synthesis-context-lifecycle

Installation
SKILL.md

Context Lifecycle Management

The Problem

AI collaborators start every session with zero context. Their effectiveness depends entirely on the quality of the context they receive. For short-lived projects (2-3 sessions), a single context file works. For long-running projects spanning weeks or months, that file grows unboundedly — combining four types of information with fundamentally different lifecycles:

Information type Access pattern Growth pattern Ideal treatment
Working memory (current state, active tasks) Every session Constant Keep lean, refresh often
Episodic memory (session logs) Rarely after 1 week Unbounded append Archive monthly
Semantic memory (stable facts, reference) Most sessions Slow, update-in-place Separate file
Completed work records Almost never Unbounded append Delete after archiving

Combining all four in one file means the file grows linearly with session count, with no mechanism for information to leave. This is the classic hot/warm/cold data problem from database engineering, manifesting in AI context management.


The Architecture

Related skills
Installs
14
GitHub Stars
6
First Seen
Mar 20, 2026