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duru-memory杜鲁记忆

Agent Skill

duru-memory 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:duru-memory(杜鲁记忆)
来源仓库:https://github.com/durugy/duru-memory
安装命令:
openclaw skills install duru-memory
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install duru-memory

简介

基于 Markdown 文件的内存连续性管理系统。

  • 适合本地代理使用 Markdown 作为主要记忆源。
  • 自动同步和更新记忆内容保持一致性。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 需确保文件系统读写权限正常。duru-memory 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 建议定期备份 Markdown 文件以防丢失。

SKILL.md

name
duru-memory
description
Markdown-based memory continuity system for agents using local Markdown files as the primary memory source. Use when building or operating local Markdown memory files (daily logs, project memory, handoff notes), maintaining current-state records, running session start/close memory protocols, and pairing a structured Markdown workflow with OpenClaw's built-in memory tools.

Duru Memory

Use this skill to run a high-traceability memory system based on plain Markdown files.

This skill is complementary to OpenClaw's built-in memory stack:

  • duru-memory manages the Markdown files, conventions, and maintenance workflow
  • built-in memory_search and memory_get are the primary recall/read tools during normal assistant operation
  • memory-core indexes and retrieves from the same Markdown tree
  • active-memory is an optional pre-reply recall layer for eligible interactive sessions, not a guaranteed default for every deployment

Treat the Markdown files as the source of truth, and OpenClaw's built-in memory stack as the main retrieval and recall layer.

Core workflow

  1. Load memory/CORE/hard-rules.md and memory/CORE/current-state.md.
  2. Load recent daily logs from memory/daily/ (default: last 2 days).
  3. Before answering context-dependent questions, prefer built-in memory_search first.
  4. Use scripts/memory-search.sh "<query>" when you want explicit file-level control, debugging, maintenance, or a second opinion against built-in recall.
  5. Update current-state.md when project/task state changes.
  6. Append a state diff entry in state-changelog.md for every meaningful state update.
  7. At session close, run scripts/session-close.sh.

Directory contract

memory/
  CORE/
    hard-rules.md
    current-state.md
    state-changelog.md
  daily/
  projects/
  people/
  concepts/
  handoff/
  archive/raw/
  INDEX.md

Memory admission rules

Record only high-value memory:

  • decisions
  • commitments
  • deadlines
  • preferences
  • blockers
  • postmortem conclusions

Add attributes when possible:

  • status: active | superseded | invalid
  • polarity: positive | negative
  • confidence: high | medium | low
  • avoid_reason: ... (required for negative/pitfall entries)

Avoid logging casual chat unless it impacts future execution.

Retrieval policy (built-in first, local deterministic second, optional semantic third)

For normal assistant recall, built-in memory_search is the default path. It integrates with memory-core, can include indexed session transcripts, and follows the configured OpenClaw memory backend.

Use this skill's local retrieval scripts when you want explicit file-level control, debugging, maintenance, reproducible inspections, or a second opinion against built-in recall.

Local deterministic retrieval uses weighted matching:

  • exact keyword / phrase in headings
  • tags and fields (decision, todo, blocker, preference)
  • recency boost for recent daily logs
  • path boost for likely directories (projects, people, CORE)

Optional local semantic retrieval is an experimental supplement, not the primary OpenClaw memory path. When local Ollama embedding is available, it can be added as a second pass after deterministic retrieval (default embedding model in config.yaml: qwen3-embedding:0.6b). This local semantic layer can coexist with built-in retrieval, but it does not replace OpenClaw's built-in memory_search contract.

This skill's semantic mode uses a local SQLite + sqlite-vec path with incremental indexing:

  • DB path: memory/.semantic-index.db
  • Vector extension: sqlite-vec (loaded via APSW)
  • Incremental policy: file mtime/size/hash detection + chunk-level embedding cache
  • Consistency keys: fixed model, embedding dimension, and pipeline_version
  • Threshold: SEMANTIC_MIN_SCORE (default 0.48)
  • Fusion rerank mode: FUSION_MODE=rrf|linear (default rrf)
  • RRF parameter: RRF_K (default 60)
  • Keyword boost in RRF mode: KEYWORD_BOOST (default 0.006)
  • Linear fallback weights: FUSION_SEM_WEIGHT + FUSION_KEY_WEIGHT (defaults 0.65/0.35)
  • Daily warmup: session-start.sh runs memory-semantic-search.py --build-only once per day
  • Negative memory handling: entries with polarity=negative or status in {invalid,superseded} are excluded from positive ranking and surfaced in a dedicated ⚠ Avoided Pitfalls warning block

Failure and degraded mode guidance

Do not assume semantic retrieval is available.

If built-in vector search, sqlite-vec, Ollama embeddings, or the skill's local semantic service is unavailable, fall back to built-in memory_search in its degraded lexical mode or to this skill's deterministic local retrieval. In degraded mode:

  • prefer exact facts from MEMORY.md, memory/CORE/current-state.md, and recent daily logs
  • treat semantic hits as optional enrichment, not a dependency
  • explicitly report uncertainty when no strong hit exists
  • avoid presenting local semantic indexing behavior as part of OpenClaw's guaranteed built-in memory contract

Coexistence guidance

Recommended division of labor:

  • Write and maintain long-term notes in MEMORY.md and memory/*.md
  • Keep memory/CORE/current-state.md as the execution truth for active work
  • Let OpenClaw memory-core index the same tree
  • Treat built-in memory_search and memory_get as the default runtime recall path
  • Consider active-memory optional and deployment-dependent, especially if pre-reply recall causes latency or timeout issues
  • Use local scripts for maintenance, audits, tagging, and deterministic investigations

Avoid creating separate parallel memory trees for built-in memory and Markdown memory. One shared Markdown tree is the cleanest setup.

Scripts

  • scripts/session-start.sh: startup checklist + quick context load hints
  • scripts/memory-search.sh: hybrid retrieval entry (keyword first, semantic optional)
  • config.yaml: centralized model/runtime tuning (ollama.base_url, models.*, semantic.*, fusion.*)
  • scripts/memory-semantic-search.py: semantic recall via Ollama /api/embeddings
  • scripts/memory-auto-tag.py: local-model auto-tagger (model from config.yaml, default gemma4:e4b) for incremental memory changes (--mode tag|review, --files, --force)
  • scripts/memory-write-tag.sh: write/append helper that immediately tags the target file
  • scripts/memory-compact.py: weekly compaction (daily -> summaries, mark stale, re-sync vectors)
  • scripts/memory-forget.py: monthly forgetting (archive old stale daily logs, keep negative pitfalls)
  • scripts/session-close.sh: runs auto-tagger in --mode review first, then daily log append + state freshness check
  • scripts/auto-commit.sh: optional git safety-net commit

References

  • references/templates.md: canonical templates for state/daily/project/handoff files

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能力概览

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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安装前确认

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