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/rohitg00/agentmemory/recallgit clone --depth 1 https://github.com/rohitg00/agentmemoryWhat 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 | $0.00184 |
| Opus 5 | $0.00000 | $0.00092 |
| Sonnet 5 | $0.00000 | $0.00037 |
| Haiku 4.5 | $0.00000 | $0.00018 |
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 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
Search past session observations and lessons for relevant context. Wrap the memory_smart_search and memory_lesson_recall MCP tools.
Usage
/recall [query]
Instructions
- Call
memory_smart_searchwith the query andlimit: 10(hybrid BM25 + vector + graph search). - Call
memory_lesson_recallwith the same query andlimit: 5(lesson search). - Combine results and present to the user:
- Group by session
- Show type, title, and narrative for each observation
- Highlight high-importance (>= 7) observations
- Show lessons separately with confidence scores
- If no results, suggest 2-3 alternative search terms.
- Never hallucinate results. Only present what the MCP tools actually return.
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 · 20 lines · 0 tokens per session scan A 3b05701dbade
recall is a command published in the GitHub repository rohitg00/agentmemory (27,776 stars, last pushed 8d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 184 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 2026-08-30.
Other commands, from other repositories
potpie-record
Record durable Potpie learnings after useful work.
cq:reflect
Mine the current session for knowledge worth sharing — identify learnings, present them for approval, and propose each approved candidate to the cq knowledge store.
distill
Distill important session insights into doc/loom/knowledge.
demo-command
Example slash command that wraps the demo-skill. Showcases the command kind.
revise-claude-md
Update CLAUDE.md with learnings from this session.
lavra-checkpoint
Save session progress by filing beads, capturing knowledge, and syncing state.