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/scitrera/memorylayer/setupgit clone --depth 1 https://github.com/scitrera/memorylayerWhat 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 | $0.00000 | $0.00446 |
| Opus 5 | $0.00000 | $0.00223 |
| Sonnet 5 | $0.00000 | $0.00089 |
| Haiku 4.5 | $0.00000 | $0.00045 |
Grade A, and why
setup scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. **Check server** — Runs `curl -sf http://localhost:61001/health`. If unreachable, offers to install and start: `pip install memorylayer-server && memorylayer serve &` What it actually says
/memorylayer-setup
Automated setup and verification for MemoryLayer.
Usage
/memorylayer-setup
Behavior
This command performs a fully automated setup sequence. Each step is executed, not just described:
- Check server — Runs
curl -sf http://localhost:61001/health. If unreachable, offers to install and start:pip install memorylayer-server && memorylayer serve & - Auto-configure permissions — Reads
.claude/settings.local.json, mergesmcp__plugin_memorylayer_memorylayer__*into the permissions allow list, writes back. This eliminates per-tool permission prompts. - Verify MCP tools — Calls
memory_briefingto confirm tools are connected and the server is responding. - Smoke test — Stores a test memory, recalls it, then forgets it to verify the full read/write cycle.
- Verify hooks — Reads
~/.memorylayer/hook-state.jsonto confirm SessionStart hook fired. - Status summary — Prints server URL, workspace, session, permission status, tool count, and active hooks.
Permission Configuration
The setup command automatically configures permissions by merging this into .claude/settings.local.json:
{
"permissions": {
"allow": [
"mcp__plugin_memorylayer_memorylayer__*"
]
}
}
This wildcard allows all memorylayer MCP tools without individual prompts.
For manual configuration alternatives:
Global settings (~/.claude.json):
{
"projects": {
"/path/to/your/project": {
"allowedTools": ["mcp__plugin_memorylayer_memorylayer__*"]
}
}
}
CLI flag (per-session):
claude --allowedTools "mcp__plugin_memorylayer_memorylayer__*"
When to Use
- First time using MemoryLayer in a project
- After installation or upgrade
- When memories aren't being stored/recalled properly
- To verify configuration is correct
- When encountering permission prompts for memory tools
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.
- yesterday First seen · 63 lines · 0 tokens per session scan A f10a10fd70db
setup is a command published in the GitHub repository scitrera/memorylayer (2 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 446 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.