Semantica is an open-source infrastructure layer that turns enterprise data into structured context and knowledge graphs, where ontologies define meaning and graph reasoning connects facts and decisions. It is intended for AI systems and agents that need traceable, governed, and explainable context in high-stakes domains. The catalogue add-ons provide agent workflows, hooks, and plugins for operating Semantica.
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 skills add semantica-agi/semantica --skill querygit clone --depth 1 https://github.com/semantica-agi/semanticaWrote 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/semantica-agi/semantica/query)<a href="https://agentmods.dev/skills/semantica-agi/semantica/query"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/query/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/semantica-agi/semantica/query"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00031 | $0.00614 |
| Opus 5.5 | $0.00012 | $0.00246 |
| Sonnet 5.5 | $0.00006 | $0.00123 |
| Haiku 4.5 | $0.00003 | $0.00061 |
Grade A, and why
query 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 1mo ago.
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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/semantica:query
Query the graph. Usage: /semantica:query <task> [args]
Which API you want depends on where the graph lives.
search "<keywords>" — the in-memory ContextGraph
This is the one that works with no external server.
import os
from semantica.context import ContextGraph
graph = ContextGraph()
graph.load_from_file(os.path.expanduser("~/.semantica/kg.json")) # load_from_file does not expand ~
results = graph.query("vendor selection", skip=0, limit=20)
Related lookups on the same object:
graph.find_nodes(...) graph.find_node(...)
graph.find_related_nodes(...) graph.get_neighbors(node_id)
graph.find_similar_nodes(...) graph.get_nodes_by_label(label)
Decision-specific queries belong to /semantica:decision.
sparql "<query>" — RDF triple stores
from semantica.triplet_store import TripletStore
store = TripletStore(backend="oxigraph") # embedded; needs semantica[tripletstore-oxigraph]
# or backend="blazegraph" | "jena" | "rdf4j" with endpoint="http://..."
result = store.execute_query(sparql)
For query planning, optimisation, and caching over a backend:
from semantica.triplet_store import QueryEngine, OxigraphStore
# QueryEngine needs an object exposing execute_sparql() — the raw backend,
# not the TripletStore wrapper above (which only exposes execute_query()).
backend = OxigraphStore()
qe = QueryEngine()
plan = qe.plan_query(sparql)
tuned = qe.optimize_query(sparql)
result = qe.execute_query(sparql, store_backend=backend)
stats = qe.get_query_statistics()
Blazegraph / Jena / RDF4J need no extra — semantica.triplet_store speaks
SPARQL over HTTP using the core requests dependency.
cypher "<query>" — labeled property graphs
from semantica.graph_store import Neo4jStore # needs semantica[graph-neo4j]
store = Neo4jStore(uri=..., user=..., password=...)
result = store.execute_query(query, parameters={...})
Also available: FalkorDBStore, ApacheAgeStore, AmazonNeptuneStore,
and GraphManager / GraphStore for backend-agnostic access.
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.
- 1mo ago Changed · +35 lines · +5 tokens per session 52381bbffa07
- 1mo ago First seen · 50 lines · 26 tokens per session scan A ff906b009af0
query is a skill published in the GitHub repository semantica-agi/semantica (14,272 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 614 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
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build-paths
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knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.