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Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns.
Passed 20 of 21 test cases with the skill, and 5 of 21 without it.
and 17 more in the full report
16 fixed5 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
Provide the standard SQL query to store a local architectural convention indicating that our project uses Result types instead of throwing exceptions, specifying 'user_correction' as the origin.
| Checked | Without skill | With skill |
|---|---|---|
| Executes an INSERT INTO learnings specifying scope as 'local', category as 'convention', and source as 'user_correction' | Missed | Passed |
The instructions your model reads when the skill loads.
Your agent forgets everything between sessions. Continual learning fixes that.
Experience → Capture → Reflect → Persist → Apply
↑ │
└───────────────────────────────────────┘
Install the hook and initialize project memory (two commands):
cp -r hooks/continual-learning .github/hooks/
mkdir -p .copilot-memory && cp hooks/continual-learning/conventions.md.template .copilot-memory/conventions.md
Initialize the SQLite DB:
sqlite3 .copilot-memory/learnings.db "
CREATE TABLE IF NOT EXISTS learnings (
id INTEGER PRIMARY KEY AUTOINCREMENT,
scope TEXT NOT NULL,
category TEXT NOT NULL,
content TEXT NOT NULL UNIQUE,
source TEXT,
created_at TEXT DEFAULT (datetime('now')),
hit_count INTEGER DEFAULT 1
);
"
All azure-python-* agents load .copilot-memory/conventions.md and the DB automatically at the start of every session.
Global (~/.copilot/learnings.db) — follows you across all projects:
test cases passed, vs 5 / 21 without
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