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/rjmurillo/ai-agents/memory-savegit clone --depth 1 https://github.com/rjmurillo/ai-agentsWhat 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 | $0.00019 | $0.01174 |
| Opus 5 | $0.00010 | $0.00587 |
| Sonnet 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 3d 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.
- 3d 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 (45 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 1,174 once invoked, about $0.0001 per session on Opus 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
update-docs
Update all the documentation related files.
focused-fix
Deep-dive feature repair — systematically fix an entire feature/module. Usage: /focused-fix.
cm
Stage working tree changes and create a Conventional Commit (no push).
init
One-time setup to initialize the Craft harness for a project.
story-implement
Implement a story through the Implement Phase. Stories must be fully designed with chunks before implementation.
become
Agent crystallization command. Studies a tool, role, or person and produces a portable 9-section agent that inhabits the domain - with beliefs, scar tissue, and instincts.