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 udjin-labs/mnemostack --skill openclawgit clone --depth 1 https://github.com/udjin-labs/mnemostackWrote 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/udjin-labs/mnemostack/openclaw)<a href="https://agentmods.dev/skills/udjin-labs/mnemostack/openclaw"><img src="https://agentmods.dev/badge/skills/udjin-labs/mnemostack/openclaw/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/udjin-labs/mnemostack/openclaw"><img src="https://agentmods.dev/badge/skills/udjin-labs/mnemostack/openclaw.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.00803 |
| Opus 5.5 | $0.00000 | $0.00321 |
| Sonnet 5.5 | $0.00000 | $0.00161 |
| Haiku 4.5 | $0.00000 | $0.00080 |
Grade A, and why
openclaw scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenClaw
Wire mnemostack into OpenClaw as an MCP server, alongside the native OpenClaw
memory tools (memory_search / memory_get). The two coexist — mnemostack
handles the hybrid pipeline (Vector + BM25 + Memgraph + Temporal + 8-stage
rerank), while the native tools do fast file-scoped retrieval.
Install
-
Binary and backend:
pip install 'mnemostack[mcp]' mnemostack health --provider gemini -
Open OpenClaw's config (
~/.openclaw/openclaw.jsonon the current host) and add an MCP server under themcpplugin block. Exact schema path:plugins.entries.mcp.config.servers(confirm the current field name withconfig.schema.lookup mcp.servers— OpenClaw's schema evolves).{ "mnemostack": { "command": "mnemostack", "args": [ "mcp-serve", "--provider", "gemini", "--collection", "memory", "--memgraph-uri", "bolt://localhost:7687" ], "env": { "GEMINI_API_KEY": "${env:GEMINI_API_KEY}" } } } -
Apply config and restart OpenClaw gateway:
# Restart takes ~1 minute on this VPS — don't poll too early. systemctl --user restart openclaw-gateway
Verify
After gateway restart, in any OpenClaw session:
List the MCP servers you can see.
Expected: mnemostack among the available servers. Then:
Call mnemostack's search tool with query "VPN failover" and tier 1.
You should get id+score+source list, no text. The existing
scripts/recall-selfeval.sh and the auto-recall-mnemostack plugin are
unaffected — they call mnemostack via its Python API directly, not via
MCP. The MCP route is for cases where an outer agent host wants tool-style
access.
When to use which
- Native
memory_search/memory_get— cheap file-scoped lookups in MEMORY.md and memory/*.md, already in OpenClaw's system prompt. scripts/recall-selfeval.sh— the default recall forcing function (also what the auto-recall plugin invokes).- mnemostack over MCP — when you want a spawned sub-agent or an external host to call into the same pipeline without shelling out to bash.
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.
- 1mo ago First seen · 88 lines · 0 tokens per session scan A 5daaab0be5e1
openclaw is a skill published in the GitHub repository udjin-labs/mnemostack (9 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 803 tokens. 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 2026-08-31.
Other skills, from other repositories
goodmem:mcp
Workflow guide for GoodMem MCP server tools. Use when an agent needs to understand what GoodMem MCP tools are available and how to use them. Parameter signatures are provided by the MCP protocol itself — do not hardcode them here.
goodmem:help
Overview of all available GoodMem skills and what a user needs to get started. Invoke when a user asks what GoodMem skills are available, how to get started, or what they need to set up.
goodmem:java
Use when the user wants to write Java code that interacts with GoodMem - creating embedders, spaces, memories, retrieval, pagination, streaming, or async workflows with the goodmem-java SDK.
goodmem:python
Use when the user wants to write Python code that interacts with GoodMem — creating embedders, spaces, memories, retrieval, managing API keys, etc. Provides the complete SDK reference for the goodmem Python package.
agent-memory-discipline
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
memanto-companion
Inspect and manage the cross-session engineering memory that Memanto maintains for your Claude Code skills. Use when the user asks what Memanto remembers, wants to see their engineering profile, manually recall context for a skill, or store a decision. The automatic lifecycle hooks handle capture/injection on their…