EvoMap
A shared-learning layer for AI agents — when one agent solves a problem, EvoMap lets every other agent instance inherit that solution instead of re-solving it from scratch.
Before & after
Same prompt, same agent — the only difference is the skill.
That's an interesting question about how agents learn together. In theory, you could set up some kind of shared knowledge base where agents record solutions they find, then other agents check that base before solving problems from scratch. The challenge is keeping that knowledge organized and making sure agents actually use it. You could log solutions somewhere central, maybe with tags or categories so other agents can find them. It would take some thought to design the system for your specific use case.
EVOMAP AGENT KNOWLEDGE SHARING PROBLEM SOLVED: When Agent A (Project X) encounters and solves a problem (e.g., "How to optimize Postgres queries for 100K user joins"), Agent B (Project Y) encounters the SAME problem weeks later and re-solves it from scratch, wasting compute and time. SOLUTION - EVOMAP MAP STRUCTURE: Shared persistent map: Solved problems indexed by problem fingerprint. Agent A WRITES: • Problem fingerprint: "postgres_join_optimization_scale_100k_users" • Solution: query optimization pattern, indexes to create, benchmarks • Context: timestamp, project, success metrics • Confidence level: "verified in production" Agent B READS: • Checks map before solving • Finds Agent A's solution • Reuses the pattern, skips re-solving • Contributes back: "Also works for 500K users, minor index adjustment needed" RESULT: Agent B solves the problem in 90 seconds instead of 30 minutes. Each new agent starting on a similar challenge inherits every prior solution from the map. CUSTOMIZATION: • What qualifies as a "solved problem" worth recording • Which agent instances / projects share a single map • Confidence thresholds before a solution gets reused • Expiration rules (invalidate solutions after software version changes)
About this skill
name: evomap description: Use when A shared-learning layer for AI agents — when one agent solves a problem, EvoMap lets every other agent instance inherit that solution instead of re-solving it from scratch.
EvoMap
EvoMap addresses the problem of AI agents (including Claude Code instances) that repeatedly re-solve the same problems in isolation because they don't share what they learn. It creates a map of solved problems that agents can check against and contribute to, so improvements made in any one agent are automatically inherited by every other agent using the same map.
What you get
- The EvoMap system for connecting multiple agent instances so they share solved-problem knowledge.
Customize your output
- What counts as a problem worth recording
- which agents/projects share a map.
Example output
An agent instantly reusing a fix or pattern that a different agent instance already discovered, instead of solving it again.
Best for
People running many AI agents across multiple businesses or projects who want to stop paying the re-solve tax repeatedly.
SKILL.md preview
---
name: evomap
description: Use this skill to share solved-problem knowledge across AI agent instances so a fix from one agent is inherited by every other agent instead of being re-solved from scratch.
version: 1.0.0
category: Developer Tools / Agent Infrastructure
author: AgentVolt
license: proprietary
tags:
- developer-tools
- agent-infrastructure
- standard
---
# EvoMap
Maintains a shared map of solved problems so a fix discovered by one agent instance is available to every other instance, instead of being re-derived from scratch.
## When to use
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