternatives Rejected |
|----------|-----|----------------------|
| [Choice] | [Reason] | [What didn't work and why] |

## Current State
- **Working**: [files/features that are functional]
- **Broken/Blocked**: [what's not working and why]
- **Modified files**: [list with brief note on changes]

## Dead Ends (Don't Retry)
- ❌ [Approach that failed] — [why it failed]

## Next Steps (Prioritized)
1. [ ] [Most important next action]
2. [ ] [Second priority]

## Environment & Gotchas
- [Any setup notes, versions, quirks discovered]

## Key Code/Commands Reference
[Only if there are non-obvious commands or snippets the next session needs]

Workflow

Scan conversation for: decisions, outcomes, failures, file changes, blockers
Identify the "handoff moment" — what would a fresh agent need to continue?
Generate structured summary using format above
Save to file: session-[topic]-[date].md in project root or /home/claude/sessions/

Compaction Triggers

Invoke this skill when:

User says: "compact", "wrap up", "save session", "summarize for next time"
Context window exceeds ~50% capacity and task is ongoing
Before major context switches
End of debugging/implementation session

Quality Criteria

Good compactions are:

Scannable: Next agent gets orientation in <30 seconds
Actionable: Clear next steps, not vague summaries
Defensive: Dead ends documented to prevent re-exploration
Minimal: No fluff, every line earns its tokens

Anti-Patterns

Avoid:

Narrative prose ("First we tried X, then Y, then Z...")
Redundant context (don't repeat what's in code comments)
Vague summaries ("Made good progress on the feature")
Missing failure documentation (most valuable part!)

Example Trigger & Response

User: "Let's wrap up, compact this session"

Agent:

Reviews conversation for key decisions, outcomes, failures
Generates structured markdown per format above
Saves to session-[topic]-[date].md
Confirms: "Session compacted to session-auth-refactor-2025-01-06.md — ready for next time."

Frequently asked questions

What does the Chat Compactor AI skill do?

Why use Chat Compactor on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ZhanlinCui/Agent-Skills-Hunter/tree/main/planning/chat-compactor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Chat Compactor?

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 Compactor?

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

Is the Chat Compactor AI skill free?

Yes. It is published on GitHub by ZhanlinCui 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Chat Compactor

Why This Exists

Output Format

Workflow

Compaction Triggers

Quality Criteria

Anti-Patterns

Example Trigger & Response

Frequently asked questions

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