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 viktorbezdek/skillstack --skill content-modellinggit clone --depth 1 https://github.com/viktorbezdek/skillstackWrote 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/viktorbezdek/skillstack/content-modelling)<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/content-modelling"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/content-modelling/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/viktorbezdek/skillstack/content-modelling"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/content-modelling.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 | $0.00114 | $0.01400 |
| Opus 5.5 | $0.00046 | $0.00560 |
| Sonnet 5.5 | $0.00023 | $0.00280 |
| Haiku 4.5 | $0.00011 | $0.00140 |
Grade A, and why
content-modelling 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Modelling
Design structured content models for reusable, multi-channel content.
When to Use / Not Use
Use when:
- Designing a CMS schema for a new project
- Defining content types with fields, constraints, and relationships
- Planning editorial workflows (draft, review, publish, archive)
- Building multi-channel content systems (web, mobile, email, API)
- Migrating from page-based to structured content models
- Establishing naming conventions for content types and fields
Do NOT use when:
- Building formal ontologies with classes, properties, and inference rules -> use
ontology-design - Standardizing naming conventions across code and documentation -> use
consistency-standards - Designing API schemas and endpoints -> use
api-design
Decision Tree
What are you modeling?
├── CMS content types (what fields, what relationships)
│ ├── New system? -> Start with content inventory, then type design (§Field Types, §Relationship Types)
│ └── Existing system with problems? -> Audit for anti-patterns first (§Anti-Patterns)
├── Multi-channel publishing (same content, different outputs)
│ └── Need COPE? -> Use semantic fields, not layout fields (§Design Principles)
├── Editorial workflow (who reviews, when, how)
│ └── Need lifecycle model? -> Define status enum + transition rules (§Content Model Template)
├── Terminology standardization (what to call things)
│ └── Naming conventions only? -> Use `consistency-standards` instead
└── Formal knowledge model with reasoning?
└── Use `ontology-design` instead
Core Concepts
| Concept | Definition |
|---|---|
| Content Type | Template defining structure for similar content |
| Field | Single data element within a content type |
| Relationship | Connection between content types |
| Instance | Specific piece of content based on a type |
Field Types
| Type | Use Case | Example |
|---|---|---|
| Short text | Titles, labels | "Getting Started" |
| Long text | Descriptions | Paragraph content |
| Rich text | Formatted content | Bold, links, lists |
| Number | Quantities | 42 |
| Boolean | Toggles | true |
| Date | Timestamps | 2024-01-15 |
| Media | Images, files | hero.png |
| Reference | Links to other content | → Author |
| Enum | Fixed choices | `draft |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 161 lines · 114 tokens per session scan A 534e643d0b8c
content-modelling is a skill published in the GitHub repository viktorbezdek/skillstack (14 stars, last pushed 4mo ago), licensed MIT. It adds 114 tokens to every session and 1,400 once invoked, about $0.0005 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
api-security-best-practices
Implement secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities.
api-patterns
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
amazon-alexa
Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).
api-and-interface-design
Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or establishing boundaries between frontend and backend.
api-designer
Generates complete, production-ready REST API endpoint specifications for any system or domain the user describes.
api-sdk-generator
Generates client SDK code, API wrapper libraries, request/response models, and language-specific usage patterns for any REST API.