Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/fusengine/agents/laravel-vector-searchnpx skills add fusengine/agents --skill laravel-vector-searchgit clone --depth 1 https://github.com/fusengine/agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fusengine/agents/laravel-vector-search)<a href="https://agentmods.dev/skills/fusengine/agents/laravel-vector-search"><img src="https://agentmods.dev/badge/skills/fusengine/agents/laravel-vector-search.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00022 | $0.01243 |
| Opus 5 | $0.00011 | $0.00622 |
| Sonnet 5 | $0.00004 | $0.00249 |
| Haiku 4.5 | $0.00002 | $0.00124 |
Grade A, and why
laravel-vector-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Laravel 13 Vector Search (pgvector)
Agent Workflow (MANDATORY)
Before ANY implementation, use TeamCreate to spawn 3 agents:
- fuse-ai-pilot:explore-codebase - Check current DB driver (must be PostgreSQL) and existing embedding columns
- fuse-ai-pilot:research-expert - Verify pgvector extension version and HNSW vs IVFFlat tradeoffs
- mcp__context7__query-docs - Pull
laravel.com/docs/13.x/search+queriesexamples
After implementation, run fuse-ai-pilot:sniper for validation.
Overview
| Feature | Description |
|---|---|
| PostgreSQL only | Requires pgvector extension; not available on MySQL/SQLite |
| Schema helper | Schema::ensureVectorExtensionExists() enables the extension |
| Query builder | whereVectorSimilarTo(), selectVectorDistance(), whereVectorDistanceLessThan(), orderByVectorDistance() |
| Auto-embedding | Pass a raw string and Laravel generates the embedding via AI SDK |
| Cosine similarity | Default distance; threshold via minSimilarity (0.0 - 1.0) |
Critical Rules
- Use PostgreSQL - Vector clauses ONLY work on
pgsqlconnections - no fallback to MySQL/SQLite - Create an HNSW index - Without an index, queries do full table scans; > 10k rows means seconds-to-minutes latency
- Match dimensions exactly - Insert-time and query-time embedding models MUST share the same dimensions
- Cache embeddings - Regenerating embeddings on every request is the #1 cost driver; persist them
- Lock the embedding model - Changing the model invalidates ALL stored embeddings; treat the model as a schema field
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 137 lines · 22 tokens per session scan A 0b1a6e3a90cc
laravel-vector-search is a skill published in the GitHub repository fusengine/agents (25 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,243 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. Trigger when user asks to: Store or search vector embeddings in PostgreSQL Set up semantic search, similarity search, or nearest neighbor search Create HNSW or IVFFlat indexes for vectors…
postgres-hybrid-text-search
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). Trigger when user asks to: Combine keyword and semantic search Implement hybrid search or multi-modal retrieval Use BM25/pgtextsearch with pgvector together Implement RRF (Reciprocal…
030201-pgvector-embeddings
Vector search with pgvector — embedding generation (OpenAI or hash), HNSW indexing, cosine similarity search, and enriched product JOIN queries.
pinecone
Set up Pinecone as the vector DB in a RAG pipeline — index config, batch upsert, semantic query, metadata filters. Use when the user is adding Pinecone or debugging vector search.
cloudflare-vectorize
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
ai-vector-brain
Builds vector-brain implementations for repos, docs hubs, and compliance corpora. Use when creating pgvector retrieval brains with scripts, SQL, manifests, and evals.