ConnectOnion is an open-source, template-first toolkit for building, debugging, deploying, and operating AI agents. Developers use its command-line tools and Python runtime to create agents, add tools, connect services, deploy them, and make them callable by other agents, while the catalogue entries are related agents, skills, and instructions.
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 openonion/connectonion --skill browser-workflow-skill-buildergit clone --depth 1 https://github.com/openonion/connectonionWrote 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/openonion/connectonion/browser-workflow-skill-builder)<a href="https://agentmods.dev/skills/openonion/connectonion/browser-workflow-skill-builder"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/browser-workflow-skill-builder/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/openonion/connectonion/browser-workflow-skill-builder"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/browser-workflow-skill-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 16 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- high Rogue Agent · line 488 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- medium Excessive Agency · line 337 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Rogue Agent · line 379 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00061 | $0.07028 |
| Opus 5.5 | $0.00024 | $0.02811 |
| Sonnet 5.5 | $0.00012 | $0.01406 |
| Haiku 4.5 | $0.00006 | $0.00703 |
Grade A, and why
browser-workflow-skill-builder 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 — 620 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Browser Workflow Skill Builder
Create or improve browser automation skills for logged-in web apps. Use this when building skills that read a page, choose the correct item, generate or prepare content, click/type/upload, or submit/post/publish.
The core pattern is: save context -> analyze DOM/CSS -> write skill-local JS extraction -> verify same item by hash -> click/type with generic browser tools -> screenshot/context verify.
Designing the command surface of a co <thing> CLI and its SKILL.md, rather than a browser workflow on top of one? That is the sibling skill cli-skill-design.
Architecture Rule
Do not add site-specific Python to the core browser tool.
Use generic browser primitives — run each as co browser <function>, positional args in order, options as --flag=value:
co browser save_page_context <name>
co browser take_screenshot
co browser run_page_script <script_path> '<args_json>'
co browser run_frame_script <script_path> '<args_json>' --frame_url_contains=<substring> --frame_name=<name>
co browser click_element_by_selector <selector> --index=<n> --text='<visible text>'
co browser type_text_by_selector <selector> '<text>' --index=<n>
co browser click_element_near_selector <anchor_selector> <target_selector> --target_text='<text>' --container_selector=<selector>
co browser upload_file_by_selector <selector> <file_path> --frame_name=<name>
co browser upload_file_after_click_by_selector <click_selector> <file_path> --text='<button text>' --frame_name=<name>
Put site-specific selectors and DOM logic in the target skill:
.co/skills/<target-skill>/
SKILL.md
scripts/
extract-<items>.js
verify-<item>.js
run_page_script(...) runs JS in the main document of the current browser page, so it shares the same browser context, login cookies, current tab, and DOM state.
run_frame_script(...) runs the same page-function script across matching frames and returns per-frame results. Use it when elements.json shows the target element has a non-main frame, when the UI is rendered through iframe/frame-like surfaces, or when main-document selectors cannot see a visible modal/composer.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 620 lines · 61 tokens per session scan A d6ef52e54e35
browser-workflow-skill-builder is a skill published in the GitHub repository openonion/connectonion (1,499 stars, last pushed today), licensed Apache-2.0. It adds 61 tokens to every session and 7,028 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 2026-08-30.
Other skills, from other repositories
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firecrawl
Search the web and scrape pages into clean markdown with the Firecrawl API — query-based discovery, single-URL extraction including public PDFs, driven by curl with a vault-stored API key.
agentlas-browser
Use when the user types /agentlas-browser , /agentlas browser, or /hep-browser for browser-required tasks, rendering pages, form flows, or visual evidence.
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agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a…
p5.js
Production pipeline for interactive and generative visual art using p5.js. Creates browser-based sketches, generative art, data visualizations, interactive experiences, 3D scenes, audio-reactive visuals, and motion graphics — exported as HTML, PNG, GIF, MP4, or SVG. Covers: 2D/3D rendering, noise and particle systems…