| name | memory-project-context |
| description | Provide first-turn understanding of the ai-memory repository with minimal tokens. Always use this skill when user asks what this project does, asks for implementation entrypoints, requests architecture explanation, or when a subagent needs a narrow read plan for this repository. |
| metadata | {"keywords":["ai-memory overview","project introduction","architecture entrypoints","first-turn understanding"],"triggers":["what is this project","introduce this repo","项目是做什么的","关键入口在哪里"]} |
Memory Project Context
Use this skill as the first project-level semantic index before broad file reads.
Project Snapshot
- Project: ai-memory
- Primary goal: persistent developer memory layer for AI coding tools
- Core model: dynamic runtime memory over static snapshots
- Data source of truth: ~/.memory/profile.md, preferences.md, infra.md, projects/*.md
Fast Answers
When user asks for a quick intro, answer with:
- One sentence on project goal.
- One sentence on architecture split: ai-memory CLI, memory files, injection layer.
- One sentence on why dynamic context is preferred.
Entrypoints
ai-memory: command router, context rendering, inject modes
lib/learn.sh: memory update pipeline from LLM output
docs/core-idea.md: architecture principles
note.md: implementation progress and roadmap
.github/copilot-instructions.md: Copilot routing instructions generated by inject
Routing Rules
- If task is architecture or design rationale:
- Read
docs/core-idea.md then ai-memory.
- If task is command behavior or bug fix:
- Read
ai-memory first, then specific module files.
- If task is memory update flow:
- Read
lib/learn.sh and related helper functions in ai-memory.
- If task is token/context efficiency:
- Use
.github/skills/memory-token-audit/SKILL.md.
Minimal Read Plan For Subagents
- Start with only one file section matching the task.
- Expand to a second file only if the first cannot answer.
- Avoid loading full generated instruction snapshots unless user asks for full dump.
Output Contract
- Start with direct answer.
- Include only required files and commands.
- Explicitly call out whether recommendation changes token cost or not.