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/gru-953/mta_plugin/memorisegit clone --depth 1 https://github.com/GRU-953/MTA_PluginWrote 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/gru-953/mta_plugin/memorise)<a href="https://agentmods.dev/commands/gru-953/mta_plugin/memorise"><img src="https://agentmods.dev/badge/commands/gru-953/mta_plugin/memorise.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.00306 |
| Opus 5 | $0.00009 | $0.00153 |
| Sonnet 5 | $0.00004 | $0.00061 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
memorise 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.
What it actually says
Digest the path(s) the user provided into local, token-free graph memory using the Memorised them All engine.
Paths: $ARGUMENTS
Steps:
- If no path was given, ask which file or folder to digest (or offer the current directory).
- Call the
digesttool with the path(s). It converts every attachment to Markdown locally (MarkItDown, plus optional Tesseract OCR for scanned images), unpacks archives safely, then builds a knowledge graph with community-detected themes — fully deterministic and model-free (no LLM/Ollama/GPU) — and writesmemory.md, per-document notes, andgraph.json. - The tool returns only metadata (counts, paths, stats) — do not try to read the converted documents back into the conversation; that would waste tokens.
- Report a short summary: files converted, entities, relations, themes, and where the
outputs were written. Offer
/recallor/export-memoryas next steps.
Tip: to rebuild a project from scratch (e.g. after upgrading), pass reset: true to
digest. Otherwise, re-running digest on the same folder just updates the memory.
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.
- 3d ago First seen · 25 lines · 18 tokens per session scan A 929460614fa0
memorise is a command published in the GitHub repository GRU-953/MTA_Plugin (1 stars, last pushed 29d ago), licensed MIT. It adds 18 tokens to every session and 306 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 2026-08-31.
Other commands, from other repositories
enrich
Enrich project memory by mining 100 recently merged PRs: extracts decisions, conventions, gotchas, and architectural facts from PR discussions, review comments, and PR bodies.
remember
Save something to agent memory - picks project or user scope automatically from context.
forget
Search agent memory and forget entries matching the given description.
handoff
Capture this session as a Throughline handoff so any agent can resume it cleanly.
save-session-learnings
Document session learnings to CLAUDE.md and AGENTS.md. Use after completing significant tasks, debugging sessions, or discovering project patterns.
kiro-steering-custom
Command "kiro-steering-custom" from gotalab/cc-sdd, covering kiro custom steering creation, workflow, available templates, steering principles and tool guidance.