hellosearch

name

hellosearch

description

Host-agnostic real-web research, source verification, and evidence synthesis using the current environment's native search and page tools. Use when a task needs live web search, latest fact checking, official-document lookup, news or product research, release-note or changelog verification, source-backed comparison, documentation-site mapping, or any answer that should cite current web sources; also use when the user says “use hellosearch”, “用 hellosearch 搜”, “帮我搜一下”, “查官网确认”, or asks to verify what is true now without adding third-party APIs or backends.

HelloSearch

Overview

Use the current host's real web capability to run disciplined search workflows without assuming any fixed vendor backend or third-party search service.

Treat hellosearch as a pure skill. It should guide and structure how the host searches the web, not introduce extra integration layers or backend requirements.

Workflow

1. Detect the live-search path

• Prefer the current host's native web search tool when available.
• Pair native search with native page open or fetch tools when the question needs full-page context.
• Use site mapping when the task asks for all docs pages, site structure, endpoint inventories, or broad documentation coverage.
• If no real web path exists, say so clearly and stop claiming live verification.
• Read references/host-routing.md when the backend choice is unclear.

2. Frame the query before searching

• Distill the core question, required freshness, target geography, likely primary sources, and whether the task needs mapping before fetching.
• Rewrite broad asks into concrete search rounds and sub-queries before searching.
• Turn relative dates such as “today”, “latest”, or “this week” into exact dates in both the search and the answer.
• Add domain filters when official docs, standards bodies, vendors, or repositories exist.
• For complex or time-sensitive tasks, run python scripts/plan_search.py "<question>" --json first and use the generated complexity, sub-queries, and execution order as your checklist.

3. Search broad, then narrow

• Start with broad discovery queries to map the landscape.
• Follow with targeted queries against official docs, release notes, repos, changelogs, standards, filings, or primary reporting.
• If the task is documentation-wide, use site mapping first, then fetch the most relevant pages.
• For technical topics, search English first unless the task is clearly China-local or the source of truth is Chinese.
• For comparisons, collect the same facts for each option before judging.

4. Fetch, verify, and reconcile

• Open or fetch the strongest sources instead of answering from snippets alone.
• Cross-check unstable or high-stakes claims across multiple independent sources.
• Reconcile conflicts in titles, dates, versions, and pricing before answering.
• If the host returns mixed answer-plus-citation text, run python scripts/extract_sources.py --input answer.md or pipe the text through the script before ranking.
• Treat official docs, release notes, standards, repos, and direct statements as higher priority than summaries or reposts.
• Read references/evidence-policy.md when the answer needs stronger source discipline.
• After collecting candidate sources, run python scripts/rank_sources.py "<question>" --input sources.json to dedupe and rank them before synthesis.

5. Synthesize the answer

• Lead with the best-supported conclusion.
• Separate confirmed facts, reasonable inference, and unresolved uncertainty.
• Include direct links when the host allows them.
• Mention exact dates for time-sensitive facts.
• Name the source type when it helps the user judge confidence.

Guardrails

• Do not rely on model memory for unstable facts.
• Do not pretend a generic chat/completions model has real web access unless the environment actually provides it.
• Do not turn this skill into a vendor-specific wrapper or backend-dependent shortcut.
• Do not hide missing evidence, ambiguity, backend limits, or site-map gaps.
• Do not answer from a single snippet when the task requires page-level context.

Trigger examples

• “用 hellosearch 搜一下这个库最近的 breaking changes”
• “Use hellosearch to verify today's NVIDIA news”
• “查官网文档，确认这个 API 现在的参数”
• “Use hellosearch to map this docs site before answering”
• “用 hellosearch 比较这三个产品，给出处和更新时间”

Resources

• Run scripts/detect_runtime.py to inspect the current workspace and choose the first routing target.
• Run scripts/plan_search.py "<question>" --json to generate one structured search plan with complexity, sub-queries, and execution order.
• Run scripts/build_workflow.py "<question>" to generate one combined routing + workflow bundle.
• Run scripts/extract_sources.py --input answer.md when host output mixes answer text with trailing citations or raw links.
• Read references/host-routing.md when deciding which search path to use in Codex, Claude Code, or OpenClaw.
• Read references/evidence-policy.md when ranking sources, handling freshness, parsing mixed citation blocks, or formatting a source-backed answer.
• Read references/runtime-adapters.md when implementing or extending a host-agnostic search backend.