Waku Agent is a local-first personal AI assistant whose readable code implements the agent loop, memory, and evaluation system. It is for people who want an assistant they can run and understand on their own laptop, with memory stored in SQLite and built-in testing. The catalogue add-ons support its agent workflow.
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 ShenSeanChen/waku-agent --skill schedule-meetinggit clone --depth 1 https://github.com/ShenSeanChen/waku-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/shenseanchen/waku-agent/schedule-meeting)<a href="https://agentmods.dev/skills/shenseanchen/waku-agent/schedule-meeting"><img src="https://agentmods.dev/badge/skills/shenseanchen/waku-agent/schedule-meeting/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/shenseanchen/waku-agent/schedule-meeting"><img src="https://agentmods.dev/badge/skills/shenseanchen/waku-agent/schedule-meeting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.00308 |
| Opus 5 | $0.00020 | $0.00154 |
| Sonnet 5 | $0.00008 | $0.00062 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
schedule-meeting 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 12d 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
How to schedule well
- Resolve relative dates ("next Tuesday", "tomorrow morning") into ISO 8601 using today's date from the system prompt. Morning = 09:00, afternoon = 14:00, evening = 18:00 unless the user says otherwise.
- Check memory context for the attendee's preferences (e.g. "prefers morning meetings") and apply them — mention it when you do ("since Alex prefers mornings, I booked 9am").
- Call
create_eventwith a short, specific title: "Coffee with Alex", not "Meeting". - If the user mentioned an agenda or context, put it in
notes. - After creating, confirm in one sentence: what, when, with whom.
Edge cases
| Situation | Do |
|---|---|
| No time given | Propose a concrete time instead of asking an open question — if memory shows the attendee's preference, lead with it ("Alex prefers mornings — Friday 9am?"). Only ask openly when memory gives you nothing |
| Past date requested | Point it out, suggest the next occurrence |
| Attendee unknown to memory | Schedule anyway; offer to save_note who they 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.
- 12d ago First seen · 26 lines · 40 tokens per session scan A 1e57b3a89625
schedule-meeting is a skill published in the GitHub repository ShenSeanChen/waku-agent (1,720 stars, last pushed 13d ago), licensed MIT. It adds 40 tokens to every session and 308 once invoked, about $0.0002 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 2026-08-30.
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