memory-palace-openclaw
Use the Memory Palace OpenClaw plugin when the task is about durable memory recall, explicit memory verification, visual memory storage, plugin health/index maintenance, or chat-guided onboarding/bootstrap/provider configuration inside OpenClaw.
Install / Use
npx skills add AGI-is-going-to-arrive/Memory-Palace-Openclaw --skill memory-palace-openclawInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Human ResourcesSupported Platforms
Tags
Our assessment of memory-palace-openclaw
memory-palace-openclaw scores 65/100 on our quality scale, 62nd of 63 Human Resources skills we index.
Its SKILL.md is 11 KB long, split into 7 sections and no code examples: a thorough specification that gives an agent plenty to work with.
It has no GitHub stars yet, so there is no community track record; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 5 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 78/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
memory-palace-openclaw compared with similar skills
All 4 of these similar skills score higher than memory-palace-openclaw; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| memory-palace-openclaw (this skill)by AGI-is-going-to-arrive | 65 | 0 | 5mo ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 31d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 31d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 137.2k | 9d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 137.2k | 9d ago | SKILL.md |
Frequently asked questions
- How do I install memory-palace-openclaw?
- Run
npx skills add AGI-is-going-to-arrive/Memory-Palace-Openclaw --skill memory-palace-openclaw. The install tabs above show the steps for each supported agent. - Which AI agents does memory-palace-openclaw work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is memory-palace-openclaw safe to use?
- It declares no license and scores 78/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is memory-palace-openclaw still maintained?
- The repository was last updated about 5 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubname: memory-palace-openclaw description: Use the Memory Palace OpenClaw plugin when the task is about durable memory recall, explicit memory verification, visual memory storage, plugin health/index maintenance, or chat-guided onboarding/bootstrap/provider configuration inside OpenClaw. metadata: { "openclaw": { "emoji": "🧠" } }
Memory Palace OpenClaw
Use this skill inside OpenClaw when the active memory plugin is memory-palace.
Default behavior
On a hook-capable OpenClaw host, the default path is:
- the active memory plugin is already
memory-palace before_prompt_buildis the primary automatic recall hook- older hosts fall back to
before_agent_start - durable / reflection recall now search with the current session included, so recent in-thread context is less likely to be dropped
- a subset of user messages may be auto-captured after a successful turn
- visual context may be auto-harvested during the current turn
Current host boundary:
- automatic recall / capture / visual-harvest require typed lifecycle hooks from the host
- the current supported floor is
OpenClaw >= 2026.3.2 - if the host lacks those hooks, treat
openclaw memory-palace ...as the primary path and do not assume the default auto path is active
So the normal starting point is:
- trust the default recall / capture path first
- only intervene explicitly when the user asks for it, or when the default path is clearly not enough
- do not upgrade ordinary day-to-day requests into explicit tool calls unless there is a clear reason
Important boundary:
- text durable memory may be auto-recalled and auto-captured
- visual context may be auto-harvested
- visual memory is not auto-stored as a long-term record unless you explicitly use
memory_store_visual - the stable user-facing entry is the memory slot plus
openclaw memory-palace ..., not raw MCP tools - if
command:newreflection or smart extraction cannot identify the target session transcript, the current behavior is to skip that transcript fallback instead of scanning the latest unrelated transcript - compact-context recall is gist-only on the visible prompt path; do not surface stored metadata such as
session_id,source_hash, orgist_methodin chat replies - lifecycle hook failures are swallowed and logged; after 5 consecutive failures for the same hook, that hook cools down for 60 seconds before one retry
Onboarding boundary:
