memory

memory is a command for coding agents from sdsrss/claude-mem-lite. It costs 25 tokens per session (421 once invoked), scanned A, original, MIT.

A command for saving important project information to a searchable long-term memory store. It can save supplied notes or summarize useful findings from the current session.

In plain words
What is it for?
Use it to record project decisions, fixes, features, refactors, discoveries, or other details you want to recall later.
Why use it?
It prevents decisions, bug fixes, discoveries, and other useful context from being lost between sessions.

Command

Part of the claude-mem-lite plugin — 8 commands, 7 hooks, 1 MCP server shipped together

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/sdsrss/claude-mem-lite/memory
Clone the repo
git clone --depth 1 https://github.com/sdsrss/claude-mem-lite

Or install claude-mem-lite, the plugin that ships this one along with the rest of its 8 commands, 7 hooks, 1 MCP server.

Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 421 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 $0.00025 $0.00421
Opus 5 $0.00013 $0.00211
Sonnet 5 $0.00005 $0.00084
Haiku 4.5 $0.00003 $0.00042

Measured 3d ago against content hash 5c7d0fb0c50f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

memory 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 3d 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.

commands/memory.md · 52 lines

What it actually says

Memory Save

Save important content to your long-term memory database.

Commands

  • /mem:memory <content> — Save the given content directly to memory
  • /mem:memory (no args) — Auto-summarize recent session highlights and save key findings

Instructions

When the user invokes /mem:memory, determine the intent:

With explicit content

If the user provides content after the command:

  1. Analyze the content to determine appropriate type (decision, bugfix, feature, refactor, discovery, change)
  2. Generate a concise title from the content
  3. Call mem_save with:
    • content: the provided text
    • title: auto-generated title
    • type: inferred type (default: "discovery")
    • importance: 2 (notable — user explicitly requested save)

With instructions/prompt

If the user provides instructions like "save the database schema we discussed" or "remember the fix for the auth bug":

  1. Review recent conversation context
  2. Extract the relevant information per the user's instruction
  3. Call mem_save with extracted content, appropriate title and type, importance=2

No arguments (auto-save)

If no content is provided:

  1. Review the current session's recent key findings
  2. Identify: decisions made, bugs fixed, patterns discovered, important code changes
  3. For each significant finding (max 5), call mem_save with:
    • Clear title and structured content
    • Appropriate type and importance level (1=routine, 2=notable)
  4. Skip trivial or already-saved items
  5. Report what was saved in a concise summary

Always set importance=2 for explicit saves (user chose to save), importance=1 for auto-saves of routine items, importance=2 for auto-saves of notable discoveries.

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. 3d ago First seen · 52 lines · 25 tokens per session scan A 5c7d0fb0c50f

Subscribe to this mod's changes

memory is a command published in the GitHub repository sdsrss/claude-mem-lite (54 stars, last pushed 8d ago), licensed MIT. It adds 25 tokens to every session and 421 once invoked, about $0.0001 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 2026-08-30.