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Context Compression

CommunityPopular
vudovn
context-compression

Manage and compress conversation context in long sessions. Detect when context is growing large, summarize completed work phases, archive old findings while preserving key decisions. Prevents context degradation.

Overview

Publishervudovn
Repositoryag-kit
Skill namecontext-compression
Stars
8.2K
Forks
1.5K
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 vudovn on GitHub. Read the source before you install it.

Installation

Install the Context Compression 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/vudovn/ag-kit/tree/main/.agents/skills/context-compression
  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/vudovn/ag-kit.git /tmp/ag-kit
mkdir -p .claude/skills
cp -r /tmp/ag-kit/.agents/skills/context-compression .claude/skills/context-compression
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context Compression 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 Context Compression 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 Context Compression 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.

Context Compression — Long Session Management

Keep sessions productive by compressing completed work while preserving key decisions.

Overview

Long sessions (30+ turns) cause context degradation — the AI loses track of earlier work, repeats itself, or forgets decisions. Context compression proactively summarizes completed phases so the context window stays focused on active work.

Token Impact: Recovers 5,000-15,000 tokens in long sessions by replacing verbose tool outputs with semantic summaries.


When to Compress

SignalAction
Session has 20+ turnsConsider proactive compression
Agent repeats earlier suggestionsContext is saturated — compress now
User says "we already discussed this"Compress immediately
Switching to a new phase of workCompress the completed phase
Large tool output (500+ lines)Micro-compact the output

Compression Levels

Level 1: Micro-Compact (Tool Output)

Compress individual tool outputs while retaining semantic content:

❌ Before (raw grep output — 200 lines, ~4,000 tokens):
src/auth/jwt.ts:15: import { verify } from 'jsonwebtoken'
src/auth/jwt.ts:23: export function validateToken(token: string) {
src/auth/jwt.ts:24:   try {
src/auth/jwt.ts:25:     const decoded = verify(token, SECRET)
... (195 more lines)

✅ After (micro-compact — 5 lines, ~100 tokens):
Grep results for "jwt": Found 8 files, 42 matches.
Key files: src/auth/jwt.ts (main JWT logic), src/middleware/auth.ts (middleware),
src/api/login.ts (token creation). Token validation at jwt.ts:23-40.
Error handling at jwt.ts:42-55. Secret loaded from env at jwt.ts:8.

Level 2: Phase Summary

Replace a completed work phase with a summary:

❌ Before (full research transcript — ~3,000 tokens):
[turn 1] Read package.json...
[turn 2] Read src/index.ts...
[turn 3] Grep for "auth"...
[turn 4] Found 8 files related to auth...
[turn 5] Read src/auth/jwt.ts...
... (10 more turns of exploration)

✅ After (phase summary — ~200 tokens):
## Research Phase Complete
- Project: Next.js 15 app with JWT auth
- Auth files: 8 files in src/auth/, src/middleware/, src/api/
- Token flow: login → create JWT → store in httpOnly cookie → validate in middleware
- Bug location: src/auth/jwt.ts:45 — expiry check uses `<` instead of `<=`
- Decision: Fix the comparison operator, add edge case test

Level 3: Session Checkpoint

Full session summary for long-running work:

markdown
## Session Checkpoint (Turn 35)

### Completed
- [x] Researched auth system (8 files, JWT flow mapped)
- [x] Fixed token expiry bug in jwt.ts:45
- [x] Added edge case test in jwt.test.ts
- [x] Verified: all 42 tests passing

### In Progress
- [ ] Update API documentation
- [ ] Review related middleware

### Key Decisions
1. Keep httpOnly cookies (not localStorage) for token storage
2. Use `<=` for expiry check to include exact-moment expiry
3. Add 5-minute grace period for clock skew

### Files Modified
- src/auth/jwt.ts (line 45: comparison fix)
- tests/auth/jwt.test.ts (added 3 edge case tests)

Compression Protocol

Step 1: Identify Completed Phases

What work is DONE and won't be revisited?
→ Research findings already synthesized
→ Implementation already verified
→ Decisions already made and applied

Step 2: Extract Key Information

From the completed phase, preserve:
✅ Decisions made and WHY
✅ File paths and line numbers of changes
✅ Key findings that inform ongoing work
✅ Error messages or test results (summarized)

Discard:
❌ Step-by-step tool invocation details
❌ Full file contents that were read
❌ Exploratory dead-ends that didn't lead anywhere
❌ Verbose error stack traces (keep the message only)

Step 3: Write Summary

Use the Phase Summary format above.
Keep to 100-300 tokens per completed phase.
Include enough detail to resume work without re-reading.

Best Practices

  1. Compress phases, not facts — Individual decisions should stay, full transcripts should go
  2. Preserve "why" over "what" — Why a decision was made matters more than the exact commands run
  3. Never auto-compress — Always tell the user "I'm summarizing the completed research phase to keep context focused"
  4. Keep file references — Always preserve file paths and line numbers in summaries
  5. Checkpoint on phase transitions — Natural compression point when switching from research to implementation

Frequently asked questions

What does the Context Compression AI skill do?

Manage and compress conversation context in long sessions. Detect when context is growing large, summarize completed work phases, archive old findings while preserving key decisions. Prevents context degradation.

Why use Context Compression on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vudovn/ag-kit/tree/main/.agents/skills/context-compression. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Context Compression?

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 Context Compression?

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

Is the Context Compression AI skill free?

Yes. It is published on GitHub by vudovn 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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