- if the user wants to install, bootstrap, reconfigure, or verify provider readiness through chat, prefer the onboarding tools first
- do not send the user to the dashboard by default when the request is explicitly chat-first or VPS/terminal-only
- when Profile C/D is requested, provider probe comes before apply
- if provider probe fails or required fields are missing, explain the exact gap and fallback behavior in plain Chinese before suggesting apply
Advanced features (Sprint 1-3)
Retrieval and storage capabilities beyond the default path:
- RRF hybrid fusion — set
RETRIEVAL_FUSION_METHOD=rrfto enable Reciprocal Rank Fusion in place of the default weighted-sum fusion. Treat it as opt-in: the May 15, 2026 rerun showed real C/D gains in memory-native and some retrieval metrics, but B and individual metrics were not uniformly better. - Bi-temporal validity — every memory now carries
valid_from/valid_until. Expired facts are excluded from search by default; passinclude_expired=trueonly when the user explicitly wants historical / superseded facts. - Guard IGNORE action — write guard actions are now five-valued:
ADD/UPDATE/NOOP/DELETE/IGNORE.IGNOREmeans the LLM judged the input not worth storing (distinct fromNOOP, which means "already stored and no update needed"). Surface the guard reason verbatim; do not retry withforce=trueunless the user re-confirms. - Lifecycle hooks via
.claude/settings.json— Claude Code hosts can wire passive memory helpers without explicit manual tool calls:SessionStart→ auto-loadsystem://bootPostToolUseonEdit|Write→ emit a safe capture hint for non-sensitive project file editsStop→ auto-triggercompact_context_reflectionWhen these hooks are active, treat boot loading, edit hints, and reflection as host-driven; do not duplicate them with explicit MCP calls.
- Typed exception system — backend errors now surface structured codes from
mcp_errors.pyandsqlite_errors.py. When a tool call fails, quote the error code (not the raw message) so the user can match it against the troubleshooting docs. - Memory bidirectional links — memories may carry typed links (
related/supersedes/derived_from/contradicts). When recalling a fact, also surface itssupersedes/contradictstargets so the user sees the resolution chain, not an isolated snippet. - Provenance tracking (schema ready, partial wiring) — memory schema supports
created_by_agent,created_by_session, andsource_operationfields. Currentlysource_operationis populated oncreate_memory; agent/session fields require upstream host integration. When provenance fields are present, surface them; when absent, report as "not yet tracked".
When to intervene explicitly
Use explicit tools only in these cases:
- The user clearly asks to check whether something is already in memory.
- The user wants the exact content of a remembered item.
- The user explicitly asks you to remember a stable fact, preference, or workflow for future turns.
- The user wants to persist an image-derived summary as a long-term visual memory.
- The user asks to check backend health, diagnose degradation, or rebuild the index.
- The user asks to install/bootstrap/reconfigure Memory Palace through chat without opening the dashboard.
Preferred intervention order
- Prefer the default OpenClaw memory path first.
- If the user explicitly says “remember this” / “记住这个” and the content is a stable long-term fact, use
memory_learn.- If
memory_learnsucceeds, acknowledge it with a minimal confirmation like记住了。/Stored.and move on. - If the user explicitly asked for an exact confirmation phrase after storing, use that phrase verbatim instead of paraphrasing it.
- If
memory_learnreturns a blocked write, explain the brief human-readable reason. - Only if the user confirms they still want a separate durable memory saved should you rerun
memory_learnwithforce=true.
- If
- If you need explicit recall, use
memory_search. - If a hit matters, follow with
memory_getusing the returnedpath. - If the user wants a long-term visual record, use
memory_store_visual. - If the user wants onboarding/bootstrap through chat, use:
memory_onboarding_statusmemory_onboarding_probememory_onboarding_apply
- If the problem is operational, ask the operator to run:
openclaw memory-palace statusopenclaw memory-palace verifyopenclaw memory-palace doctoropenclaw memory-palace smokeopenclaw memory-palace index
Use these explicit intervention tools:
memory_searchmemory_learnmemory_getmemory_store_visualmemory_onboarding_statusmemory_onboarding_probememory_onboarding_apply
Anti-patterns
- Do not treat every request as a manual memory workflow.
