Row-Bot is a local-first desktop AI assistant that combines language models with memory and tools for working across files, repositories, workflows, and communication channels. It is intended for people who want an assistant that can run locally while keeping application data on their machine.
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 siddsachar/row-bot --skill task_automationgit clone --depth 1 https://github.com/siddsachar/row-botWrote 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/siddsachar/row-bot/task_automation)<a href="https://agentmods.dev/skills/siddsachar/row-bot/task_automation"><img src="https://agentmods.dev/badge/skills/siddsachar/row-bot/task_automation/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/siddsachar/row-bot/task_automation"><img src="https://agentmods.dev/badge/skills/siddsachar/row-bot/task_automation.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 | $0.00019 | $0.02784 |
| Opus 5.5 | $0.00008 | $0.01114 |
| Sonnet 5.5 | $0.00004 | $0.00557 |
| Haiku 4.5 | $0.00002 | $0.00278 |
Grade A, and why
task_automation 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 1mo 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When the user wants to set up automations, create recurring workflows, schedule something, or set a reminder, apply these principles:
How Tasks Work
A task is an ordered list of prompts executed sequentially in a dedicated thread. Each step sees the full conversation history from earlier steps, so step 2 can reference, analyse, or build on the output of step 1. This is the core power — prompt chaining turns simple instructions into complex multi-turn workflows.
There are three task types:
- Multi-step prompt tasks — the agent executes each prompt in order, accumulating context. Use for research, reports, gather-then-summarise workflows.
- Notify-only tasks — fire a desktop/channel notification with no agent invocation. Use for simple reminders and timers.
- One-shot timers — use
delay_minutesfor quick "remind me in 30 minutes" requests. These auto-delete after firing.
Prompt Chaining — The Core Pattern
-
Chain Steps That Build on Each Other — Design prompts so each step uses the output of the previous one. Example:
- Step 1: "Search for the latest news about AI regulation in the EU"
- Step 2: "Now summarise the key findings from above into 5 bullet points with source links"
- Step 3: "Draft a short email to my team highlighting the top 3 developments"
Step 2 works because it can see step 1's search results in the conversation. Step 3 works because it can see the summary.
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Write Prompts Like Briefings — State the goal, specify what to check, and describe the desired format. Vague prompts produce vague results. Each prompt should make it clear what the agent should do in that step.
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Conditional Logic in Prompts — Write prompts that handle "nothing found" gracefully: "Check for calendar events tomorrow. If there are none, just say 'Clear schedule tomorrow.' If there are events, list them with times and highlight any conflicts."
-
Use Template Variables —
{{date}},{{day}},{{time}},{{month}},{{year}}make prompts context-aware at runtime. "Summarise news for {{date}}" produces different results each day.
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.
- 1mo ago First seen · 186 lines · 19 tokens per session scan A 90fa6ab17b9f
task_automation is a skill published in the GitHub repository siddsachar/row-bot (1,632 stars, last pushed today), licensed Apache-2.0. It adds 19 tokens to every session and 2,784 once invoked, about $0.0001 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 2026-08-30.
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watch
A monitoring and briefing workflow that checks unread Telegram messages, email, failed checks, and failed scheduled units, then decides what needs the owner's attention. It can also explain actions such as creating a task, delaying a reminder, ignoring an item, or fixing a failure.
insight
A scheduled daily practice that looks for a repeated manual task, builds and tests a small tool for it, and suggests the result. It can instead report a development issue when the agent cannot solve its own problem.
brief
Brief — the owner's day in review: tasks, calendar, mail, Telegram, every Connection and plugin, goals from CORE, weather. Load in a scheduled Brief turn (08:30 and 14:00 by default), on /digest, and when the owner asks for a day plan, a review of the day or their open tasks.
email-assistant
A read-only assistant for reviewing incoming Yandex or Gmail messages from a chat.
scheduler
A scheduler for recurring tasks using cron, a time-based system for running jobs automatically. It can create, list and cancel repeating schedules, but does not support one-time reminders.