change

change is a skill for Claude Code from semantica-agi/semantica. It costs 38 tokens per session (640 once invoked), scanned A, original, MIT.

A tool for inspecting changes in Semantica knowledge graphs over time. A knowledge graph stores entities and the relationships between them.

In plain words
What is it for?
It computes graph differences between snapshots and shows the history of a node or relationship within a selected time range.
Why use it?
It shows what was added, removed, or changed and helps assess how an update affects related graph data.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the semantica plugin — 17 skills, 3 agents, 2 hooks shipped together

Good fit It computes graph differences between snapshots and shows the history of a node or relationship within a selected time range.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/semantica-agi/semantica/change
About the project

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.

semantica-agi/semantica · 14,321 stars · on GitHub · getsemantica.ai

Install

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.

Any agent
npx skills add semantica-agi/semantica --skill change
Clone the repo
git clone --depth 1 https://github.com/semantica-agi/semantica

Made for: Claude Code.

Or install semantica, the plugin that ships this one along with the rest of its 17 skills, 3 agents, 2 hooks.

Wrote 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.

agentmods badge for change

README.md
[![agentmods](https://agentmods.dev/badge/skills/semantica-agi/semantica/change/github.svg)](https://agentmods.dev/skills/semantica-agi/semantica/change)
Your own site
<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.

agentmods 80×15 button for change

Your own site · 80×15
<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>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 640 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash e238d95567f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

plugins/skills/change/SKILL.md · 81 lines

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)

Read the full file on GitHub · 81 lines

Changes

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.

  1. 1mo ago Changed · +42 lines · +11 tokens per session e238d95567f1
  2. 2mo ago First seen · 39 lines · 27 tokens per session scan A 1335f44920df

Subscribe to this mod's changes

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.

Related

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.

bojieli/ai-agent-book · 131 tokens

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.

marimo-team/marimo · 57 tokens

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.

bojieli/ai-agent-book · 91 tokens

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.

bojieli/ai-agent-book · 159 tokens

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.

bojieli/ai-agent-book · 126 tokens

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.

bojieli/ai-agent-book · 168 tokens