Borrowing it
Nothing to install: this file belongs to oyekamal/mnemo-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/oyekamal/mnemo-agent/master/.claude/skills/remember/SKILL.mdgit clone --depth 1 https://github.com/oyekamal/mnemo-agentWrote 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/skills/oyekamal/mnemo-agent/remember)<a href="https://agentmods.dev/skills/oyekamal/mnemo-agent/remember"><img src="https://agentmods.dev/badge/skills/oyekamal/mnemo-agent/remember/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/skills/oyekamal/mnemo-agent/remember"><img src="https://agentmods.dev/badge/skills/oyekamal/mnemo-agent/remember.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 | $0.00050 | $0.00134 |
| Opus 5.5 | $0.00020 | $0.00054 |
| Sonnet 5.5 | $0.00010 | $0.00027 |
| Haiku 4.5 | $0.00005 | $0.00013 |
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 2mo 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 2mo ago First seen · 12 lines · 50 tokens per session scan A f8f916aab0fc
remember is a skill published in the GitHub repository oyekamal/mnemo-agent (3 stars, last pushed 3mo ago), with no licence file. It adds 50 tokens to every session and 134 once invoked, about $0.0002 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 skills, from other repositories
loop-memory
Give the agent episodic memory so it improves every cycle instead of repeating mistakes. Use this skill whenever working in a project that has a .loop/LESSONS.md file, whenever a task is being retried after a failure, whenever the user says "remember this", "don't make that mistake again", "learn from this", or "why…
principle-guard-the-context-window
Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.
memclaw
The agent's persistent long-term memory — the only knowledge that survives across sessions, shared across the fleet under access control. Consult it at the start of a task to recall prior decisions, findings, and rules before acting, and write outcomes, decisions, and lessons as work completes. Use whenever a caura…
company-brain
How an agent should operate as one mind inside a shared Company Brain built on Caura — recall before acting, obey fleet keystones, reuse and publish skills, and report outcomes so every task compounds across the whole organization. Use this whenever you work as part of a Caura-connected team or fleet and your work…
compass-integrity
Use when the user is about to rely on prior session memory for a non-trivial decision, or when something feels off about a recall hit — runs the Merkle chain check, drift summary, and daemon liveness in one pass and surfaces any tampering, drift bursts, or daemon-down state before the user acts on stale or corrupted…
lifecycle_report
Scan project memory for v1.7.1 lifecycle frontmatter · report tier distribution + promotion candidates (reinforcecount >= promoteafter) · NO LLM.