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/semantica-agi/semantica/visualizenpx skills add semantica-agi/semantica --skill visualizegit clone --depth 1 https://github.com/semantica-agi/semanticaWhat 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 | $0.00000 | $0.01741 |
| Opus 5 | $0.00000 | $0.00870 |
| Sonnet 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 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 — 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=20,
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.
- yesterday First seen · 250 lines · 0 tokens per session scan A 355d072818b5
visualize is a skill published in the GitHub repository semantica-agi/semantica (11,359 stars, last pushed yesterday), 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.
Other skills, from other repositories
ai-instruction-detox
AI 指令排毒與規則治理：把散落在 CLAUDE.md、AGENTS.md、skills、context、memory 的規則 原子化、查衝突、去重複、找過時、揪 Prompt Injection，產出可套用的精簡架構與回復方案。 觸發時機：用戶說「指令排毒」「規則太亂」「CLAUDE.md 太長」「規則互相矛盾」「上下文減肥」 「AI 設定治理」「多個 Agent 規則分裂」「context 膨脹」，或要求審查／清理 AI 指令檔。 不要觸發：一般程式重構、產品程式碼審查、單純想縮短一份文件（那是編輯不是治理）。.
compile
Compile a deterministic CIGAR context bundle for the current task with an explicit token budget and inspectable manifest.
effect
Prepare, inspect, commit, and reconcile governed CIGAR effects while preserving explicit authorization and idempotency.
why
Explain the provenance, authority, expiry, degradation state, and token accounting of CIGAR context already presented in this session.
checkpoint
Create an inspectable CIGAR checkpoint before compaction, interruption, or a meaningful task boundary.
handoff
Create or accept a recipient-specific CIGAR handoff without forwarding the parent conversation transcript.