Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/rjmurillo/ai-agentsnpx agentmods add commands/rjmurillo/ai-agents/memory-saveWrote 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/commands/rjmurillo/ai-agents/memory-save)<a href="https://agentmods.dev/commands/rjmurillo/ai-agents/memory-save"><img src="https://agentmods.dev/badge/commands/rjmurillo/ai-agents/memory-save/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/commands/rjmurillo/ai-agents/memory-save"><img src="https://agentmods.dev/badge/commands/rjmurillo/ai-agents/memory-save.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 | $0.00019 | $0.01174 |
| Opus 5.5 | $0.00008 | $0.00470 |
| Sonnet 5.5 | $0.00004 | $0.00235 |
| Haiku 4.5 | $0.00002 | $0.00117 |
Grade A, and why
memory-save 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.
This is a copy
86% identical to memory-save — 41 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Save Memory
Deprecated: Use
mcp__serena__write_memoryfor memory creation. See Memory Interface Decision Matrix.
Create an atomic memory from the current conversation context.
Your Task
- Analyze the conversation for the key insight/decision/pattern to capture
- Check for existing related memories that might be affected
- Create the memory with proper curation
User guidance: $ARGUMENTS
Pre-Creation: Check for Existing Memories
Before creating, query for related memories:
execute_forgetful_tool("query_memory", {
"query": "<topic of new memory>",
"query_context": "Checking for existing memories before creating new one",
"k": 5,
"include_links": true
})
Analyze results to determine if the new memory would:
- Invalidate an existing memory (mark it obsolete with
mark_memory_obsolete) - Update an existing memory (use
update_memoryinstead of creating new) - Supersede an existing memory (create new, then mark old as obsolete with
superseded_by) - Complement existing memories (create new and potentially
link_memories)
Atomic Memory Principles (Zettelkasten)
Before creating, verify the memory passes the atomicity test:
- Can you understand the idea at first glance?
- Can you easily title it in 5-50 words?
- Does it represent ONE concept/fact/decision?
Memory Constraints
- Title: Max 200 characters - short, searchable phrase
- Content: Max 2000 characters (~300-400 words) - single concept
- Context: Max 500 characters - WHY this matters
- Keywords: Max 10 - for semantic clustering
- Tags: Max 10 - for categorization
Importance Scoring Guide
- 9-10: Personal facts, foundational architectural patterns
- 8-9: Critical technical solutions, major architectural decisions
- 7-8: Useful patterns, strong preferences, tool choices
- 6-7: Project milestones, specific solutions
- 5-6: Minor context (use sparingly)
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 · 152 lines · 19 tokens per session scan A 355efe59582f
memory-save is a command published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,174 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to memory-save, differing in 41 lines, and is treated as a copy.
Other commands, from other repositories
load-session
Load a synced session summary for context.
ingest
Ingest source material into an active wiki. Accepts URLs, file paths, PDFs, freeform text, or processes the inbox. Supports tweets via Grok MCP.
rem_help
Each section is printed verbatim by --help for the command it names. The agreed design is issue #1656; change a page there and here together.
compass-verify
Verify the Merkle hash chain over /.claude/projects/ /memory/ and report any tampered or missing files.
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.
research-verify
Verify existing research findings against independent primary sources. Upgrades confidence from 'sources agree' to 'independently verified.'.