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):

1. Scaffold outputs:

• python C:\Users\羽裳\.codex\skills\webthinker-deep-research\scripts\init_webthinker_run.py --topic "…" --out outputs/webthinker

1. Search (broad → narrow):

• Use web.run search queries or mcp__tavily__tavily_search if available.

1. Browse/extract:

• Use web.run open/click/find for structured pages
• Use playwright when pages require dynamic rendering / interactions

1. Draft + iterate:

• Update notes.md and sources.json continuously
• Write report.md as you go (think-search-and-draft), not only at the end

1. Verification:

• Triangulate key claims across ≥2 sources when possible
• Flag uncertainties explicitly

Mode B (Optional): Full WebThinker stack

Only choose this if you want to run the upstream system end-to-end and you have the environment:

• Requires heavy deps (torch, transformers, vllm) + a served reasoning model
• Requires a search API (Serper recommended by upstream)
• Optional: Crawl4AI parser client for JS-heavy pages

This mode is for high-throughput deep research runs; for most VCO tasks, Lite mode is enough and cheaper.

Action trace format (trace.jsonl)

Each line is one JSON object, e.g.:

• {"ts":"…","type":"search","query":"…","provider":"web.run"}
• {"ts":"…","type":"open","url":"…"}
• {"ts":"…","type":"extract","url":"…","highlights":["…","…"]}
• {"ts":"…","type":"decision","reason":"why this source matters","next":"…"}

Quality gates

• Every major claim in report.md links back to at least one entry in sources.json.
• sources.json contains the exact URLs you used (no “I saw somewhere…”).
• Keep the report actionable: add “Next steps” with concrete verification tasks.