aria_skills/unified_search/SKILL.md
```skill --- name: aria-unified-search description: "🔎 RRF-merged search across semantic + graph + memory backends" metadata: {"aria": {"emoji": "🔎"}} --- # aria-unified-search Unified search engine that queries semantic memories (pgvector cosine), knowledge graph (ILIKE), and traditional memories (text match), then merges results via Reciprocal Rank Fusion (RRF) with content-hash deduplication. ## Architecture ``` Query ↓ (parallel to 3 backends) ├── SemanticBackend (pgvector cosi
npx skillsauth add najia-afk/aria_moltbot aria_skills/unified_searchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
---
name: aria-unified-search
description: "🔎 RRF-merged search across semantic + graph + memory backends"
metadata: {"aria": {"emoji": "🔎"}}
---
# aria-unified-search
Unified search engine that queries semantic memories (pgvector cosine),
knowledge graph (ILIKE), and traditional memories (text match), then
merges results via Reciprocal Rank Fusion (RRF) with content-hash
deduplication.
## Architecture
Query ↓ (parallel to 3 backends) ├── SemanticBackend (pgvector cosine similarity via api_client) ├── GraphBackend (ILIKE text match via api_client.graph_search) └── MemoryBackend (text match via api_client.get_memories) ↓ RRFMerger (k=60) ├── semantic weight: 1.0 ├── graph weight: 0.8 └── memory weight: 0.6 ↓ Content-hash deduplication ↓ Ranked results (SearchResult objects)
## RRF Formula
$$\text{score}(d) = \sum_{b \in \text{backends}} \frac{w_b}{k + \text{rank}_b(d)}$$
Where $k = 60$ (damping constant) and $w_b$ is the backend weight.
## Usage
```bash
# Unified search across all backends
exec python3 /app/skills/run_skill.py unified_search search '{"query": "security best practices"}'
# With filters
exec python3 /app/skills/run_skill.py unified_search search '{"query": "AI safety", "limit": 10, "backends": ["semantic", "graph"], "min_importance": 0.5}'
# Semantic-only search
exec python3 /app/skills/run_skill.py unified_search semantic_search '{"query": "deployment pipeline"}'
# Graph-only search
exec python3 /app/skills/run_skill.py unified_search graph_search '{"query": "moltbook"}'
# Memory-only search
exec python3 /app/skills/run_skill.py unified_search memory_search '{"query": "user preferences"}'
Unified search across all backends with RRF merge. Returns deduplicated, ranked results with backend attribution and timing info.
Search semantic memories only via pgvector cosine similarity. Supports category and importance filters.
Search knowledge graph only via ILIKE text matching on entity names and relation labels.
Search traditional key-value memories only via text matching. Supports category filter.
api_client (all 3 backend searches)tools
# aria-working-memory Persistent short-term memory operations for Aria runtime context. ## Purpose - Store, retrieve, rank, checkpoint, and prune working memory. - Provide high-relevance context for decision and response flows. ## Main Tools - `remember` - `recall` - `get_context` - `checkpoint` - `restore_checkpoint` - `forget` - `update` - `reflect` - `sync_to_files`
tools
# aria-telegram Telegram Bot API integration skill. ## Purpose - Send and reply to messages through bot credentials. - Fetch updates and basic bot metadata. ## Main Tools - `send_message` - `reply_to_message` - `get_updates` - `get_me`
tools
# aria-sprint-manager Sprint planning and board flow management skill. ## Purpose - Operate goal board as Product Owner. - Move, prioritize, and report sprint progress. ## Main Tools - `sprint_status` - `sprint_plan` - `sprint_move_goal` - `sprint_report` - `sprint_prioritize`
testing
Manage Aria's social presence and posts on Moltbook and other platforms.