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.
git clone --depth 1 https://github.com/macbyclp/memorryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/macbyclp/memorry/hatirla)<a href="https://agentmods.dev/commands/macbyclp/memorry/hatirla"><img src="https://agentmods.dev/badge/commands/macbyclp/memorry/hatirla/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/macbyclp/memorry/hatirla"><img src="https://agentmods.dev/badge/commands/macbyclp/memorry/hatirla.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00011 | $0.00457 |
| Opus 5.5 | $0.00004 | $0.00183 |
| Sonnet 5.5 | $0.00002 | $0.00091 |
| Haiku 4.5 | $0.00001 | $0.00046 |
Grade A, and why
hatirla 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 1mo 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.
What it actually says
The user wants this committed to persistent memory (memorry): $ARGUMENTS
Steps:
- Make sure the memorry server is running (port 8765). If it isn't, start
it — on Windows via
start-memory.bat, otherwisepython server.pyfrom your memorry checkout. - Distill the text into ONE self-contained sentence — one that still makes sense read on its own months from now, with no surrounding conversation. If the user already phrased it that way, keep their wording; don't pad it with commentary.
- Search the same topic with
memory_searchfirst. If you find a record that is now stale or contradicts the new fact, remove it withmemory_delete(or correct it in place withmemory_update) before writing. - Pick the right
project. Keep personal facts and preferences separate from infrastructure/setup decisions — see the note below on setting your own default. - Write a
titleof 3–6 words naming the topic (not a shortened copy of the sentence). It becomes the note's filename and its label in Obsidian's Graph View, so a truncated sentence there makes the graph unreadable. - Add 1–3
tags, reusing tags already in use — check withmemory_listif you don't know them. Tags are what group records into clusters and what the Graph View colours by; a new tag per record gives you neither. - Save it with
memory_add. - Reply with a single line: what you saved and the id it got. No essay.
Customize this: step 4's routing is deliberately generic. Edit this file to name your own projects (e.g.
personalvsinfra, or one per codebase) and to hardcode the path to yourstart-memory.batif you want step 1 to be a single copy-pasteable command.
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.
- 1mo ago First seen · 38 lines · 11 tokens per session scan A c51598d60642
hatirla is a command published in the GitHub repository macbyclp/memorry (3 stars, last pushed 2mo ago), licensed MIT. It adds 11 tokens to every session and 457 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.