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.
git clone --depth 1 https://github.com/scitrera/memorylayerWrote 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/scitrera/memorylayer/recall)<a href="https://agentmods.dev/commands/scitrera/memorylayer/recall"><img src="https://agentmods.dev/badge/commands/scitrera/memorylayer/recall/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/scitrera/memorylayer/recall"><img src="https://agentmods.dev/badge/commands/scitrera/memorylayer/recall.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 | $0.00290 |
| Opus 5.5 | $0.00000 | $0.00116 |
| Sonnet 5.5 | $0.00000 | $0.00058 |
| Haiku 4.5 | $0.00000 | $0.00029 |
Grade A, and why
recall 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 23d 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
/memorylayer:recall
Quick command to search memories.
Usage
/memorylayer:recall <query>
Examples
/memorylayer:recall database decisions
/memorylayer:recall authentication bugs
/memorylayer:recall user preferences
/memorylayer:recall what do we know about the API
Behavior
When this command is invoked:
- Use
memory_recallwith the provided query - Display results in a readable format
- Show relevance scores and key metadata
Response Format
Found 3 relevant memories:
1. [0.89] We decided to use PostgreSQL for better JSON support
Type: semantic | Tags: postgresql, database
Stored: 2 days ago
2. [0.76] Database connection pooling set to max 20 connections
Type: procedural | Tags: database, config
Stored: 1 week ago
3. [0.71] User prefers PostgreSQL over MySQL for new projects
Type: semantic | Tags: database, preference
Stored: 3 weeks ago
If no results:
No memories found matching "your query"
Try:
- Broader search terms
- Check /memorylayer:status for connection issues
- Use /memorylayer:remember to store new memories
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.
- 23d ago Changed e55d7e672e65
- 1mo ago First seen · 56 lines · 0 tokens per session scan A d3a69b66151e
recall is a command published in the GitHub repository scitrera/memorylayer (2 stars, last pushed 24d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 290 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 commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.