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 agentmods add skills/openonion/connectonion/browser-workflow-skill-buildernpx 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.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.07028 |
| Opus 5 | $0.00030 | $0.03514 |
| Sonnet 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 5d 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.
- 5d 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 (1,481 stars, last pushed today), licensed Apache-2.0. It adds 61 tokens to every session and 7,028 once invoked, about $0.0003 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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