Continual Learning for AI Coding Agents

SkillDocs & knowledge

Guides your agent through setting up continual learning with hooks, memory scoping, and reflection patterns.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Continual Learning for AI Coding Agents skill

About this capability

Orchestrate continual learning by delegating transcript mining and AGENTS.md updates to `agents-memory-updater`.

What this skill tells your AI

The instructions your AI receives, as published by barbieri/local-openfinance in .agents/skills/continual-learning/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
22
Forks
2
Last commit
Sep 2026

Others that do the same job

Advanced
Catalog kind
skill
Gateway key
continual-learning
Source
github.com/barbieri/local-openfinance