> continual-learning
Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.
curl "https://skillshub.wtf/microsoft/skills/continual-learning?format=md"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
- One step to install — if it takes more than
cp -r, it won't get adopted - Scope correctly — global for tool patterns, local for project conventions
- Be specific —
"Use semantic_configuration_name="beats"use the right parameter" - Let it compound — small improvements per session create exponential gains over weeks
> related_skills --same-repo
> skill-creator
Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.
> podcast-generation
Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.
> mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node/TypeScript (MCP SDK), or C#/.NET (Microsoft MCP SDK).
> github-issue-creator
Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.