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 research-reportgit 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/research-report)<a href="https://agentmods.dev/skills/shenseanchen/waku-agent/research-report"><img src="https://agentmods.dev/badge/skills/shenseanchen/waku-agent/research-report/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/research-report"><img src="https://agentmods.dev/badge/skills/shenseanchen/waku-agent/research-report.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 | $0.00000 | $0.01658 |
| Opus 5.5 | $0.00000 | $0.00663 |
| Sonnet 5.5 | $0.00000 | $0.00332 |
| Haiku 4.5 | $0.00000 | $0.00166 |
Grade A, and why
research-report 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write a report when the person asks you to research companies, competitors, markets, products or people, or asks you to save a brief, summary, snapshot or report built from what your tools found (an audience brief from Reddit comments, an AI visibility snapshot). Each of these is one report in this format, never a set of separate memories. Anything else gets a normal answer.
Start from what is known
With Waku Memory connected, Waku searches it first and lists the hits, each with its date, under "What the company brain already knows".
Start there: name an earlier report and its date, list it in Sources ("via": "waku-memory"), and research only what is missing or older than 30 days.
Each earlier report is listed as its summary; call waku_memory_memory_get for a whole one only when the person asks to compare details.
Cheap first
- Prefer free and preview endpoints, and keep
limitsmall (5 rows unless asked). Long tool results are cut to their opening once you have read them, so write down in a line or two what you will use from each before your next call. - Read
catalog_get's price before any paid call. - Ask before this turn's treg spend would pass $0.25, saying what it would buy.
- Call what treg charged the "treg cost", or name each endpoint and its cost. Never state a total, a model cost, or what the run or turn cost: your own tokens cost money too, and you cannot see that number. The receipt under the reply shows the totals.
- No cost tile in Numbers;
cost_usdin Sources is the only cost the report carries.
The reply
Two or three plain sentences on what you found, then the report from its marker line (once per reply). The chat keeps only your sentences, so they must stand alone. Waku saves the report to Waku Memory itself, once: never save it with memory_remember. Never write about the save in the reply. Do not say the report will be saved, was saved, or that you have not seen a confirmation: the chat shows "Report saved" under your sentences. The reply ends with the report's last section.
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.
- 10d ago Changed a68d7d319d7a
- 12d ago First seen · 86 lines · 0 tokens per session scan A add3a27cfcca
research-report is a skill published in the GitHub repository ShenSeanChen/waku-agent (2,023 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,658 tokens. 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 2026-10-04.
Other skills, from other repositories
migrating-mastra-to-pydantic-ai
Migrate TypeScript Mastra applications to Python with Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports @mastra/ or relies on Mastra agents, tools, workflows, memory, processors, streaming, approvals, skills, or subagents.
migrating-vercel-ai-sdk-and-eve-to-pydantic-ai
Migrate TypeScript Vercel AI SDK or Eve applications to Python with Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports ai, @ai-sdk/, or eve, or relies on generateText, streamText, ToolLoopAgent, useChat, Eve agents, tools, skills, subagents, approvals, sessions, channels, schedules…
migrating-openai-agents-sdk-to-pydantic-ai
Migrate Python OpenAI Agents SDK applications to Pydantic AI and, when warranted, Pydantic AI Harness. Use for agents.Agent, Runner, function tools, handoffs, guardrails, sessions, human approval, streaming, or SandboxAgent. Do not use for applications built directly on the OpenAI Responses API without the Agents SDK…
hep-search
Search Agentlas Cloud and Hub agent candidates without invoking.
slacrawl
Archive and search Slack workspace messages, threads, and channels via the slacrawl CLI. Supports API sync, Slack export ZIP import, local desktop cache import, and full-text search.
obsidian
Read, search, and create notes in the Obsidian vault.