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 dcc-mcp/dcc-mcp-core --skill example-layered-skillgit clone --depth 1 https://github.com/dcc-mcp/dcc-mcp-coreWrote 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/dcc-mcp/dcc-mcp-core/example-layered-skill)<a href="https://agentmods.dev/skills/dcc-mcp/dcc-mcp-core/example-layered-skill"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-core/example-layered-skill/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/dcc-mcp/dcc-mcp-core/example-layered-skill"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-core/example-layered-skill.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 | $0.00070 | $0.00732 |
| Opus 5.5 | $0.00028 | $0.00293 |
| Sonnet 5.5 | $0.00014 | $0.00146 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
example-layered-skill 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 27d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Layered Skill Architecture — Reference
This skill demonstrates the internal layered organisation recommended for
complex skills (see docs/guide/skills.md
section "Complex Skill Architecture").
It is intentionally simple — three asset-management tools that share a small service object — so the structure, not the business logic, is the focus.
Layout
example-layered-skill/
├── SKILL.md ← this file (frontmatter + prose)
├── tools.yaml ← MCP tool declarations (sibling, per #356)
├── scripts/
│ ├── __init__.py
│ ├── create_asset.py ← thin tool adapters (parse params, return envelope)
│ ├── publish_asset.py
│ ├── validate_asset.py
│ ├── services/ ← business logic (orchestration, error handling)
│ │ ├── __init__.py
│ │ └── asset_service.py
│ └── utils/ ← pure helpers (no I/O, no DCC calls, fully unit-testable)
│ ├── __init__.py
│ └── path_utils.py
└── prompts/
└── system.md ← optional system prompt sidecar
Layer responsibilities
| Layer | Responsibility | Size guidance |
|---|---|---|
| tool scripts | Parse JSON params from stdin, validate, delegate, return envelope. | < 30 lines |
| services/ | Orchestrate DCC commands. Easily unit-testable in isolation. | Grows with feature |
| utils/ | Pure functions — path normalisation, primitive helpers. No side effects. | Grows with feature |
Tools exposed
| Tool | Description |
|---|---|
example_layered_skill__create_asset |
Create a new asset record |
example_layered_skill__publish_asset |
Publish an existing asset |
example_layered_skill__validate_asset |
Validate an asset against project rules (read-only) |
Why a sibling tools.yaml
Per #356, tool
declarations live in a sibling YAML referenced from
metadata.dcc-mcp.tools so that SKILL.md frontmatter stays
agentskills.io 1.0 compliant.
What ships with it
10 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.
- prompts/system.md 619 B
- scripts/__init__.py 78 B runs code
- scripts/create_asset.py 1.4 KB runs code
- scripts/publish_asset.py 1.1 KB runs code
- scripts/services/__init__.py 54 B runs code
- scripts/services/asset_service.py 2.1 KB runs code
- scripts/utils/__init__.py 76 B runs code
- scripts/utils/path_utils.py 700 B runs code
- scripts/validate_asset.py 1.1 KB runs code
- tools.yaml 1.7 KB
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.
- 27d ago Changed · +5 lines a62c73699a4a
- 1mo ago First seen · 75 lines · 70 tokens per session scan A 600adfeac88e
example-layered-skill is a skill published in the GitHub repository dcc-mcp/dcc-mcp-core (70 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 732 once invoked, about $0.0003 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.
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