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/NicolasPrimeau/artelWrote 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/nicolasprimeau/artel/artel-remember)<a href="https://agentmods.dev/commands/nicolasprimeau/artel/artel-remember"><img src="https://agentmods.dev/badge/commands/nicolasprimeau/artel/artel-remember.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 5.1 | $0.00014 | $0.00110 |
| Opus 5 | $0.00007 | $0.00055 |
| Sonnet 5 | $0.00003 | $0.00022 |
| Haiku 4.5 | $0.00001 | $0.00011 |
Grade A, and why
artel-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 8d 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
Write the following to Artel shared memory so the rest of the fleet has it: $ARGUMENTS
Use mcp__plugin_artel_artel__memory_write. Choose a sensible entry_type (default memory; use skill if it is procedural how-to), add useful tags, and make the content self-contained so it stands on its own later. Confirm what you stored.
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.
- 8d ago First seen · 9 lines · 14 tokens per session scan A fc1a0292f7fc
artel-remember is a command published in the GitHub repository NicolasPrimeau/artel (8 stars, last pushed 4d ago), licensed MIT. It adds 14 tokens to every session and 110 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
arrecall
AgentRecall on-demand recall — surface past fixes, decisions, and patterns mid-session without leaving your flow.
arreflect
AgentRecall consolidation & reflection — periodic triage of recurring corrections; proposes rule changes, never applies them without the owner.
arsave
AgentRecall full save — journal + palace + awareness + insights in one shot.
dream
Run a judgment session over the Pseudolife memory bank — triage the review queues; extract facts only where no extractor can.
memory-status
Check the Pseudolife-MCP memory daemon and report bank health.
dreaming
Configure and run the dreaming subsystem that consolidates, combines, and prunes memories on a wall-clock floor. Invoke when the user asks to turn dreaming on/off, change how often it runs, run a one-shot dreaming pass, or check when dreaming last fired. Defaults: mode=auto, interval=30m for auto / 24h for on, include…