Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add automateyournetwork/netclaw --skill mempalacegit clone --depth 1 https://github.com/automateyournetwork/netclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/automateyournetwork/netclaw/mempalace)<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/mempalace"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/mempalace/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/mempalace"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/mempalace.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 108 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.02726 |
| Opus 5 | $0.00032 | $0.01363 |
| Sonnet 5 | $0.00013 | $0.00545 |
| Haiku 4.5 | $0.00006 | $0.00273 |
Grade A, and why
mempalace scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MemPalace — AI Memory System
Persistent, structured, local-only memory across sessions. 19 MCP tools. Source: https://github.com/milla-jovovich/mempalace
Relationship to OpenClaw's built-in memory: OpenClaw writes raw daily logs to
memory/YYYY-MM-DD.md. MemPalace adds a structured, searchable layer on top — semantic search across all sessions, a temporal knowledge graph for network facts, and per-agent diaries. Use daily logs for "what happened today" and MemPalace for "what did we decide and why."
How to Call the Tools
All tools use mcp-call with the mempalace MCP server:
python3 $MCP_CALL "python3 -u $MEMPALACE_MCP_SCRIPT" <tool-name> '<arguments-json>'
Palace Read Tools (7)
mempalace_status — Palace Overview
python3 $MCP_CALL "python3 -u $MEMPALACE_MCP_SCRIPT" mempalace_status '{}'
Returns total drawers, wing/room counts, palace path, memory protocol instructions, and AAAK dialect spec. Call this at session start to load palace context.
mempalace_get_aaak_spec — AAAK Dialect Spec
python3 $MCP_CALL "python3 -u $MEMPALACE_MCP_SCRIPT" mempalace_get_aaak_spec '{}'
Get the AAAK dialect specification — the compressed memory format MemPalace uses. Call this if you need to read or write AAAK-compressed memories.
mempalace_search — Semantic Search
python3 $MCP_CALL "python3 -u $MEMPALACE_MCP_SCRIPT" mempalace_search '{"query":"why did we configure OSPF area 10 as stub","limit":5}'
Parameters:
query(required): Natural language searchlimit(optional, default 5): Max resultswing(optional): Restrict to wingroom(optional): Restrict to room
Semantic search. Returns verbatim drawer content with similarity scores.
mempalace_list_wings — List All Wings
python3 $MCP_CALL "python3 -u $MEMPALACE_MCP_SCRIPT" mempalace_list_wings '{}'
List all wings with drawer counts.### mempalace_list_rooms — List Rooms
python3 $MCP_CALL "python3 -u $MEMPALACE_MCP_SCRIPT" mempalace_list_rooms '{"wing":"wing_netclaw"}'
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 252 lines · 63 tokens per session scan A 6b33bb85d2e7
mempalace is a skill published in the GitHub repository automateyournetwork/netclaw (657 stars, last pushed 6d ago), licensed Apache-2.0. It adds 63 tokens to every session and 2,726 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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