- Do not treat this OpenClaw plugin skill as a mirror of the canonical MCP skill.
- Do not start with
memory_searchwhen the default recall path is already enough. - Do not rely only on implicit auto-capture when the user explicitly asks you to remember a stable fact for future turns; use
memory_learn. - Do not use
force=trueonmemory_learnunless the user has already confirmed they still want the memory saved after a blocked write. - Do not invent memory contents.
- Treat backend/tool errors as real failures, not as empty recall.
- Do not imply that visual auto-harvest means long-term visual auto-storage.
- Do not present raw MCP tools as the default entry for ordinary OpenClaw users.
- Prefer text summaries and references for visual memory, never raw binary image storage.
- If you explicitly recall something important, prefer
memory_searchfirst andmemory_getsecond. - Do not tell VPS/terminal-only users that dashboard setup is required when onboarding tools can do the same job.
- Do not claim Profile C/D is ready before provider probe actually passes.
- Do not hide fallback to Profile B; name the failing provider and tell the user what to fix.
- Do not tell users that
/responsesis the primary LLM runtime path; current main path is still/chat/completions. - Do not switch fusion to RRF silently; only set
RETRIEVAL_FUSION_METHOD=rrfwhen the user asks or when default fusion is clearly under-performing. - Do not include expired facts in recall by default; flip
include_expired=trueonly on explicit user request for historical context. - Do not conflate
IGNOREwithNOOP;IGNOREis a deliberate "not worth storing" decision,NOOPis "already stored". - Do not duplicate host-driven lifecycle hooks (SessionStart / PostToolUse / Stop) with manual boot/edit/reflection calls.
- Do not invent provenance, links, or temporal fields when they are absent; report them as missing instead.
- Do not leak compact-context metadata into visible chat; only use the gist text that is meant for recall.
Trigger examples
Default recall path:
- “回忆一下之前记过的偏好”
- “继续我们上次的决定”
Explicit verification:
- “查记忆里有没有这件事”
- “把刚才提到的那条记忆正文读出来”
Explicit durable remember:
- “请记住这个长期偏好:以后默认简洁回答”
- “Remember this workflow for future turns: code first, tests immediately after, docs last”
- “请记住这个长期偏好,存好后只回复‘已记录。’”
- “Remember this durable fact and reply only ‘stored profile c’ after saving”
Exact confirmation phrase:
- If the user explicitly requires one exact confirmation string after
memory_learn, pass it throughconfirmationPhrase(orconfirmation_phrase) and reuse that phrase verbatim. - Example:
memory_learn({ content: \"请记住这个长期偏好:以后默认简洁回答\", confirmationPhrase: \"已记录。\" }) - Example:
memory_learn({ content: \"Remember this workflow for future turns\", confirmationPhrase: \"stored profile c\" })
Long-term visual storage:
- “把这张白板照片的 OCR 摘要存成 visual memory”
- “把这张会议截图整理成长期 visual record”
Maintenance and diagnostics:
- “检查 memory backend 状态并重建索引”
- “跑一遍 verify / doctor / smoke 看插件是不是正常”
Chat-first onboarding:
- “不要开 dashboard,直接通过对话把 Memory Palace 接进 OpenClaw”
- “先帮我判断该走 Profile B 还是 C/D,再一步一步收集 provider 配置”
- “我在 VPS 上,只能通过聊天引导完成 setup”
Advanced features:
- “切换到 RRF 融合,关键词和语义结果差太多”
- “这条事实已经过期了吗?把历史版本也调出来看看” (
include_expired=true) - “这条信息为什么没存?是 IGNORE 还是 NOOP?”
- “这条记忆是谁、什么时候、哪个 session 写进去的?” (provenance)
- “找出和这条记忆 contradicts / supersedes 的其他记忆” (bidirectional links)
- “Claude Code 的 SessionStart / PostToolUse / Stop 钩子是不是已经接上了?”
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
