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webthinker-deep-research

Research
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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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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/foryourhealth111-pixel/Vibe-Skills/blob/HEAD/bundled/skills/webthinker-deep-research/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/webthinker-deep-research/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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.