remember

A command for explicitly saving an insight, decision, or lesson to agentmemory for use in later sessions.

In plain words
What is it for?
Use /remember with text describing what should be saved, such as a design decision, bug, preference, or workflow.
Why use it?
It keeps important context from being lost when the current session ends.

Command

Install

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.

agentmods
npx agentmods add commands/rohitg00/agentmemory/remember
Clone the repo
git clone --depth 1 https://github.com/rohitg00/agentmemory
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 192 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00192
Opus 5 $0.00000 $0.00096
Sonnet 5 $0.00000 $0.00038
Haiku 4.5 $0.00000 $0.00019

Measured yesterday against content hash 8e27caf25ba2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

remember 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

  • remember — 100% identical, 0 lines differ
  • remember — 89% identical, 1 lines differ
plugin/opencode/commands/remember.md · 20 lines

What it actually says

Explicitly save an insight, decision, or learning to agentmemory for future sessions. Wraps the memory_save MCP tool.

Usage

/remember [what to remember]

Instructions

  1. Analyze what needs to be remembered — extract the core insight, decision, or fact.
  2. Extract 2-5 searchable concepts (lowercased keyword phrases). Prefer specific terms ("jwt-refresh-rotation" over "auth").
  3. Extract relevant file paths the memory references.
  4. Call memory_save with:
    • content — full text to remember (preserve user's phrasing)
    • concepts — extracted concept list
    • files — extracted file list (empty array if none)
    • type — choose from: pattern, preference, architecture, bug, workflow, fact
  5. Confirm the save and show the concepts tagged so the user knows retrieval terms.
Changes

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.

  1. yesterday First seen · 20 lines · 0 tokens per session scan A 8e27caf25ba2

Subscribe to this mod's changes

remember 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 192 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.