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Obsidian Long-Term Memory

AI Agent Updated 2026.08.30

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Please install @user_7da98a98/obsidian-memory using the guide at https://skillhub.cn/install/skillhub.md.

About this skill

Problem

Long-term agent memory often accumulates three failure modes: stale context, contradictory preferences, and repeated pitfalls that never become rules. agent+obsidian=long-term memory stores memory in an Obsidian vault, using profile.md, rules.md, projects/, and session-log.md to separate stable preferences, hard rules, project state, and recent sessions.

How It Works

  • Load on start: read the vault path from SOUL.md when present, then load required files; open a project file only when the user names that project.
  • Write on trigger: persist new preferences, decisions, pitfalls, and session summaries only after triggers such as 任务完成, /记一下, /记录, or /更新记忆.
  • Hit stamping: mark entries actually used with a last hit date, and flush those marks together with the next real write.
  • Memory audit: run /审计记忆 or accept a 7-day reminder to detect conflicts, stale entries, repeated pitfalls, and log overflow; after confirmation, archive projects or promote recurring issues into rules.md.

Boundaries

It fits agent workflows that need durable preferences, project continuity, and reusable rules. It is not meant to store full task outputs, temporary parameters, or one-off debugging details. If the user says no memory, the skill skips all memory reads and writes.

Use Cases

  • When a user asks to continue a named project, load only that project's current-state block from the Obsidian vault instead of reading unrelated project files.
  • After a task completion trigger or `/记一下`, write new preferences, decisions, and pitfall notes back to the appropriate Obsidian memory files.
  • Run the memory audit to find stale project states, repeated pitfall patterns, and session-log capacity limits, then wait for user confirmation.
  • Detect conflicting preference entries in profile.md during audit, list the proposed fixes, and update them only after the user confirms.

Best For

  • Independent developers who need agents to remember writing preferences and formatting rules.
  • AI application engineers managing several long-running projects and preserving state plus pitfall notes.
  • Agent platform maintainers who want recurring agent mistakes promoted into durable rules.
  • Obsidian-based engineers who need controlled loading and writing of agent memory files.