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Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns.
Passed 19 of 21 test cases with the skill, and 8 of 21 without it.
and 12 more in the full report
13 fixed7 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
How many consecutive tool execution failures during a single session cause the automated memory hook to log a failure pattern?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies that 4 tool failures trigger the learning. | 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 (one step):
cp -r hooks/continual-learning .github/hooks/
Auto-initializes on first session. No config needed.
Global (~/.copilot/learnings.db) — follows you across all projects:
Local (.copilot-memory/learnings.db) — stays with this repo:
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
test cases passed, vs 8 / 21 without
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