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 visualizegit 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/visualize)<a href="https://agentmods.dev/skills/semantica-agi/semantica/visualize"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/visualize/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/visualize"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/visualize.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 | $0.00000 | $0.01741 |
| Opus 5.5 | $0.00000 | $0.00696 |
| Sonnet 5.5 | $0.00000 | $0.00348 |
| Haiku 4.5 | $0.00000 | $0.00174 |
Grade A, and why
visualize 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 18d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- visualize — 98% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/semantica:visualize
Render graph visualizations as Mermaid, ASCII, or structured Markdown. Usage: /semantica:visualize <sub-command> [args]
$ARGUMENTS = sub-command + optional node label or filter.
topology [--filter <node_type>]
Full graph structure analysis — node types, edge distribution, connectivity metrics.
from semantica.kg.graph_analyzer import GraphAnalyzer
from semantica.context import ContextGraph
graph = ContextGraph(advanced_analytics=True)
analyzer = GraphAnalyzer()
# Comprehensive analysis
analysis = analyzer.analyze_graph(graph=graph.to_dict())
metrics = analyzer.compute_metrics(graph=graph)
connectivity = analyzer.analyze_connectivity(graph=graph)
Output:
Graph Topology:
Nodes: N (M types)
Edges: P
Density: 0.23
Avg degree: 4.7
Connected: YES / NO (K components)
Node type distribution:
[Mermaid pie chart]
| Type | Count | % | Avg Degree |
Top-10 connected nodes:
| Node | Type | Degree | Betweenness |
centrality [--type degree|betweenness|closeness|eigenvector|pagerank|all] [--top N]
Calculate and rank nodes by centrality.
from semantica.kg.centrality_calculator import CentralityCalculator
from semantica.context import ContextGraph
graph = ContextGraph()
calc = CentralityCalculator()
if centrality_type == "all" or not centrality_type:
scores = calc.calculate_all_centrality(graph=graph)
elif centrality_type == "degree":
scores = calc.calculate_degree_centrality(graph=graph)
elif centrality_type == "betweenness":
scores = calc.calculate_betweenness_centrality(graph=graph)
elif centrality_type == "closeness":
scores = calc.calculate_closeness_centrality(graph=graph)
elif centrality_type == "eigenvector":
scores = calc.calculate_eigenvector_centrality(graph=graph)
elif centrality_type == "pagerank":
scores = calc.calculate_pagerank(
graph=graph,
max_iterations=100,
damping_factor=0.85,
)
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.
- 18d ago Changed 5e68906f75d4
- 1mo ago First seen · 250 lines · 0 tokens per session scan A 355d072818b5
visualize is a skill published in the GitHub repository semantica-agi/semantica (14,139 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,741 tokens. 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.
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kv-cache-design
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marimo-pair
Work inside the user's live marimo notebook from the code editor: run Python in the same kernel the user does, inspect live notebook state, and commit durable notebook changes through code mode. Use whenever you create, analyze, or improve the user's marimo notebook.
post-training-strategy
A guide to choosing and planning ways to improve an AI model after its initial training. It explains when to add knowledge, teach a response format, use trial-and-error training, or use a smaller model to learn from a larger one.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.
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.