Memory Router — Team Unified Knowledge Retrieval Skill Skill

Memory Router — Team Unified Knowledge Retrieval Skill

Author: Daniel Li
Copyright © Daniel Li. All rights reserved.

Purpose

All team agents (小a/ops/code/quant/data/finance/research/market/pm/content/law) MUST route knowledge retrieval through this standardized pipeline before answering questions about:

系统配置、架构、部署
历史决策、TODO、进度
Skill 用法与触发条件
项目状态与交付件
人物、日期、数字

QMD Feature Map (all features agents should use)

| Feature | Command | When to Use | |---------|---------|-------------| | Hybrid search | qmd query "<question>" | DEFAULT: combines BM25 + vector + rerank. Best for most queries | | Structured query | qmd query $'lex: keyword\nvec: semantic' | When you need precise keyword AND semantic results | | BM25 keyword | qmd search "<exact term>" | Exact term/filename/config key lookup | | Vector similarity | qmd vsearch "<concept>" | Conceptual/fuzzy similarity (e.g., "how to deploy") | | Get document | qmd get path/to/file.md:42 -l 30 | Read specific lines after search identifies a file | | Multi-get | qmd multi-get "reports/*.md" | Batch fetch multiple files (e.g., all reports) | | Collection filter | qmd query -c skills "<question>" | Restrict search to one collection for precision | | Full output | qmd query --full "<question>" | Get complete document content instead of snippets | | JSON output | qmd query --json "<question>" | Machine-readable output for scripts | | List files | qmd ls skills | Browse what's indexed in a collection | | Status | qmd status | Health check: pending embeds, collection sizes | | Update index | qmd update | Re-index after file changes | | Embed vectors | qmd embed | Generate embeddings for new/changed files | | Context notes | qmd context list | View collection descriptions/usage hints | | MCP server | qmd mcp | Expose as MCP tool for IDE/agent integration |

Collections (current)

| Collection | Content | Use For | |------------|---------|---------| | clawd-memory | ~/clawd/**/*.md (1603 files) | Memory, docs, reports, runbooks | | daily-memory | ~/clawd/memory/*.md (44 files) | Daily work logs | | team | ~/.openclaw/agents/**/*.json (860 files) | Agent configs, models, auth | | openclaw-config | ~/.openclaw/**/*.json (1031 files) | OpenClaw system config | | projects | ~/clawd/projects/**/*.md (100 files) | Project PRDs, deliverables | | skills | ~/clawd/skills/**/SKILL.md (468 files) | All skill documentation | | reports | ~/clawd/reports/*.md (2 files) | Research & review reports |

Routing Algorithm

Input: user question Q

Step 1 — CLASSIFY
  A = "prior decisions / todos / people / dates / what happened"
  B = "system config / how-to / skill usage / architecture"
  C = "project status / deliverables / PRD"
  D = "external facts / live data" (falls through to web)

Step 2 — RETRIEVE (execute ALL applicable, not just one)

  if A:
    → qmd query -c daily-memory "<Q>" -n 5
    → qmd query -c clawd-memory "<Q>" -n 5
    → Also check ~/clawd/MEMORY.md directly for TODOs/decisions

  if B:
    → qmd query -c openclaw-config "<Q>" -n 5
    → qmd query -c skills "<Q>" -n 5
    → qmd query -c clawd-memory "<Q>" -n 3  (for runbooks/docs)

  if C:
    → qmd query -c projects "<Q>" -n 5
    → qmd query -c reports "<Q>" -n 3

  if D:
    → qmd query "<Q>" -n 5  (all collections, no filter)
    → If low recall → web_fetch / browser

Step 3 — CITE
  Every answer MUST include 1-3 source citations:
  - File path: `~/clawd/docs/memory-router.md:15`
  - QMD URI: `qmd://skills/geo-agent/SKILL.md`
  - Or: "Source: qmd query -c projects 'content factory status'"

Agent Integration (mandatory AGENTS.md section)

Each agent's AGENTS.md must contain:

## 知识库 / Memory Router（强制）

- 你在回答任何「配置/流程/历史/怎么做」类问题前，**必须先检索本地知识库**：
  1) 优先 QMD：`qmd query "<问题>"`（必要时加 `--collection openclaw-config|projects|skills|reports|clawd-memory`）
  2) 涉及待办/决策/人/日期 → 再查工作区记忆文件（`~/clawd/memory/YYYY-MM-DD.md` 与 `~/clawd/MEMORY.md`）
- 输出时至少引用 1-3 个来源（文件路径或 `qmd://...` URI）。

Health Monitoring

Daily Cron (recommended)

# qmd-health: run daily at 08:00
qmd status | grep -E "Total|Vectors|Pending|Updated"
qmd update

Weekly Embed Refresh

# qmd-embed: run weekly Sunday 03:00
qmd embed
qmd status

Health Metrics to Track

覆盖率: Vectors / Total (target: >90%)
Pending: should be <100 after weekly embed
Collection freshness: Updated timestamps should be <24h for active collections
Query latency: hybrid query should return <2s

Troubleshooting

| Problem | Solution | |---------|----------| | qmd embed hangs | Check if node-llama-cpp is compiling (first run). Wait ~10min. | | CUDA errors | Normal on CPU-only servers. QMD auto-falls back to CPU. | | Ollama not found | QMD uses node-llama-cpp, NOT Ollama. Ignore Ollama references. | | Low recall | Try --full flag, or use qmd search (BM25) for exact terms | | Missing files | Run qmd update to re-index, then qmd embed for new vectors |

Script: memory_router.sh

For automated context injection into agent prompts:

#!/bin/bash
# Usage: ./memory_router.sh "question" [collection]
QUERY="$1"
COLLECTION="${2:-}"

if [ -n "$COLLECTION" ]; then
  qmd query -c "$COLLECTION" "$QUERY" -n 5 --line-numbers
else
  qmd query "$QUERY" -n 5 --line-numbers
fi

Last updated: 2026-03-03 Maintainer: 小a (CEO)

Agent Skills: Memory Router — Team Unified Knowledge Retrieval Skill

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