Searching Precisely

Overview

Web search pipeline that minimizes token consumption via local intent classification, semantic caching, credibility validation, and streaming fragment assembly.

架构分工：

• **宿主 AI（Host Agent）**负责实际的网页 GET / Search API 调用，返回原始 fragments
• 本 Skill 的脚本负责前处理（intent 分类、query 改写、budget 控制、cache 查询）和后处理（credibility probe、stream 组装、cache 写入）

Core Rule: Always check the semantic cache first. Only invoke web search on a cache miss.

Pipeline Architecture

Query → [Intent Parser] → [Query Rewriter] → [Budget Controller]
                                                     ↓
                                           [Semantic Cache] ──hit──→ Return
                                                     ↓ miss
                                           [Web Search]  (≤1500 tok)
                                                     ↓
                                           [Parallel Credibility Probe]
                                                     ↓
                                           [Stream Assembler] → [Write Cache]

Instructions

When this skill activates, execute the pipeline below in order. Exit early at any step that produces a final answer — do not run later steps unnecessarily.

Note: Replace <placeholders> with actual runtime values. All arguments must be valid JSON strings.

Step 1 — Classify Intent

Run via shell tool:

node scripts/intent-parser.js '<original_query>'

Extract intent and confidence from the JSON output.
If confidence < 0.5, default to intent = "web_search" and continue.

Step 2 — Initialize Budget

node scripts/budget-controller.js init

Keep the returned state.remaining value. Abort any later step that would exceed it.

Step 3 — Check Semantic Cache

node scripts/semantic-cache.js check '{"query":"<original_query>","intent":"<intent>"}'

• hit: true and similarity ≥ 0.85 → return result to the user. Pipeline complete. Skip all remaining steps.
• hit: false → continue to Step 4.

Step 4 — Rewrite Query

node scripts/query-rewriter.js '{"intent":"<intent>","query":"<original_query>"}'

Use the returned subQueries array (max 3) for web search.

Step 5 — Web Search (host agent)

Using your native search_web tool, search each sub-query from Step 4.
Collect result URLs and content fragments.
Always perform live search on a cache miss — never fabricate results.

Step 6 — Validate Source Credibility

Extract up to 5 unique source URLs from Step 5. Run:

node scripts/parallel-probe.js '{"sources":[{"url":"<url1>"},{"url":"<url2>"}]}'

• verdict: "trust" → use directly
• verdict: "verify" → use with caution; flag in the answer
• available: false → discard that source

Step 7 — Score Credibility

node scripts/credibility-arbiter.js '{"results":[<probe_results_array>]}'

If all sources score < 0.4, discard everything and tell the user no reliable source was found. Do not assemble.

Step 8 — Assemble Answer

node scripts/stream-assembler.js '{"fragments":[<trusted_fragments>],"query":"<original_query>"}'

Return the answer field to the user.
If coherenceScore < 0.5, add a note that the result may be incomplete.

Step 9 — Write Cache

node scripts/semantic-cache.js write '{"query":"<original_query>","intent":"<intent>","result":"<answer>"}'

This caches the result for future similar queries. Always run this step after a successful assembly.

Search Decision Rules

Token Budget Defaults

Common Mistakes

• ❌ Calling web search before checking the semantic cache
• ❌ Skipping credibility check for unknown domains
• ❌ Assembling fragments from a single unverified source
• ❌ Fetching full page content when a HEAD probe + summary suffices

ClawHub Security Notes

All permission declarations, dependencies, I/O scopes, and security properties are formally declared in the YAML frontmatter above (permissions, dependencies, io, security_notes). Static analyzers should use those fields as the authoritative source.

Summary per script: