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 DanialDaeHyunNam/omniscitus --skill ontology-initgit clone --depth 1 https://github.com/DanialDaeHyunNam/omniscitusWrote 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/danialdaehyunnam/omniscitus/ontology-init)<a href="https://agentmods.dev/skills/danialdaehyunnam/omniscitus/ontology-init"><img src="https://agentmods.dev/badge/skills/danialdaehyunnam/omniscitus/ontology-init/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/danialdaehyunnam/omniscitus/ontology-init"><img src="https://agentmods.dev/badge/skills/danialdaehyunnam/omniscitus/ontology-init.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 | $0.00060 | $0.00999 |
| Opus 5 | $0.00030 | $0.00500 |
| Sonnet 5 | $0.00012 | $0.00200 |
| Haiku 4.5 | $0.00006 | $0.00100 |
Grade A, and why
ontology-init 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 12d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ontology Init — Define Your Project's Domain Taxonomy
Create or update .omniscitus/ontology.yaml to define the domains, topic
conventions, and classification rules that /wrap-up uses. This ensures
all team members classify work consistently.
When to Use
- User types
/ontology-init - User says "온톨로지", "도메인 설정", "domain setup"
- First-time setup for a team project
- Adding a new domain as the project evolves
Instructions
Step 1: Check Existing Ontology
Read .omniscitus/ontology.yaml if it exists.
If it exists, show the current domains and ask:
📋 Ontology already exists with {N} domains:
{list of domain names + descriptions}
Want to update it, or keep as-is?
Use AskUserQuestion with options: "Update domains" / "Keep as-is". If "Keep as-is", stop here.
If ontology does not exist, proceed to create one.
Step 2: Gather Context
Read the project structure to understand what domains exist:
ls -d */ 2>/dev/null
Also check existing history units for domains already in use:
ls .omniscitus/history/ 2>/dev/null
Use AskUserQuestion:
- "What are the main functional areas of this project? (e.g., backend API, frontend UI, data pipeline, ML models, infrastructure)"
Step 3: Create Ontology File
Write .omniscitus/ontology.yaml with the following structure:
version: 1
# Domains define the top-level classification for work units.
# wrap-up uses these to categorize session work consistently.
domains:
server:
description: "Backend API, database, authentication, business logic"
keywords:
- api
- backend
- database
- auth
- middleware
- migration
directories:
- src/server
- src/api
- src/db
web:
description: "Frontend UI, components, styling, client-side logic"
keywords:
- frontend
- ui
- component
- style
- page
- layout
directories:
- src/web
- src/components
- src/pages
devops:
description: "CI/CD, deployment, infrastructure, monitoring"
keywords:
- ci
- deploy
- docker
- terraform
- monitoring
- pipeline
directories:
- .github
- infra
- deploy
product:
description: "Planning, requirements, design, user research"
keywords:
- prd
- design
- spec
- requirement
- roadmap
# Topic naming conventions for consistency
topic_conventions:
format: "kebab-case, 2-4 words, descriptive"
examples:
- "oauth-token-refresh"
- "dashboard-chart-redesign"
- "ci-pipeline-optimization"
anti_examples:
- "fix-stuff" # too vague
- "session-2026-04-08" # date-based, not topic-based
- "misc-changes" # catch-all
# Classification rules for ambiguous cases
classification_rules:
- "If work spans multiple domains, use the primary domain (where most changes occurred)"
- "Test files follow the domain of the source they test"
- "Documentation follows the domain of the feature it documents"
- "If truly cross-cutting (e.g., major refactor), use 'cross-cutting' as domain"
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.
- 12d ago First seen · 164 lines · 60 tokens per session scan A 691cfd579e18
ontology-init is a skill published in the GitHub repository DanialDaeHyunNam/omniscitus (5 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 999 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…