/deep-research

name: deep-research
description: |
  Conduct deep web research using the openbrowser-ai agent: decompose a query, investigate sub-questions across multiple sources, and produce a cited markdown report plus structured JSON under local_docs/research/.
  Trigger when the user asks to: research a topic, do a deep dive, investigate, gather evidence, compare options, write a literature review, build a briefing, or produce a cited report.
allowed-tools: Bash(openbrowser-ai:*) Bash(curl:*) Bash(uv:*) Bash(irm:*) Bash(mkdir:*) Bash(date:*) Read Write

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>.md`
• `local_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.

Setup

Verify install:

openbrowser-ai --help

Install if missing:

# macOS / Linux
curl -fsSL https://openbrowser.me/install.sh | sh

# Windows PowerShell
irm https://openbrowser.me/install.ps1 | iex

No LLM API key required. The skill drives the daemon via `openbrowser-ai -c` only, which executes raw CDP / JS through the daemon's Python namespace and never invokes a model. (The `-p` "prompt mode" of the CLI is a separate code path that loads `get_llm()` and requires an OpenAI / Anthropic / Google key per `cli.py:434-490`. This skill explicitly avoids `-p`.)

Set the headless env var so the daemon starts without a visible browser window (the default in `daemon/server.py` is already `headless: True`, but a user config file can override it; this env var wins over config):

export OPENBROWSER_HEADLESS=true

Prepare output dir at the project root (NOT user home):

mkdir -p local_docs/research

Workflow

Step 0 -- Session check

Enforce headless mode and detect whether a daemon is already running. If yes, reuse it (operate in NEW tabs only). If no, the next `-c` call auto-starts one.

`OPENBROWSER_HEADLESS=true` is set here so the daemon spawned by the first `-c` call inherits it, even if the user's config file sets `headless: false`. Already-running daemons are unaffected (their browser was opened at start time).

export OPENBROWSER_HEADLESS=true

if openbrowser-ai daemon status 2>&1 | grep -qi 'running\|listening\|pid'; then
    echo "Reusing existing daemon -- will work in new tabs"
    export DEEP_RESEARCH_REUSED=1
else
    echo "No daemon running -- will start fresh headless session"
    export DEEP_RESEARCH_REUSED=0
fi

Snapshot existing tabs so cleanup leaves them untouched:

openbrowser-ai -c - <<'EOF'
state = await browser.get_browser_state_summary()
_preexisting_tab_ids = {t.target_id for t in state.tabs} if state.tabs else set()
print(f"Pre-existing tabs: {len(_preexisting_tab_ids)}")
EOF

Step 1 -- Plan

Decompose the user query into sub-questions and pick the mode. Daemon namespace persists `_plan` across later `-c` calls.

openbrowser-ai -c - <<'EOF'
import json, re, datetime, os

QUERY = """<USER_QUERY>"""  # paste exact user query here

# Daemon CWD often != shell CWD. Hard-code the absolute project root.
# Set this to the shell CWD at the start of the run; do NOT rely on os.getcwd().
PROJECT_ROOT = "<ABSOLUTE_PATH_TO_PROJECT_ROOT>"  # e.g. /Users/foo/myproject

# Auto-detect mode
DRILL_RE = re.compile(r"\b(deep ?dive|exhaustive|recursive|drill ?down|thorough)\b