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 decisiongit 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/decision)<a href="https://agentmods.dev/skills/semantica-agi/semantica/decision"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/decision/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/decision"><img src="https://agentmods.dev/badge/skills/semantica-agi/semantica/decision.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 | $0.00055 | $0.01471 |
| Opus 5 | $0.00028 | $0.00736 |
| Sonnet 5 | $0.00011 | $0.00294 |
| Haiku 4.5 | $0.00006 | $0.00147 |
Grade A, and why
decision 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 10d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/semantica:decision
Full decision lifecycle management. Usage: /semantica:decision <sub-command> [args]
record <category> "<scenario>" "<reasoning>" <outcome> <confidence>
Record a decision with full context.
from semantica.context import AgentContext
ctx = AgentContext(decision_tracking=True)
decision_id = ctx.record_decision(
category=category, # "loan_approval", "deployment", "hiring"
scenario=scenario, # natural-language situation description
reasoning=reasoning, # why this decision was made
outcome=outcome, # "approved", "rejected", "deferred"
confidence=float(confidence),
entities=entities or [],
decision_maker="ai_agent",
valid_from=valid_from, # optional ISO date string
valid_until=valid_until,
)
Output: Decision <decision_id> recorded | <category> | <outcome> (conf: 0.95)
query "<question>" [--hops N] [--hybrid]
Query decisions using natural language with multi-hop graph traversal.
from semantica.context import AgentContext
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
results = ctx.query_decisions(
query=question,
max_hops=int(hops) if hops else 3,
include_context=True,
use_hybrid_search="--hybrid" in args,
)
For structured lookups use DecisionQuery:
from semantica.context.decision_query import DecisionQuery
dq = DecisionQuery(graph_store=ctx.graph_store)
# dq.find_by_category(category, limit=100)
# dq.find_by_entity(entity_id, limit=100)
# dq.find_by_time_range(start, end, limit=100)
# dq.multi_hop_reasoning(start_entity, query_context, max_hops=3)
# dq.trace_decision_path(decision_id, relationship_types)
# dq.analyze_decision_influence(decision_id, max_depth=3)
Return: | ID | Category | Scenario | Outcome | Confidence | Timestamp |
precedents "<scenario>" [--category <cat>] [--advanced] [--hops N] [--as-of <date>]
Find similar past decisions using hybrid semantic + structural + vector search.
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.
- 10d ago First seen · 198 lines · 55 tokens per session scan A f2e5f2fcc485
decision is a skill published in the GitHub repository semantica-agi/semantica (12,474 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 1,471 once invoked, about $0.0003 per session on Opus 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
ai-instruction-detox
A review and cleanup method for instructions stored in files such as CLAUDE.md, AGENTS.md, skills, context, and memory.
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.
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
gsd-graphify
Build, query, and inspect the project knowledge graph in .planning/graphs/.
openmemory
Manage persistent memory via OpenMemory MCP. TRIGGER when: user says "remember this", "save to memory", "store this", "recall", "what do you remember about", "check memory", "forget this", "delete memory", "clean up memory", or when agent forms a stable conclusion worth persisting. DO NOT TRIGGER when: user refers to…