Stop Losing Context Between AI Sessions: Introducing the Archive Skill

By OPC Team | February 23, 2026 | 7 min read

TL;DR — Every AI coding session generates valuable knowledge: debugging solutions, deployment steps, configuration tricks. But when the session ends, that knowledge vanishes. The new Archive skill for OPC Skills solves this by creating indexed, searchable markdown archives in your project. Your AI agent can write session learnings to .archive/ and consult them in future sessions. Install in 30 seconds with npx skills add ReScienceLab/opc-skills --skill archive. No API keys required.

The Problem: AI Agents Have No Long-Term Memory

You've been there. You spend 45 minutes debugging a CloudWatch logging issue with Claude Code. You find the fix — a specific IAM permission plus a log group naming convention. Session ends.

Three weeks later, you hit the exact same issue. Your AI agent has no memory of the previous session. You start from scratch.

This is the context amnesia problem. According to a 2025 GitHub survey, developers using AI coding assistants report that losing session context is one of their top 3 frustrations with AI tools.

The root cause is simple: AI coding sessions are stateless by design. Claude Code, Cursor, Droid — they all start fresh each session. Your hard-won debugging knowledge, deployment procedures, and configuration decisions evaporate when the session ends.

What is the Archive Skill?

The Archive skill gives your AI agent a structured knowledge base it can write to and read from across sessions.

It works with any AI coding tool that supports skills: Claude Code, Cursor, Droid, OpenCode, Codex, and 12+ other platforms.

How It Works

After a significant task — your agent writes a concise markdown file to .archive/YYYY-MM-DD/
Index updated — .archive/MEMORY.md gets a one-line entry linking to the archive
Next session — your agent reads MEMORY.md first, finds relevant past solutions
Knowledge compounds — over weeks, your project builds a searchable knowledge base

Directory Structure

.archive/
├── MEMORY.md                          # Master index
├── 2026-02-20/
│   ├── cloudwatch-logging.md          # Specific solution
│   └── ecs-deploy-fix.md              # Another solution
├── 2026-02-23/
│   └── github-actions-cache.md        # Today's learnings

Each archive file has YAML frontmatter with tags, categories, and related entries — making it easy to search with grep -ri "keyword" .archive/.

When to Use the Archive Skill

The skill is designed around natural triggers. Your AI agent activates it when:

Trigger	Example
"Archive this"	After solving a tricky bug
"Save learnings"	After a multi-step deployment
"Session notes"	At the end of a productive session
"Check archives"	Before debugging a familiar problem
"Past solutions"	When encountering a recurring error

The agent can also proactively suggest archiving when it detects a significant task completion.

Real-World Use Cases

1. Debugging Knowledge Base

You resolve a CORS issue that required 3 specific configuration changes across API Gateway, CloudFront, and your Express server. Instead of losing that knowledge:

---
tags: [cors, api-gateway, cloudfront, express]
category: debugging
related: [2026-01-15/api-gateway-setup.md]
---

# CORS Fix: API Gateway + CloudFront + Express

## Problem
403 errors on preflight OPTIONS requests...

## Solution
1. API Gateway: Add OPTIONS method with mock integration
2. CloudFront: Whitelist Origin and Access-Control headers
3. Express: Use cors() middleware with explicit origin

Next time CORS breaks, your agent reads MEMORY.md, finds this entry, and applies the fix in minutes instead of hours.

2. Deployment Procedures

Every deployment has subtle steps that are easy to forget. Archive them:

---
tags: [deploy, ecs, staging, production]
category: release
---

# ECS Deployment: Staging → Production

## Steps
1. Build: docker build -t app:$(git rev-parse --short HEAD) .
2. Push: aws ecr push ...
3. Update task definition (increment revision)
4. Important: wait for service stability before proceeding
5. Health check: curl https://api.example.com/health

3. Configuration Decisions

Why did you choose that particular database index? Why is the rate limit set to 100/min? Archive the reasoning:

---
tags: [postgres, indexing, performance]
category: infrastructure
---

# PostgreSQL Index Strategy for Orders Table

## Decision
Composite index on (user_id, created_at DESC) instead of separate indexes.

## Reasoning
- Query pattern: always filter by user_id + sort by created_at
- Composite index serves both WHERE and ORDER BY in one scan
- Benchmarked: 12ms vs 89ms on 1M rows

Installation

30-Second Install

npx skills add ReScienceLab/opc-skills --skill archive

That's it. No API keys, no configuration, no dependencies.

Verify Installation

Ask your AI agent: "Do you have access to the archive skill?" or simply say "archive this" after completing a task.

Supported Platforms

The Archive skill works with all major AI coding tools:

Platform	Install Command
Claude Code	`npx skills add ReScienceLab/opc-skills --skill archive -a claude`
Cursor	`npx skills add ReScienceLab/opc-skills --skill archive`
Factory Droid	`npx skills add ReScienceLab/opc-skills --skill archive -a droid`
OpenCode	`npx skills add ReScienceLab/opc-skills --skill archive -a opencode`
All tools	`npx skills add ReScienceLab/opc-skills --skill archive`

Archive Skill vs. Other Approaches

Approach	Persistent	Searchable	Structured	Works Offline	Cost
Archive Skill	Yes	Yes	Yes	Yes	Free
Session history	No	No	No	N/A	Free
Manual notes	Yes	Somewhat	No	Yes	Free
Notion/Docs	Yes	Yes	Somewhat	No	$8-10/mo
Vector DB (RAG)	Yes	Yes	No	No	$20+/mo

The Archive skill is the simplest approach that actually works. Plain markdown files, version-controlled, searchable with grep, readable by any AI agent.

How It Integrates with Other OPC Skills

The Archive skill complements the entire OPC Skills ecosystem:

After SEO-GEO audit → Archive the optimization steps and before/after metrics
After domain-hunter search → Archive the registrar comparison and final decision
After logo-creator session → Archive the style decisions and prompt that worked
After requesthunt research → Archive key findings and user demand patterns

Each skill session produces knowledge worth preserving.

FAQ

How is this different from just keeping a `NOTES.md` file?

The Archive skill provides structure: YAML frontmatter for tags and categories, a master index (MEMORY.md), date-organized directories, and templates. More importantly, your AI agent knows how to read and write to it automatically — you don't have to maintain it manually.

Does the archive get committed to git?

No. .archive/ should be in your .gitignore — these are local project notes, not source code. The skill enforces this convention. If you want to share archives across a team, you can override this, but the default is local-only.

What happens when the archive gets large?

The MEMORY.md index keeps things navigable. It's a one-line-per-entry index organized by category. Your agent reads the index first (small file), then fetches specific entries as needed. Even with hundreds of entries, lookups are fast.

Can I use this with multiple AI tools on the same project?

Yes. The archive is plain markdown in a standard directory structure. Claude Code, Cursor, Droid — they all read the same .archive/ directory. Knowledge created by one tool is available to all others.

Does it work with the Claude Code marketplace?

You can install via the marketplace, but we recommend using npx skills add for the most reliable installation. See our installation guide for details.

What categories does the archive support?

Five built-in categories: infrastructure (AWS, networking), release (versioning, deploys), debugging (bug fixes, error resolution), feature (implementation notes), and design (UI/UX decisions). You can add custom categories as needed.

Getting Started

Install the skill:

npx skills add ReScienceLab/opc-skills --skill archive

Complete a task in your next AI session
Say "archive this" — your agent creates the archive entry automatically
Next session, say "check archives for [topic]" — your agent finds relevant past solutions

Your project knowledge compounds over time. Every debugging session, every deployment, every configuration decision — preserved and searchable.