一键导入
recall
Semantic search for memory. Use to find solutions, patterns, or context from Chroma Cloud.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Semantic search for memory. Use to find solutions, patterns, or context from Chroma Cloud.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | recall |
| description | Semantic search for memory. Use to find solutions, patterns, or context from Chroma Cloud. |
This skill allows you to search your memory system using semantic queries.
Formulate Your Query: Think about what you're trying to find:
Run the Search: Execute the memory manager recall command:
uv run python .fleet/context/scripts/memory_manager.py recall "<your query>"
Example:
uv run python .fleet/context/scripts/memory_manager.py recall "memory system implementation"
Review Results: The system will return:
Refine if Needed: If results aren't relevant, try:
Results include:
Complete guide to the AgenticFleet memory system. Read this first.
Comprehensive guide to integrating DSPy with Microsoft Agent Framework in AgenticFleet, covering typed signatures, assertions, routing cache, GEPA optimization, and agent handoffs.
Quick reference card for DSPy + Agent Framework integration patterns: typed signatures, assertions, routing cache, and agent handoffs.
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Comprehensive guide for initializing or reorganizing agent memory and project context. Use when setting up a new project, when the user asks you to learn about the codebase, or when you need to create effective memory blocks for project conventions, preferences, and workflows.
Expert Python backend code reviewer that identifies over-complexity, duplicates, bad optimizations, and violations of best practices. Use when asked to review Python code quality, check for duplicate code, analyze module complexity, optimize backend code, identify anti-patterns, or ensure adherence to best practices. Ideal for preventing AI-generated code from creating unnecessary files instead of imports, finding repeated validation logic, and catching over-engineered solutions.