compact-memory-implementation SKILL.md skillA
compact-memory-implementation from simbajigege/book2skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add simbajigege/book2skills/compact-memory-implementation@git:20260524.a98558fPins git:20260524.a98558f in mdr.lock. Pin the fingerprint instead.
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- SKILL.md skill
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- simbajigege/book2skills
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skills/compact-memory-implementation/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260524.a98558f, committed May 24, 2026
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- License
- MIT
Developer implementation guide for adding compact memory to an Agent — covers fork agent pattern for compaction, trigger strategy, summary format design, and memory restoration in subsequent sessions. Use when a developer asks how to implement compact memory, context compression, or memory persistence in their agent built with Claude Agent SDK or Anthropic API.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# compact-memory-implementation
A developer guide for building compact memory into an Agent: detect when to compress, fork a compactor sub-agent, produce a structured summary, and restore it in the next session.
## Step 1 — Understand the setup
Before designing anything, clarify:
- **SDK / language**: Claude Agent SDK? Direct Anthropic API? Python or TypeScript?
- **Agent architecture**: single-agent loop, multi-agent, tool-calling?
- **Session model**: one long-running session or multiple short sessions?
- **What must survive compaction**: task state, decisions, tool results, conversation history?
This determines which pattern fits.
---
## Step 2 — When to trigger compact
Three strategies, pick based on your session model:
**1. Token threshold** (recommended)
Check `usage.input_tokens` from the previous response. When it exceeds ~70–80% of your model's context limit, trigger compact.
```python
COMPACT_THRESHOLD = 150_000 # adjust per model
if response.usage.input_tokens > COMPACT_THRESHOLD:
compact = compact_memory(history)
history = [] # reset — compact moves to system prompt
```
**2. Turn count**
…Read the whole file at its exact version.
How to install
mdr add simbajigege/book2skills/compact-memory-implementation@sha256:42bf48ed608ed9fbThe line at the top of this page pins the label git:20260524.a98558f, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_vrg2okginwd4a626)
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Versions
The registry has recorded 1 version of this file, committed on May 24, 2026.
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Source
simbajigege/book2skills · 202 stars · license MIT · pushed 2026-08-26 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_vrg2okginwd4a626 GET https://markdownregistry.com/api/v1/resolve?ref=simbajigege/book2skills/compact-memory-implementation GET https://markdownregistry.com/api/v1/blob/42bf48ed608ed9fb4251e1086beaca184eaf51dc5d7a43d4cbd1e08f7243a5b0
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.