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 MarcelRoozekrans/LongtermMemory-MCP --skill long-term-memorygit clone --depth 1 https://github.com/MarcelRoozekrans/LongtermMemory-MCPWrote 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/marcelroozekrans/longtermmemory-mcp/long-term-memory)<a href="https://agentmods.dev/skills/marcelroozekrans/longtermmemory-mcp/long-term-memory"><img src="https://agentmods.dev/badge/skills/marcelroozekrans/longtermmemory-mcp/long-term-memory/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/marcelroozekrans/longtermmemory-mcp/long-term-memory"><img src="https://agentmods.dev/badge/skills/marcelroozekrans/longtermmemory-mcp/long-term-memory.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 | $0.00041 | $0.01578 |
| Opus 5 | $0.00020 | $0.00789 |
| Sonnet 5 | $0.00008 | $0.00316 |
| Haiku 4.5 | $0.00004 | $0.00158 |
Grade A, and why
long-term-memory 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 10d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long-Term Memory
Persistent, semantic memory across sessions. Two modes: RECALL at session start (pull relevant context) and SAVE after completing tasks (persist insights). All memory operations should be invisible to the user.
Tools Available
| Tool | Purpose |
|---|---|
save_memory |
Store text with auto-generated semantic embedding, tags, importance, and type |
search_memory |
Find relevant memories using natural language query (cosine similarity) |
update_memory |
Modify an existing memory (content, metadata, tags, importance, type) |
delete_memory |
Remove a specific memory by ID |
delete_all_memories |
Wipe all memories (irreversible) |
get_all_memories |
List all stored memories (paginated) |
memory_stats |
Get count and database location |
search_by_type |
Filter memories by category (general, fact, preference, conversation, task, ephemeral) |
search_by_tags |
Find memories matching any of the provided tags |
search_by_date_range |
Find memories created within a specific date range |
create_backup |
Manually trigger a database backup with JSON export |
When to Use
- Session start: Always. Recall relevant memories before any work begins.
- After debugging / bug fixes: Save root causes and fix patterns.
- After brainstorming / design work: Save design decisions and architecture choices.
- After planning / implementation: Save implementation approach and tech decisions.
- After code reviews: Save style preferences and feedback patterns.
- After writing tests: Save test patterns and strategies.
- After any major task: Save workflow preferences discovered during the session.
RECALL Mode (Session Start)
When invoked at the start of a session:
-
Detect context — Determine the current project from:
- Working directory name or git remote
- Any CLAUDE.md in the project
- The user's first message
-
Search memories — Call
search_memorywith two queries (sequentially):"{project name} {task keywords from user message}"— project-specific context"preference workflow"— general preferences that apply everywhere
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.
- 10d ago First seen · 135 lines · 41 tokens per session scan A b47ea4fb5744
long-term-memory is a skill published in the GitHub repository MarcelRoozekrans/LongtermMemory-MCP (1 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 1,578 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
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mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
shellm
Reference for the shellm system — recursive LLM shell, identity management, memory, skills, trajectory, and all CLI tools. Use when working on shellm itself, debugging agent behavior, or understanding how the pieces fit together.