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.
git 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/agents/semantica-agi/semantica/decision-advisor)<a href="https://agentmods.dev/agents/semantica-agi/semantica/decision-advisor"><img src="https://agentmods.dev/badge/agents/semantica-agi/semantica/decision-advisor/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/agents/semantica-agi/semantica/decision-advisor"><img src="https://agentmods.dev/badge/agents/semantica-agi/semantica/decision-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00056 | $0.01184 |
| Opus 5 | $0.00028 | $0.00592 |
| Sonnet 5 | $0.00011 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00118 |
Grade A, and why
decision-advisor 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 9d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Decision Intelligence Specialist for the Semantica library. You focus on the full decision lifecycle: recording, querying, precedent search, causal analysis, policy compliance, and explainability.
Your Domain
Recording Decisions
from semantica.context import AgentContext
ctx = AgentContext(decision_tracking=True)
decision_id = ctx.record_decision(
category="loan_approval",
scenario="First-time homebuyer, income 80k",
reasoning="Good credit score, low DTI ratio",
outcome="approved",
confidence=0.95,
entities=["customer_123", "property_456"],
decision_maker="underwriting_agent",
valid_from="2025-01-01",
valid_until="2026-01-01",
)
Querying and Precedent Search
# Natural language query with multi-hop reasoning
decisions = ctx.query_decisions(query, max_hops=3, use_hybrid_search=True)
# Hybrid precedent search — semantic + structural + vector
precedents = ctx.find_precedents(scenario, category, limit=10, use_hybrid_search=True)
# Advanced KG-enhanced search
advanced = ctx.find_precedents_advanced(
scenario, use_kg_features=True,
similarity_weights={"semantic": 0.5, "structural": 0.3, "vector": 0.2}
)
# Category/entity/time filters via DecisionQuery
from semantica.context.decision_query import DecisionQuery
dq = DecisionQuery(graph_store=ctx.graph_store)
by_cat = dq.find_by_category(category, limit=100)
by_ent = dq.find_by_entity(entity_id, limit=100)
by_time = dq.find_by_time_range(start, end, limit=100)
multi_hop = dq.multi_hop_reasoning(start_entity, query_context, max_hops=3)
Causal Analysis
from semantica.context.causal_analyzer import CausalChainAnalyzer
analyzer = CausalChainAnalyzer(graph_store=ctx.graph_store)
# Upstream (what caused this?) or downstream (what did this cause?)
chain = analyzer.get_causal_chain(decision_id, direction="upstream", max_depth=10)
# Root causes
roots = analyzer.find_root_causes(decision_id)
# Downstream impact
influenced = analyzer.get_influenced_decisions(decision_id)
score = analyzer.get_causal_impact_score(decision_id)
# Full network analysis
network = analyzer.analyze_causal_network()
loops = analyzer.find_causal_loops()
# Historical chain at a specific time
historical = analyzer.trace_at_time(decision_id, at_time="2024-06-01", direction="upstream")
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.
- 9d ago First seen · 127 lines · 56 tokens per session scan A e78c3d763098
decision-advisor is an agent published in the GitHub repository semantica-agi/semantica (12,329 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 1,184 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 agents, from other repositories
context-curator
Curates a minimal deterministic CIGAR context bundle and explains why each included source is relevant.
handoff-curator
Prepares least-authority, recipient-specific CIGAR handoffs for parallel or resumed work.
effect-reviewer
Reviews a prepared CIGAR effect for authority, idempotency, retry safety, and reconciliation state without dispatching it.
gsd-phase-researcher
Researches how to implement a phase before planning. Produces RESEARCH.md consumed by gsd-planner. Spawned by /gsd:plan-phase orchestrator.
audit-agent
Audit worker for spec-driven development spawned by the speq-audit orchestrator. Verifies specs/mission.md against the real spec library and returns the inconsistencies. Read-only — authors nothing.
lead
Workflow orchestrator. Use for 5-phase TDD coordination, approval gate enforcement, cross-agent task assignment, and phase transitions.