Guide · Agent skill

How to use the Context Compression Strategies agent skill

Updated 2026-10-29 · written with AI from the project's own documentation and checked against our catalogue data

The context-compression skill teaches an AI agent how to shrink a long conversation history without losing the details it needs to finish a task, such as which files it changed, what it decided, and what comes next. It is aimed at developers running long coding agent sessions, teams writing handoff summaries between agents, and people building evaluation pipelines for summarization. After this guide you will know what the skill changes about the agent's behaviour, how to install it, how to trigger it, and what to check before relying on it.

Context Compression Strategiesmuratcankoylan/Agent-Skills-for-Context-Engineering · MIT Download the skill

Quick start

1Download the ZIP. Get the skills/context-compression ZIP from this directory page.
2Unzip into a skills folder. Extract it to ~/.claude/skills/ for personal use or to .claude/skills/ inside a project.
3Or upload in the Claude apps. Go to Settings → Capabilities → Skills and upload the skill there instead.
4Describe a long-session problem. Ask in plain language, for example about an agent forgetting files it edited or needing a handoff summary.
5Use the structured summary. Ask the agent to populate fixed sections such as Files Modified, Decisions Made and Next Steps.
6Test with probes (optional). Run the Python evaluator and its tests after implementing the LLM judge calls yourself.

What an agent skill is and what this one changes

An agent skill is a folder of instructions, and optionally scripts and reference files, that an AI agent loads when a task matches the skill's description. This one is defined in a SKILL.md file with the description: this skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries.

When it applies, the agent follows a specific approach to compression. It uses structured summaries with fixed sections such as session intent, files modified, decisions made, current state and next steps. When compression triggers, it summarizes only the newly truncated span and merges that into the existing summary instead of regenerating everything. It also judges compression by total tokens needed to finish a task, including re-fetching costs, rather than by tokens per request.

Artifact trail: keeps full file paths, changed functions and error messages, and recommends a separate artifact index in agent scaffolding.
Trigger choice: recommends a sliding window with structured summaries as the default for coding agents.
Evaluation: uses probe questions (recall, artifact, continuation, decision) rather than ROUGE or embedding similarity.

Install the context-compression skill

Download the ZIP for skills/context-compression from the directory page and unzip it. For a personal install, place the folder in ~/.claude/skills/. For a single project, place it in that project's .claude/skills/ folder.

In the Claude apps you can instead upload the skill under Settings → Capabilities → Skills.

Personal: ~/.claude/skills/context-compression
Project: .claude/skills/context-compression inside your project folder

Trigger the skill with natural requests

You do not need a special command. The skill activates when your request matches its description. Describe the problem in plain language, for example that the agent has lost track of files it edited, or that you need a summary to hand off to another agent.

The SKILL.md also lists situations where the skill should not be used, such as general token-efficiency tactics (handled by context-optimization) and long-term memory across sessions (handled by memory-systems).

"My coding agent keeps forgetting which files it changed. Help me design a summary that prevents this."
"Write a handoff summary that preserves decisions, files, risks and next actions so another agent can continue."
"Set up a way to test whether my compressed summaries still answer recall and artifact questions."

Use the structured summary template

The skill recommends summaries with mandatory sections. Each section works as a checklist, so an omission is visible instead of silent. Adapt the section names to your domain: a debugging agent might use Root Cause and Error Messages, while a migration agent might use Source Schema and Target Schema.

## Session Intent
[What the user is trying to accomplish]

## Files Modified
- auth.controller.ts: Fixed JWT token generation
- config/redis.ts: Updated connection pooling
- tests/auth.test.ts: Added mock setup for new config

## Decisions Made
- Using Redis connection pool instead of per-request connections
- Retry logic with exponential backoff for transient failures

## Current State
- 14 tests passing, 2 failing
- Remaining: mock setup for session service tests

## Next Steps
1. Fix remaining test failures
2. Run full test suite
3. Update documentation

Run the bundled evaluator and its tests

The package includes scripts/compression_evaluator.py and tests/test_compression_evaluator.py, which use Python 3 and its unittest tests. The evaluator generates probe questions to check whether a compressed summary still preserves key facts. Its reference guide, references/evaluation-framework.md, describes the probe types, scoring rubrics and LLM judge configuration.

Important: the bundled LLM judge calls are stubs for demonstration. You must implement them, using an LLM API, before using the evaluator for real quality checks.

Requires Python 3.
Real judge calls need an LLM API that you connect yourself.

Safety notes: scripts and network

This skill runs scripts, so read compression_evaluator.py before running it. According to the package details, it does not use the network. Real judge calls would need an LLM API, which is a separate step you configure, and the bundled stubs do not make them.

Keep the evaluator out of production gating until you have replaced the stubs and checked the results on your own sessions.

Runs scripts: yes
Uses network: no (as listed for this package)
Do not rely on the bundled evaluator as-is for production quality checks.

Tips for getting good results

Measure success by re-fetching: if the agent keeps re-reading files it already processed, compression is too aggressive. Keep a separate artifact index rather than relying on the summarizer to capture every file path. Use task-boundary triggers when your session has clear phases such as research, implementation and testing.

Track how often the agent re-reads files as your main quality signal.
Include function names, not only file names, in the modified-files section.
Skip compression for short chats where it only adds overhead.

What people use it for

Keeping a long coding session on track so the agent remembers which files it modified and how.
Writing a handoff summary that another agent or teammate can pick up without re-reading everything.
Comparing summarization methods in an evaluation pipeline using probe questions.
Deciding when to trigger compression, such as a sliding window for coding agents or task boundaries for phased work.

Frequently asked questions

How do I install the context-compression skill?

Download the ZIP for skills/context-compression from the directory page and unzip it into ~/.claude/skills/ for personal use or into a project's .claude/skills/ folder. In the Claude apps, you can upload it under Settings → Capabilities → Skills instead.

What do I need to run it?

The bundled evaluator and its unittest tests need Python 3. Real LLM judge calls need an LLM API that you connect yourself, because the bundled judge calls are stubs that must be implemented for production use.

Is the context-compression skill free?

The package is listed under the MIT licence. This guide does not state any price, so check the directory page for current terms.

Does the skill use the internet?

According to the package details, it does not use the network. It does run scripts, so review scripts/compression_evaluator.py before running it.

Can I use the evaluator in production as it is?

No. Its LLM judge calls are stubbed for demonstration, so you must implement them before using it for real quality checks.

What should I use instead for long-term memory across sessions?

This skill does not cover that. The SKILL.md directs long-term memory across separate sessions to the memory-systems skill, and general token-saving tactics to context-optimization.

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Our verdict

This skill suits developers running long coding agent sessions that lose track of edited files, teams writing handoff summaries, and people building summarization evaluations. It is not worth the overhead for short chats, and it is not a memory system for work spanning separate sessions. Treat the bundled evaluator as a starting point until you have implemented its judge calls.

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