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

2. Search (broad → narrow):
   • Use

     web.run

     search queries or

     mcp__tavily__tavily_search

     if available.
3. Browse/extract:
   • Use

     web.run open/click/find

     for structured pages
   • Use

     playwright

     when pages require dynamic rendering / interactions
4. Draft + iterate:
   • Update

     notes.md

     and

     sources.json

     continuously
   • Write

     report.md

     as you go (think-search-and-draft), not only at the end
5. 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.