memory-recall

Agent Skills › zilliztech/memsearch › memory-recall

memory-recall

GitHub

提供渐进式记忆检索能力，通过search、get和transcript工具回顾历史对话与决策，适用于需要上下文或过往经验的场景。

plugins/openclaw/skills/memory-recall/SKILL.md zilliztech/memsearch

Trigger Scenarios

用户询问过往讨论内容或决策原因系统注入[memsearch] Memory可用提示

Install

npx skills add zilliztech/memsearch --skill memory-recall -g -y

More Options

Non-standard path

npx skills add https://github.com/zilliztech/memsearch/tree/main/plugins/openclaw/skills/memory-recall -g -y

Use without installing

npx skills use zilliztech/memsearch@memory-recall

指定 Agent (Claude Code)

npx skills add zilliztech/memsearch --skill memory-recall -a claude-code -g -y

安装 repo 全部 skill

npx skills add zilliztech/memsearch --all -g -y

预览 repo 内 skill

npx skills add zilliztech/memsearch --list

SKILL.md

Frontmatter

{
    "name": "memory-recall",
    "metadata": {
        "openclaw": {
            "emoji": "🧠"
        }
    },
    "description": "Search and recall relevant memories from past sessions via memsearch. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge -- especially questions like 'what did I decide about X', 'why did we do Y', or 'have I seen this before'. Also use when you see `[memsearch] Memory available` hints injected via SessionStart or UserPromptSubmit. Typical flow: search for 3-5 chunks, expand the most relevant, optionally deep-drill into original transcripts via the anchor format. Skip when the question is purely about current code state (use Read\/Grep), ephemeral (today's task only), or the user has explicitly asked to ignore memory."
}

You have three memory tools for progressive recall. Start with search, go deeper only when needed.

Tools (use progressively)

1. memory_search — Start here

Semantic search across all past conversation memories.

Returns: chunk summaries with dates, topics, chunk_hash identifiers
Use for: "What did we discuss about X?", "Have I asked about Y before?"

2. memory_get — When search results aren't detailed enough

Expands a specific chunk_hash to show the full markdown section with surrounding context.

Input: chunk_hash from memory_search results
Returns: full section text, may include transcript anchors ()
Use for: "Show me the details", "I need more context on that result"

3. memory_transcript — When you need the exact original conversation

Parses the original session transcript to retrieve the raw dialogue.

Input: transcript_path from the anchor comment in memory_get results
Returns: formatted conversation with [User]/[Assistant] labels and tool calls
Use for: "What exactly did I say?", "Show me the original conversation"
If the anchor format is unfamiliar (e.g. rollout:, turn:, db: instead of transcript:), try reading the referenced file directly to explore its structure and locate the relevant conversation by the session or turn identifiers in the anchor.

Decision guide

User intent	Tools to use
Quick recall ("did we discuss X?")	memory_search only
Need details ("what was the solution?")	memory_search → memory_get
Need original dialogue ("show me the exact conversation")	memory_search → memory_get → memory_transcript

Tips

memory_search returns chunk_hash — pass it to memory_get for expansion
memory_get may reveal  anchors — pass the path to memory_transcript
If memory_search returns no results, try rephrasing with different keywords
Results are sorted by relevance (hybrid BM25 + vector search)

When unsure what to search

The SessionStart injection already shows you a heading-level preview of recent memory files — skim it first to spot concrete topics (dates, session numbers, task names). If that preview doesn't surface an obvious query, try broad keywords like overview, recent work, or a topic guess — hybrid BM25 + vector retrieval will surface chunks that share any of the terms, and you can iterate from there.