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 changegit 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/change)<a href="https://agentmods.dev/skills/semantica-agi/semantica/change"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/change/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/change"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/change.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 | $0.00038 | $0.00640 |
| Opus 5.5 | $0.00015 | $0.00256 |
| Sonnet 5.5 | $0.00008 | $0.00128 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
change 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/semantica:change
Track what changed. Usage: /semantica:change <task> [args]
Two distinct mechanisms cover this, and they are not interchangeable:
Question Tool "What did the graph look like on date X?" ContextGraph.state_at()"What changed between ontology versions?" change_management.VersionManager
graph-at <timestamp> — point-in-time graph state
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 ~
snapshot = graph.state_at("2026-06-01") # str | int | float | datetime
Diff two moments by comparing node IDs — state_at()["nodes"] is a list of
dicts (unhashable), so compare the id fields, not the dicts themselves:
before = graph.state_at("2026-06-01")
after = graph.state_at("2026-09-01")
before_ids = {n["id"] for n in before["nodes"]}
after_ids = {n["id"] for n in after["nodes"]}
added = after_ids - before_ids
For richer temporal work (scrubbing, evolution, temporal patterns) use
/semantica:temporal, which wraps the same layer.
node-history <node_id> — who touched this node
Node-level history is provenance, not change management:
import os
from semantica.provenance import ProvenanceManager
db_path = os.path.expanduser("~/.semantica/prov.db") # storage_path is passed to
os.makedirs(os.path.dirname(db_path), exist_ok=True) # sqlite3.connect() unexpanded
pm = ProvenanceManager(storage_path=db_path)
history = pm.revision_history(node_id)
log = pm.audit_log(since="2026-01-01")
versions / diff <v1> <v2> — ontology versioning
from semantica.change_management import VersionManager
vm = VersionManager()
vm.create_version("1.1.0", ontology)
vm.list_versions()
vm.get_latest_version()
delta = vm.compare_versions("1.0.0", "1.1.0")
delta = vm.diff_ontologies(base_ontology, target_ontology)
migrated = vm.migrate_ontology("1.0.0", "1.1.0", ontology)
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 · +42 lines · +11 tokens per session e238d95567f1
- 2mo ago First seen · 39 lines · 27 tokens per session scan A 1335f44920df
change is a skill published in the GitHub repository semantica-agi/semantica (14,321 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 640 once invoked, about $0.0002 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
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
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.
kv-cache-design
A guide to arranging an AI agent's conversation so previously processed text can be reused. A KV cache stores intermediate results for earlier tokens, while a prompt cache reuses a stable beginning across requests.
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.