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Webthinker Deep Research
Deep web research for VCO: multi-hop search+browse+extract with an auditable action trace and a structured report (WebThinker-style).
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# WebThinker Deep Research (VCO) ## When to use Use this skill when the task requires **deep web research** (not just one-shot search), for example: - Multi-hop questions (“find → open → follow links → verify”) - “Deep research report” / “调研报告” / “竞品调研” / “技术调研” - Need an **auditable trace** of web actions and sources - Need to merge findings into a structured deliverable (report / brief / spec) ## Non-goals (avoid redundancy) - For **quick citations** or “give me 3 sources”, prefer `research-lookup`. - For **interactive UI flows** (login / forms / downloads), prefer `playwright` or `turix-cua` overlays. - For **codebase structure / call chains**, prefer GitNexus overlays (not web research). ## Output contract (must) Produce a folder with: - `report.md` — structured report (problem → findings → implications → next steps) - `sources.json` — all sources (URL/title/access time/snippet) - `trace.jsonl` — append-only action trace (search/open/extract/decision) - `notes.md` — working notes with per-source anchors Use `scripts/init_webthinker_run.py` to scaffold the folder. ## Runtime (Upstream vendoring) This VCO skill supports a **stable Lite mode** by default, and keeps the upstream WebThinker repo **vendored** for optional advanced use. - Vendored upstream paths: - `C:\Users\羽裳\.codex\_external\ruc-nlpir\WebThinker\` - Runtime config (no secrets stored): - `C:\Users\羽裳\.codex\skills\vibe\config\ruc-nlpir-runtime.json` - Preflight / install (no secrets echoed): - `pwsh C:\Users\羽裳\.codex\skills\vibe\scripts\ruc-nlpir\preflight.ps1` - Manually create an isolated venv for the vendored runtime and install only the minimal packages you need. The old `install-upstreams.ps1` auto-install path has been removed on purpose. LLM endpoint conventions (recommended): - Base URL: `OPENAI_BASE_URL` (or runtime default) - API key: `OPENAI_API_KEY` (**env var only; never write into files or CLI args**) ## Modes ### Mode A (Recommended): Lite — tool-orchestrated deep research Use existing tools (no heavy model hosting):
#broad-capability#creative#deep#researchplaywrightpythonweb-search