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 skills add khef-labs/khef-mac --skill remgit clone --depth 1 https://github.com/khef-labs/khef-macWrote 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/khef-labs/khef-mac/rem)<a href="https://agentmods.dev/skills/khef-labs/khef-mac/rem"><img src="https://agentmods.dev/badge/skills/khef-labs/khef-mac/rem/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/khef-labs/khef-mac/rem"><img src="https://agentmods.dev/badge/skills/khef-labs/khef-mac/rem.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 | $0.00071 | $0.01768 |
| Opus 5.5 | $0.00028 | $0.00707 |
| Sonnet 5.5 | $0.00014 | $0.00354 |
| Haiku 4.5 | $0.00007 | $0.00177 |
Grade A, and why
rem 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
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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 · 158 lines · 71 tokens per session scan A 7813ea1300d3
rem is a skill published in the GitHub repository khef-labs/khef-mac (2 stars, last pushed 5d ago), with no licence file. It adds 71 tokens to every session and 1,768 once invoked, about $0.0003 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
memory
Retrieve relevant durable BB memories or save verified knowledge useful to future threads.
review-ui-leak-via-listeners
Normative reviewer for memory leaks where UI components or view classes are retained by listeners on properties of long-lived services (APP/Project) or by strong references in singletons (object, companion object, static fields). Use when reviewing tool-window panels, settings pages, dialogs, wizard steps, or any code…
chronicle
Analyze indexed Aily chat session history for prior decisions, tool executions, project summaries, usage tips, and session-store reindexing.
agent-team-os
Run a team of AI agents (or one agent in several role sessions) as one governed unit — layered long-term memory, hub-coordinated governance with evidence anchors and human approval gates, an identity-based agent mailbox with receipts, and an event-driven doorbell that wakes seats on new mail without polling. Use when…
squads-learn
Capture learnings after completing work. Use when finishing a task, fixing a bug, discovering a pattern, or learning something worth remembering for future sessions. Helps build institutional memory.
agent-engineering-expert
Build LLM agents that use tools safely: tool design, the agent loop, memory, MCP servers, multi-agent orchestration, sandboxing and prompt-injection defence. Use when the user mentions AI agents, tool use or function calling, MCP or Model Context Protocol, autonomous workflows, multi-agent systems, LangChain or…