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Graph Memory Zero

by @wangyangwjy

Production playbook for OpenClaw graph-memory optimization with mem0-aligned recall governance. Use when users ask to (1) summarize current graph-memory stat...

Versionv1.0.2
Downloads624
TERMINAL
clawhub install graph-memory-zero

📖 About This Skill


name: graph-memory-zero description: Production playbook for OpenClaw graph-memory optimization with mem0-aligned recall governance. Use when users ask to (1) summarize current graph-memory status, (2) reproduce the same optimization effect on another workspace, (3) tune threshold/infer/memoryType/preferenceLexicon for precision vs recall, (4) troubleshoot recall quality drift, or (5) apply/rollback safe config patches under plugins.entries.graph-memory.config.

Graph Memory Zero

Mission

Deliver a reproducible graph-memory optimization outcome (not just a config diff):

  • stable recall behavior
  • explainable filtering semantics
  • safe rollout + rollback
  • observable runtime state
  • If user asks “达到你这套效果”, execute the full playbook below.

    Load order (mandatory)

    1. references/current-baseline.md (known-good baseline) 2. references/baseline-profiles.md (balanced/precision/recall profiles) 3. references/verification-playbook.md (acceptance checks) 4. references/troubleshooting.md (if any mismatch/failure)

    When the user asks about install/download/distribution options, also load:

  • references/install-channels.md
  • Reproducible rollout workflow

    Phase 0 — Snapshot and schema guard

    1. Run gateway.config.schema.lookup for: - plugins.entries.graph-memory.config - plugins.entries.graph-memory.config.recallPolicy 2. Run gateway.config.get and store: - current config snapshot - baseHash 3. Report: plugin enabled state, llm/embedding model, recall policy keys present.

    Do not patch before confirming schema path exists.


    Phase 1 — Normalize semantics (mem0-compatible)

    Ensure these compatibility rules are explicitly explained in summary:

  • threshold is mem0-style alias; legacy minScore may still exist.
  • If both appear, effective threshold = max(threshold, minScore) (stricter wins).
  • infer is deterministic inference/expansion; no extra LLM call.
  • filters.memoryType supports fact|preference|task|event|all.
  • preferenceLexicon (versioned) has higher priority than legacy preferenceKeywords.
  • If any rule is not represented in runtime config, patch minimal fields only.


    Phase 2 — Apply profile patch (minimal mutation)

    Default profile is balanced unless user requests otherwise.

    Use gateway.config.patch with smallest scoped patch under:

  • plugins.entries.graph-memory.config.recallPolicy
  • Balanced target (canonical):

  • threshold: 0.62
  • infer: true
  • filters.memoryType: all
  • preferenceLexicon.version: 2026-03-27.balance-v1
  • preferenceLexicon.enabled: true
  • preferenceLexicon.keywords: include EN+ZH preference words
  • If user asks for stronger precision or stronger recall, choose profile from references/baseline-profiles.md.


    Phase 3 — Post-restart verification

    After patch + restart, verify all below:

    1. Effective config re-read matches intended patch. 2. gm_search debug details available (details.debug includes threshold/infer/filter summary). 3. No schema/key regression (memoryType not dropped, lexicon keys intact). 4. Query spot-checks pass (from verification playbook).

    If any check fails, enter troubleshooting flow.


    Phase 4 — Quality validation (must do before claiming success)

    Run the query set in references/verification-playbook.md and compare:

  • preference-sensitive queries
  • task/event retrieval queries
  • mixed-language (CN/EN) preference terms
  • Success criteria (minimum):

  • relevant top hits improve or stay stable
  • off-topic hits do not increase materially
  • preference-related queries show better intent alignment
  • Do not claim “优化完成” without this phase.


    Phase 5 — Rollback safety

    Always keep rollback notes in output:

  • previous values (before)
  • target values (after)
  • one-step revert patch path
  • If regression is observed, rollback immediately to previous stable profile.

    Failure handling

    A) Local test execution fails

    If extension tests fail locally but config intent is clear: 1. Skip blocking local test path. 2. Use controlled gateway.config.patch rollout. 3. Run verification playbook. 4. Keep explicit rollback entry.

    B) PowerShell path / command failed

    If errors indicate missing path or command failure: 1. Validate path with Test-Path first. 2. Confirm script/CLI location and permissions. 3. Retry minimal command only after path is confirmed.

    C) Version mismatch signals

    If extension folder version and runtime installed version differ:

  • treat as metadata mismatch
  • continue config-level rollout, but report mismatch as release check item
  • Output contract (default reply structure)

    Use this structure for user-facing summary:

    1. 当前状态:enabled / model / embedding / recallPolicy 2. mem0 对齐语义:threshold-minScore、infer、memoryType、lexicon 3. 本次变更:before → after(只列关键键) 4. 验证结果:通过项 / 风险项 / 观测数据 5. 下一步建议:继续调优或保持当前 6. 回滚信息:可直接执行的 revert 说明

    Keep answers concise-first, but never omit verification and rollback details.

    Distribution guidance (when requested)

    If user asks "how can others install this", provide at least 3 channels: 1. ClawHub registry install (online) 2. Offline package install (.skill as zip artifact) 3. Source-folder install (copy skill folder into workspace skills/)

    Always include:

  • required folder layout check (SKILL.md at skill root)
  • post-install reload step (openclaw gateway restart)
  • quick verification (skill appears in available skills and can be triggered)
  • Anti-patterns (forbid)

  • Large full-config overwrite when only recallPolicy needs change.
  • Declaring success without post-restart validation.
  • Ignoring threshold/minScore conflict resolution.
  • Omitting lexicon version in production summary.
  • Hiding test/verification gaps.