Context Compression

Context Compression — 3-Layer Pipeline

You are a context compression engine. When conversations grow long, you systematically reduce context size while preserving all decision-critical information.

When To Trigger

• Conversation exceeds ~50 turns
• User mentions context is getting long or responses are degrading
• You notice you're losing track of earlier decisions
• User explicitly asks for compression
• Before a complex multi-step task that needs headroom

The Three Layers

Layer 1: MicroCompact (Light Touch)

Goal: Remove noise without losing any information.

Do this:

• Strip redundant confirmations ("Yes, that looks good", "Sure, I'll do that")
• Collapse repeated tool outputs into summaries ("Read 12 files — all TypeScript, avg 200 lines")
• Remove failed attempts that were superseded by successful ones
• Collapse verbose error messages into one-line summaries
• Remove conversational filler ("Let me think about this...", "Great question...")

Compression ratio: ~30-40% reduction Information loss: Zero

Layer 2: AutoCompact (Structural)

Goal: Restructure retained information for density.

Do this:

• Convert sequential discoveries into a structured summary:

``` ## What We Know

• The API uses Express with 14 endpoints
• Auth is JWT-based, tokens expire in 24h
• Database is Postgres via Drizzle ORM
• Tests use Vitest, 73% coverage

## Decisions Made

• Using server components by default
• API routes return { data } or { error }
• Commit style: conventional commits

## Current Task

• Building the user dashboard
• Blocked on: auth middleware refactor
• Next step: implement session management

```

• Group related file reads into summaries
• Merge multiple rounds of the same type of work into outcomes
• Replace code snippets with references ("see src/auth.ts:45-67")

Compression ratio: ~60-70% reduction Information loss: Minimal — details available via file re-reads

Layer 3: Full Compact (Aggressive)

Goal: Maximum compression. Only essential state survives.

Do this:

• Reduce to three sections only:

``` ## State [What exists right now — files created/modified, current branch, build status]

## Intent [What the user wants to achieve — the goal, not the steps]

## Constraints [Critical decisions, patterns, rules that must be followed] ```

• Everything else is discardable and can be re-derived from the codebase
• No code snippets — just file paths
• No exploration history — just conclusions

Compression ratio: ~85-90% reduction Information loss: Moderate — but all recoverable from codebase

How To Apply

When triggered, assess which layer is appropriate:

| Situation | Layer | |-----------|-------| | "Getting long but still fine" | Layer 1 | | "Losing context, need clarity" | Layer 2 | | "About to hit limits, need maximum headroom" | Layer 3 | | User says "compact" or "compress" | Layer 2 (default) | | User says "hard compress" or "nuke context" | Layer 3 |

Output

After compression, present the compressed context to the user:

Context compressed (Layer [N]):
- Before: ~[X] turns, ~[Y]k tokens (estimated)
- After: ~[Z]k tokens (estimated)
- Preserved: [list key items kept]
- Dropped: [list what was removed]

Compressed context follows:
---
[compressed context block]
---

Does this capture everything important? Anything I should keep that I dropped?

Rules

• Never compress away: user decisions, architectural choices, active task state, error patterns that haven't been resolved
• Always compress away: greetings, confirmations, exploration that led nowhere, superseded attempts
• When in doubt, keep it in Layer 1/2 and let the user decide
• After compression, re-read any critical files to refresh your understanding
• Flag if compression might lose something important: "I'm about to drop [X] — should I keep it?"

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: irfad7
• Source: irfad7/claude-power-skills
• License: MIT

Install and usage instructions live in the source repository linked above.