Agent skill

ios-rag

(ePost) Use when searching iOS codebase for Swift views, UIKit/SwiftUI patterns, or design system tokens via vector search

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/ios-rag

Metadata

Additional technical details for this skill

keywords
rag vector-search ios swift swiftui uikit design-system
triggers 
[
    "search swift",
    "find view",
    "ios pattern",
    "theme token"
]
platforms 
[
    "ios"
]
connections 
{
    "enhances": [
        "ios-development"
    ]
}
agent affinity 
[
    "epost-a11y-specialist",
    "epost-muji",
    "epost-fullstack-developer"
]

SKILL.md

iOS RAG Skill

Purpose

Vector search across iOS repositories. Semantic search of Swift code, SwiftUI views, UIKit patterns, and design tokens via MCP tools. Call status to discover currently indexed projects.

MCP Tools

Tool Purpose
query Semantic search — returns code chunks with relevance scores
status Health check, indexed projects, document counts
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. iOS also exposes filter metadata via GET /api/rag/filters (REST) — use query inputSchema for MCP-accessible filter options.

Discovery Protocol

Before querying, discover what's indexed:

  1. Call status — returns available projects, document counts, health
  2. Call expansions (once per session) — get component aliases and synonym groups

Use discovered project names for filters.project param. Use enforce_scope: false to search across all indexed projects.

When to Use

Scenario RAG Grep/Glob Context7
Find Swift view by concept yes if know filename no
Search theme tokens yes yes no
Apple framework API no no yes
Cross-project pattern yes one repo at a time no
Existing view for task yes no no

Query Strategy

  1. Known view 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 sparse? -> Try enforce_scope: false to search all indexed repos
  6. 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
  • ios-development — iOS development patterns (enhanced by this skill)
  • docs-seeker — External Apple 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

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