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 rules/firebolt-db/firebolt-memory-layer/cursorrulesgit clone --depth 1 https://github.com/firebolt-db/firebolt-memory-layerWhat 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 | $0.00333 | $0.00333 |
| Opus 5 | $0.00167 | $0.00167 |
| Sonnet 5 | $0.00067 | $0.00067 |
| Haiku 4.5 | $0.00033 | $0.00033 |
Grade A, and why
cursorrules 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 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.
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
FML Memory System Rules
You have access to the FML (Firebolt Memory Layer) MCP server for persistent memory management.
Session Management
- At the start of each conversation, call
init_sessionwith user_id="jon" to initialize or resume a memory session - Use the returned session_id for all subsequent memory operations
Automatic Conversation Logging
After EVERY user message, call add_to_working_memory with:
- content_type="message"
- A summary of the user's query and your response (keep it concise but capture key details)
When to Store to Long-Term Memory
Call store_memory when the user:
- Makes decisions about architecture, tools, or approaches
- Shares preferences about coding style, frameworks, or workflows
- Provides important context about their project or environment
- Asks you to remember something
- Completes a significant task or milestone
When to Recall Memories
Call recall_memories or get_relevant_context when:
- Starting work on a task (to get relevant background)
- The user asks "what did we decide about X?"
- You need context about the project, user preferences, or past decisions
- The user references something from a previous conversation
Best Practices
- Use
get_relevant_contextfor complex queries - it automatically combines working + long-term memory - Pin important working memory items that shouldn't be evicted
- Use entity tags (e.g., "table:users", "file:api.py") for better recall
- Call
checkpoint_working_memoryat natural conversation boundaries or when the session gets long
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 · 34 lines · 333 tokens per session scan A bc62376c2d43
cursorrules is a cursor rule published in the GitHub repository firebolt-db/firebolt-memory-layer (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 333 tokens to every session, about $0.0017 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 2026-08-31.
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