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 temporalgit 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/temporal)<a href="https://agentmods.dev/skills/semantica-agi/semantica/temporal"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/temporal/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/temporal"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/temporal.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 | $0.01134 |
| Opus 5.5 | $0.00000 | $0.00454 |
| Sonnet 5.5 | $0.00000 | $0.00227 |
| Haiku 4.5 | $0.00000 | $0.00113 |
Grade A, and why
temporal 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- temporal — 88% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/semantica:temporal
Temporal graph operations. Usage: /semantica:temporal <sub-command> [args]
$ARGUMENTS = sub-command + query/node + date expression.
query "<question>" [at|before|after <date>]
Temporally-scoped natural-language graph query.
from semantica.kg.temporal_query_rewriter import TemporalQueryRewriter
from semantica.kg.temporal_normalizer import TemporalNormalizer
normalizer = TemporalNormalizer()
# Normalize natural date expressions: "last month", "Q3 2024", "2025-01-15"
date = normalizer.normalize(date_expr)
rewriter = TemporalQueryRewriter()
# Rewrite query with temporal constraint
rewritten = rewriter.rewrite(
query=question,
temporal_constraint={"op": direction, "value": date}, # op: "at"|"before"|"after"
)
Then run the rewritten query through AgentContext.retrieve() or ContextGraph.query().
Return ranked results with Valid From, Valid Until, Active At <date> columns. Mark nodes that were not yet created at the target time as [not yet created].
snapshot <date>
Reconstruct the full graph state as it existed at a specific point in time.
from semantica.context import ContextGraph
graph = ContextGraph(advanced_analytics=True)
# state_at returns a dict snapshot of the graph at that timestamp
snapshot = graph.state_at(timestamp=date) # ISO string or datetime
Output:
Graph snapshot at <date>:
Nodes: N (M added since prev snapshot, K removed)
Edges: P
Density: 0.21
Communities: Q
Active decision categories at <date>:
| Category | Count | Avg Confidence |
Top 10 nodes (by degree at <date>):
| Node | Type | Degree |
[Compact Mermaid graph TD — top-10 most connected nodes at that time]
timeline <node_id>
Show attribute and relationship changes for a node across its full history.
from semantica.context import ContextGraph
graph = ContextGraph()
# Use state_at() at multiple time points to reconstruct history
# Check add_node timestamps and edge addition times from graph data
node_data = graph.find_node(node_id)
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 First seen · 165 lines · 0 tokens per session scan A b5f65e9a0de8
temporal 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 1,134 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
reward-design
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kv-cache-design
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marimo-pair
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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
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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.