[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FDAWW7PDFT12DE5WM8ER7C":3,"skill-same:01M6FDAWW7PDFT12DE5WM8ER7C":47},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":6,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M6FDAWW7PDFT12DE5WM8ER7C","continual-learning",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fmicrosoft\u002Fskills\u002Fcontinual-learning\u002FSKILL.md",[],[18,19],"continual learning","learning","2026-11-10 10:12:30",false,"# Continual Learning for AI Coding Agents\n\nYour agent forgets everything between sessions. Continual learning fixes that.\n\n## The Loop\n\n```\nExperience → Capture → Reflect → Persist → Apply\n     ↑                                       │\n     └───────────────────────────────────────┘\n```\n\n## Quick Start\n\nInstall the hook (one step):\n```bash\ncp -r hooks\u002Fcontinual-learning .github\u002Fhooks\u002F\n```\n\nAuto-initializes on first session. No config needed.\n\n## Two-Tier Memory\n\n**Global** (`~\u002F.copilot\u002Flearnings.db`) — follows you across all projects:\n- Tool patterns (which tools fail, which work)\n- Cross-project conventions\n- General coding preferences\n\n**Local** (`.copilot-memory\u002Flearnings.db`) — stays with this repo:\n- Project-specific conventions\n- Common mistakes for this codebase\n- Team preferences\n\n## How Learnings Get Stored\n\n### Automatic (via hooks)\nThe hook observes tool outcomes and detects failure patterns:\n```\nSession 1: bash tool fails 4 times → learning stored: \"bash frequently fails\"\nSession 2: hook surfaces that learning at start → agent adjusts approach\n```\n\n### Agent-native (via store_memory \u002F SQL)\nThe agent can write learnings directly:\n```sql\nINSERT INTO learnings (scope, category, content, source)\nVALUES ('local', 'convention', 'This project uses Result\u003CT> not exceptions', 'user_correction');\n```\n\nCategories: `pattern`, `mistake`, `preference`, `tool_insight`\n\n### Manual (memory files)\nFor human-readable, version-controlled knowledge:\n```markdown\n# .copilot-memory\u002Fconventions.md\n- Use DefaultAzureCredential for all Azure auth\n- Parameter is semantic_configuration_name=, not semantic_configuration=\n```\n\n## Compaction\n\nLearnings decay over time:\n- Entries older than 60 days with low hit count are pruned\n- High-value learnings (frequently referenced) persist indefinitely\n- Tool logs are pruned after 7 days\n\nThis prevents unbounded growth while preserving what matters.\n\n## Best Practices\n\n1. **One step to install** — if it takes more than `cp -r`, it won't get adopted\n2. **Scope correctly** — global for tool patterns, local for project conventions\n3. **Be specific** — `\"Use semantic_configuration_name=\"` beats `\"use the right parameter\"`\n4. **Let it compound** — small improvements per session create exponential gains over 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