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 agentmods add commands/rand/mnemosyne/memory-storegit clone --depth 1 https://github.com/rand/mnemosyneWhat 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 | $0.00015 | $0.00503 |
| Opus 5 | $0.00008 | $0.00251 |
| Sonnet 5 | $0.00003 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
memory-store 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 2d 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.
What it actually says
I will help you store a memory in Mnemosyne. Please provide the content you want to store as arguments to this command.
Usage:
/memory-store <content>- Store with default importance (5)/memory-store --importance <1-10> <content>- Store with specific importance/memory-store --context <context> <content>- Store with additional context
Instructions for me:
-
Parse the arguments:
- Extract
--importanceflag if present (default: 5) - Extract
--contextflag if present (default: "User-provided memory") - The remaining text is the memory content
- Extract
-
Auto-detect namespace:
- Use the Bash tool to detect the current project
- Check for git root:
git rev-parse --show-toplevel 2>/dev/null - If in a git repo, read
.claude/CLAUDE.mdorCLAUDE.mdfor project name - Parse YAML frontmatter for
project:field, or use first H1 heading - Construct namespace as
project:<name> - If no project detected, use
global
-
Call Mnemosyne MCP tool:
{ "name": "mnemosyne.remember", "arguments": { "content": "<parsed content>", "namespace": "<detected namespace>", "importance": <parsed importance>, "context": "<parsed context>" } } -
Format the output:
✓ Memory stored successfully ID: <memory_id> Summary: <llm-generated summary> Tags: <comma-separated tags> Importance: <importance>/10 Namespace: <namespace> -
Error handling:
- If MCP server not available: "Error: Mnemosyne MCP server not running. Start with 'mnemosyne serve'"
- If API key not configured: "Error: Anthropic API key not set. Configure with 'mnemosyne config set-key'"
- If content is empty: "Error: No content provided. Usage: /memory-store "
Please proceed to store the memory with the arguments I provided.
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.
- 2d ago First seen · 58 lines · 15 tokens per session scan A 8494df3b4d96
memory-store is a command published in the GitHub repository rand/mnemosyne (84 stars, last pushed 9mo ago), licensed MIT. It adds 15 tokens to every session and 503 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
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hatch3r-bug-pipeline
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story-6.1.4
Story ID: 6.1.4 Epic: Epic-6.1 - Agent Identity System Wave: Wave 1 (Foundation) Status: 📋 Ready to Start Priority: 🔴 Critical Owner: Dev (Dex) Created: 2025-01-14 Updated: 2025-01-17 (v4 - Unified System Integration) Duration: 2.5 days (20 hours) Investment: $250.00.