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Continual Learning

MicrosoftOfficialPublished by Microsoft

  • Skill
  • Guidelines
  • Agent tooling
  • MIT
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From the author

Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to enabling continual learning for AI coding agents, ensuring they retain knowledge across sessions. It defines a two-tier memory architecture that separates global cross-project insights from local project-specific conventions. By utilizing hooks for automatic observation and providing methods for agent-native or manual memory storage, the framework allows agents to evolve their behavior based on past successes and failures while managing memory growth through automated compaction.

What it does

  • Establishes a two-tier memory system for global and local knowledge storage.
  • Provides hooks to automatically capture and reflect on tool outcomes.
  • Enables manual and agent-native storage of patterns, mistakes, and preferences.
  • Implements automated compaction to prune stale data and maintain memory efficiency.
  • Defines a lifecycle loop of experience, capture, reflection, persistence, and application.

When to use it

  • When you need your AI coding agent to remember project-specific conventions.
  • When you want to reduce repetitive errors across multiple agent sessions.
  • When setting up infrastructure to allow an agent to learn from its own tool usage history.
  • When managing cross-project coding preferences that should persist globally.

Advantages

  • Reduces redundant configuration by persisting learned preferences.
  • Improves agent reliability by surfacing past mistakes and successful patterns.
  • Prevents memory bloat through automated decay and pruning of low-value entries.
  • Supports both automated capture and explicit human-defined knowledge.

Usage examples

“How can I make my agent remember that this project uses specific authentication credentials?”
The agent can store this as a local learning entry, ensuring it applies the correct credential pattern in future sessions within that repository.
“My agent keeps failing with the same bash command, how do I stop this?”
By installing the provided hooks, the agent will detect the repeated failure pattern and adjust its future approach to avoid the problematic command.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/continual-learning/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip continual-learning.zip -d .agents/skills

Documentation by Microsoft

Continual Learning for AI Coding Agents

Your agent forgets everything between sessions. Continual learning fixes that.

The Loop

Experience → Capture → Reflect → Persist → Apply
     ↑                                       │
     └───────────────────────────────────────┘

Quick Start

Install the hook (one step):

cp -r hooks/continual-learning .github/hooks/

Auto-initializes on first session. No config needed.

Two-Tier Memory

Global (~/.copilot/learnings.db) — follows you across all projects:

  • Tool patterns (which tools fail, which work)

  • Cross-project conventions

  • General coding preferences

Local (.copilot-memory/learnings.db) — stays with this repo:

  • Project-specific conventions

  • Common mistakes for this codebase

  • Team preferences

How Learnings Get Stored

Automatic (via hooks)

The hook observes tool outcomes and detects failure patterns:

Session 1: bash tool fails 4 times → learning stored: "bash frequently fails"
Session 2: hook surfaces that learning at start → agent adjusts approach
Agent-native (via store_memory / SQL)

The agent can write learnings directly:

INSERT INTO learnings (scope, category, content, source)
VALUES ('local', 'convention', 'This project uses Result<T> not exceptions', 'user_correction');

Categories: pattern, mistake, preference, tool_insight

Manual (memory files)

For human-readable, version-controlled knowledge:

# .copilot-memory/conventions.md
- Use DefaultAzureCredential for all Azure auth
- Parameter is semantic_configuration_name=, not semantic_configuration=

Compaction

Learnings decay over time:

  • Entries older than 60 days with low hit count are pruned

  • High-value learnings (frequently referenced) persist indefinitely

  • Tool logs are pruned after 7 days

This prevents unbounded growth while preserving what matters.

Best Practices

  1. One step to install — if it takes more than cp -r, it won't get adopted

  2. Scope correctly — global for tool patterns, local for project conventions

  3. Be specific — "Use semantic_configuration_name=" beats "use the right parameter"

  4. Let it compound — small improvements per session create exponential gains over weeks

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/skills/continual-learning · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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AutoGineer is not affiliated with or endorsed by Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download continual-learning.zip