How do I install the Web RAG skill?

Run npx skillmds@latest add majiayu000/web-rag in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Web RAG safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Web RAG?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Web RAG free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Web RAG?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Web RAG by majiayu000 — Agent Skill

Name: Web RAG
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

Web RAG Skill

Purpose

Vector search for the web codebase. Semantic search across luz_next application code, klara-theme component libraries, design tokens, and implementation patterns via MCP tools.

MCP Tools

Tool	Purpose
`query`	Semantic search — returns code chunks with relevance scores
`status`	Health check, indexed modules, document counts
`catalog`	List available components/hooks/styles per module (no vector search)
`navigate`	O(1) shortcut to known code locations
`expansions`	Component alias mappings + synonym groups for query planning

Tool parameters are self-describing — check inputSchema for current options.

Discovery Protocol

Before querying, discover what's indexed:

Call status — returns available modules, document counts, health
Call catalog (optional) — lists components/hooks/styles for discovery
Call expansions (once per session) — get component aliases and synonym groups

Use discovered values for module, filters.scope, and filters.component params.

When to Use

Scenario	RAG	Grep/Glob	Context7
Find component by concept	yes	if know filename	no
Search design tokens	yes	yes	no
Library API docs	no	no	yes
Pattern across codebase	yes	exact match only	no
Discover available components	catalog	no	no

Query Strategy

Known component or file? -> query + component filter (get canonical name from expansions)
Known topic area? -> query + topic/file_type filters (discover valid values via status)
Conceptual/cross-cutting? -> Smart query with HyDE (see references/smart-query.md)
**< 3 results or low scores?** -> Broaden: remove filters, try synonyms, alternate casing
Still nothing? -> Fall through to Grep/Glob (codebase search)

Rules

Start broad, refine with filters — not longer queries
Natural language works best — "button with loading state" over "btn loading"
Low relevance scores suggest rephrasing needed
If server offline, fall back to Grep/Glob
stale_sidecar: true in results — trust code chunks, ignore metadata fields
Do NOT generate synonym variants — server handles expansion automatically

Integration

Priority level 2 in knowledge-retrieval chain:

docs/ files -> 2. RAG -> 3. Skills/codebase -> 4. Context7

Related Skills

knowledge-retrieval — Orchestrates source priority
web-frontend — Frontend patterns (enhanced by this skill)
docs-seeker — External documentation lookup

References

references/smart-query.md — HyDE + multi-query retrieval strategy
references/sidecar-workflow.md — AI-generated metadata enrichment
references/component-mappings.md — Get canonical names via expansions MCP tool
references/synonym-groups.md — Get synonym groups via expansions MCP tool

---name: web-ragdescription: (ePost) Use when searching web codebase for components, design tokens, Next.js patterns, or any luz_next implementation via vector search--- # Web RAG Skill ## Purpose Vector search for the web codebase. Semantic search across luz_next application code, klara-theme component libraries, design tokens, and implementation patterns via MCP tools. ## MCP Tools | Tool | Purpose | |------|---------| | `query` | Semantic search — returns code chunks with relevance scores | | `status` | Health check, indexed modules, document counts | | `catalog` | List available components/hooks/styles per module (no vector search) | | `navigate` | O(1) shortcut to known code locations | | `expansions` | Component alias mappings + synonym groups for query planning | Tool parameters are self-describing — check inputSchema for current options. ## Discovery Protocol Before querying, discover what's indexed: 1. **Call `status`** — returns available modules, document counts, health 2. **Call `catalog`** (optional) — lists components/hooks/styles for discovery 3. **Call `expansions`** (once per session) — get component aliases and synonym groups Use discovered values for `module`, `filters.scope`, and `filters.component` params. ## When to Use | Scenario | RAG | Grep/Glob | Context7 | |----------|-----|-----------|----------| | Find component by concept | yes | if know filename | no | | Search design tokens | yes | yes | no | | Library API docs | no | no | yes | | Pattern across codebase | yes | exact match only | no | | Discover available components | catalog | no | no | ## Query Strategy 1. **Known component or file?** -> `query` + component filter (get canonical name from `expansions`) 2. **Known topic area?** -> `query` + topic/file_type filters (discover valid values via `status`) 3. **Conceptual/cross-cutting?** -> Smart query with HyDE (see `references/smart-query.md`) 4. **< 3 results or low scores?** -> Broaden: remove filters, try synonyms, alternate casing 5. **Still nothing?** -> Fall through to Grep/Glob (codebase search) ## Rules 1. Start broad, refine with filters — not longer queries 2. Natural language works best — "button with loading state" over "btn loading" 3. Low relevance scores suggest rephrasing needed 4. If server offline, fall back to Grep/Glob 5. `stale_sidecar: true` in results — trust code chunks, ignore metadata fields 6. Do NOT generate synonym variants — server handles expansion automatically ## Integration Priority level 2 in `knowledge-retrieval` chain: 1. `docs/` files -> 2. **RAG** -> 3. Skills/codebase -> 4. Context7 ## Related Skills - `knowledge-retrieval` — Orchestrates source priority - `web-frontend` — Frontend patterns (enhanced by this skill) - `docs-seeker` — External documentation lookup ## References - `references/smart-query.md` — HyDE + multi-query retrieval strategy - `references/sidecar-workflow.md` — AI-generated metadata enrichment - `references/component-mappings.md` — Get canonical names via `expansions` MCP tool - `references/synonym-groups.md` — Get synonym groups via `expansions` MCP tool

Frequently asked questions

Web RAG