Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add billy-enrizky/openbrowser-ai/plugin install openbrowserWrote 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/billy-enrizky/openbrowser-ai/deep-research)<a href="https://agentmods.dev/skills/billy-enrizky/openbrowser-ai/deep-research"><img src="https://agentmods.dev/badge/skills/billy-enrizky/openbrowser-ai/deep-research.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 5.1 | $0.00082 | $0.07588 |
| Opus 5 | $0.00041 | $0.03794 |
| Sonnet 5 | $0.00016 | $0.01518 |
| Haiku 4.5 | $0.00008 | $0.00759 |
Grade A, and why
deep-research 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 8d 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 — 688 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Drive openbrowser-ai to investigate a topic across multiple web sources and produce a cited markdown report plus structured JSON. Two modes:
- flat synthesis (default) -- decompose query into 3-7 sub-questions, dispatch one parallel sub-agent per sub-question (each owns one tab), merge into one cited report.
- drilldown (auto-detected from prompt phrasing: "deep dive", "exhaustive", "recursive", "drilldown", "thorough") -- same as flat, plus a second wave of parallel sub-agents on findings flagged
needs_depth=true. Hard cap depth=2, max 3 follow-up sub-agents per parent.
Output paths (relative to current project root):
local_docs/research/YYYY-MM-DD-<slug>.mdlocal_docs/research/YYYY-MM-DD-<slug>.json
Architecture (mandatory): the orchestrating Claude session (the one running this skill) MUST dispatch parallel sub-agents via /dispatching-parallel-agents, one sub-agent per sub-question. Each sub-agent owns exactly ONE tab. Sub-agents do not open additional tabs. The orchestrator merges per-agent findings into one report.
Why one tab per sub-agent and not asyncio.gather over tabs in a single -c call: a single Python coroutine driving N tabs through one daemon serializes navigation events at the CDP layer, contends for the LLM-extraction worker, and cannot make independent decisions about pagination or follow-up clicks per tab. Dispatching real Claude sub-agents (each with its own context window and its own browser tab) gives true parallelism, independent reasoning per tab, and isolates failures so one bad page doesn't poison the rest.
Hard rules:
- One sub-agent = one tab. Sub-agents must NOT call
navigate(url, new_tab=True)to spawn additional tabs. - All sub-agents share the same daemon (and so the same Chrome process). Tabs are isolated; navigation in one tab does not affect another.
- Each sub-agent writes its findings to its own JSON file under
local_docs/research/_partial/<slug>-NN.json. The orchestrator reads and merges these. - The orchestrator never drives tabs itself. It only plans, dispatches, merges, renders, verifies, cleans up.
If a first-wave sub-agent returns <2 findings, the orchestrator dispatches a Step 2b retry sub-agent with broader search strategy (alternative engines, query reformulation, lower thresholds). Still -c-only: the skill never calls openbrowser-ai -p.
Variables persist across -c calls in the daemon namespace.
Session reuse: Step 0 checks openbrowser-ai daemon status. If a daemon is already running (warm browser), the skill reuses it and operates in NEW tabs (never disturbs the user's existing tabs). If no daemon, the skill auto-starts one on first -c call.
Every factual claim in the report carries a footnote citation [N]. Verifier fails the run if uncited prose is found.
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.
- 8d ago First seen · 688 lines · 82 tokens per session scan E 14c5c643dc96
deep-research is a skill published in the GitHub repository billy-enrizky/openbrowser-ai (241 stars, last pushed 2mo ago), licensed MIT. It adds 82 tokens to every session and 7,588 once invoked, about $0.0004 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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