Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.
npx skills add https://github.com/microsoft/skills --skill continual-learning
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
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
For human-readable, version-controlled knowledge:
# .copilot-memory/conventions.md
- Use DefaultAzureCredential for all Azure auth
- Parameter is semantic_configuration_name=, not semantic_configuration=
Learnings decay over time:
This prevents unbounded growth while preserving what matters.
cp -r, it won't get adopted"Use semantic_configuration_name=" beats "use the right parameter"Skill converted from mcp-deploy-manage-agents.prompt.md
Use this skill when the user wants to launch a new AltClaw, OpenClaw, PicoClaw, or Ottie deployment through Cloud Claw. Covers the same user-facing fields and constraints exposed in the Cloud Claw UI, using the local altllm cloud-claw-* commands. Do NOT use for post-launch lifecycle tasks like start/stop/delete/logs; use cloud-claw-manage-vm.
Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents in Azure AI Foundry.
Build MCP (Model Context Protocol) servers on Cloudflare Workers with tools, resources, and prompts.
Chain agent outputs as inputs in sequential or parallel pipelines for data flow orchestration
Audit cloned or reimplemented websites for fidelity gaps, tracking scripts, source-brand and language residue, placeholders, and risky external dependencies. Use before handoff or deployment, or when asked to review a website clone for cleanup and readiness.
> Install and operate Hermes Tweet, a Hermes Agent plugin for X/Twitter research, timeline reading, tweet analysis, and approval-gated tweet actions. Use this skill when installing Hermes Tweet, researching X/Twitter accounts, monitoring launch signals, investigating mentions, auditing giveaways, or preparing guarded tweet actions. Use proactively when a Hermes Agent workflow needs current X/Twitter context. Requires XQUIK_API_KEY for read and action tools.
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Take microsoft/continual-learning from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.