Skill: nileader/keep-learning (version 0.0.2)

--- name: keep-learning description: "Learn and memorize knowledge from local directories. Supports Markdown and code files. Extracts key insights, builds knowledge index, and stores in agent memory. Trigger with '持续学习知识' or 'keep learning'." license: MIT-0 compatibility: "Requires local filesystem access and git (optional, for auto-pull)." metadata: author: nileader version: "0.0.2" repository: https://github.com/nileader/keep-learning --- # Keep Learning Learn knowledge from local directories and store it in agent memory for future reference. ## When to Use This Skill Activate this skill when user says: - "持续学习知识" - "keep learning" - "learn knowledge base" - "学习知识库" ## Supported File Formats (v0.0.1) | Format | Extensions | Support | |--------|------------|---------| | Markdown | .md, .markdown | Full | | Python | .py | Full | | JavaScript/TypeScript | .js, .ts, .jsx, .tsx | Full | | Java | .java | Full | | Go | .go | Full | | Rust | .rs | Full | | C/C++ | .c, .cpp, .h, .hpp | Full | | Shell | .sh, .bash, .zsh | Full | | YAML/JSON/TOML | .yaml, .yml, .json, .toml | Full | | SQL | .sql | Full | | Other text | .txt, .csv | Full | Not supported in v0.0.1: PDF, Word, Excel, PowerPoint, Keynote, audio, video files. ## Three-Layer Knowledge Architecture | Layer | Storage | Content | Purpose | |-------|---------|---------|---------| | L1 Core Memory | Agent Memory | Key conclusions, core concepts, decisions | Auto-surface in daily conversations | | L2 Knowledge Index | Agent Memory | File paths, summaries, keyword mappings | Know where knowledge lives | | L3 Source Files | Local filesystem | Complete original content | Deep-dive when needed via read_file | How It Works: 1. Daily conversations: L1 memories automatically appear in memory_overview 2. Need more detail: Query L2 index to find relevant files 3. Deep investigation: Use read_file to access L3 source files ## Runtime Data Directory All runtime data is stored in ~/.keep-learning/: | File | Purpose | |------|---------| | last-commit | Git commit hash of last learning session | | config.json | User configuration (knowledge base path, etc.) | ## Learning Workflow ### Step 1: Get Configuration First, search memory (category: project_environment_configuration) for an existing knowledge base path. - If found: confirm the path with the user before proceeding. Example: "Found your knowledge base at `~/knowledge/work-assistant`. Start learning from there?" - If NOT found: **stop and ask the user** to provide the knowledge base path before doing anything else. Do NOT proceed until the user provides a valid path. Example: "Please provide the path to your knowledge base directory (e.g., ~/knowledge/work-assistant)." Once confirmed, store the path in memory using update_memory with category project_environment_configuration. ### Step 2: Git Pull (If Applicable) Check if knowledge base is a git repository and pull latest changes before learning. ### Step 3: Scan Files Scan for supported files. Exclude: .git, node_modules, .obsidian, __pycache__, .venv ### Step 4: Detect Changes (Incremental Learning) For git repositories, detect ALL types of changes: 1. **Committed changes**: Compare current HEAD with last-commit hash stored in `~/.keep-learning/last-commit` using `git diff <last-commit> HEAD --name-only` 2. **Uncommitted changes**: Detect modified/added files in working directory using `git status --porcelain` Combine both results to get the full list of changed files. This ensures learning happens even when: - Remote has no updates, but local files were edited - Local commits exist that haven't been pushed yet - Files are modified but not yet committed After learning completes, update `~/.keep-learning/last-commit` with current HEAD hash. For non-git directories: scan all supported files (no incremental detection). ### Step 5: Read and Extract Knowledge For each file: read content, identify theme/concepts/conclusions, extract key knowledge. ### Step 6: Store L1 Core Memory Create L1 memory entries using update_memory with appropriate category: - expert_experience: Domain expertise, best practices - project_introduction: Project/product overviews - learned_skill_experience: Reusable methods, procedures Title format: [Domain] Concise Topic Description ### Step 7: Build L2 Knowledge Index Create knowledge index with file path, theme, keywords mappings. ### Step 8: Generate Learning Report Output: Timestamp, Statistics, L1 Memories list, L2 Index summary, Notes. ## Memory Deduplication Before creating: search_memory first. If exists, update; if not, create. ## Quick Reference | Situation | Action | |-----------|--------| | First time user | Ask for knowledge base path | | Git repo detected | Run git pull before scanning | | Large file | Read in chunks, summarize each section | | Duplicate knowledge | Update existing memory | | Unsupported file | Skip and note in report | ## Limitations (v0.0.1) - Only Markdown and code files supported - No PDF/Word/Excel/PPT support - Memory entries have size limits


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)