/install dcc-mcp-skills-creator
DCC-MCP Skills Creator
A first-class meta-skill for creating, validating, and reviewing DCC-MCP skill
packages. It combines the scaffold/validation tools from dcc-skills-creator
with agent-facing authoring guidance for SKILL.md, tools.yaml, scripts,
groups, prompts, and progressive-loading taxonomy.
Use dcc-mcp-creator when the task is to create a full adapter repository for
a host such as Nuke, Blender, 3ds Max, Unreal, ZBrush, Houdini, or Maya. Use
this skill when the task is to create or improve the skill packages loaded by
those adapters.
Installation
This skill ships with dcc-mcp-core. Add it to your skill path:
# Linux/macOS
export DCC_MCP_SKILL_PATHS="${DCC_MCP_SKILL_PATHS}:$(python -c 'import dcc_mcp_core; print(dcc_mcp_core.__file__)')/../skills"
# Windows
set DCC_MCP_SKILL_PATHS=%DCC_MCP_SKILL_PATHS%;C:\path o\dcc-mcp-core\skills
Or reference it directly when starting your MCP server:
from dcc_mcp_core import create_skill_server, McpHttpConfig
server = create_skill_server(
"maya",
McpHttpConfig(port=8765),
extra_paths=["/path/to/dcc-mcp-core/skills"],
)
Quick Start
Create a new skill
# Call the loaded MCP tool:
# dcc_mcp_skills_creator__create_skill(
# name="maya-rigging",
# parent_dir="/path/to/skills/dir",
# dcc="maya",
# tool_name="create_locator",
# affinity="main",
# )
Validate an existing skill
from dcc_mcp_core import validate_skill
report = validate_skill("/path/to/my-skill")
if report.has_errors:
for issue in report.issues:
print(f"[{issue.severity}] {issue.category}: {issue.message}")
else:
print("Skill is valid!")
Get a SKILL.md template
# Call the loaded MCP tool:
# dcc_mcp_skills_creator__skill_template()
Skill Directory Structure
my-skill/
|-- SKILL.md # Required: metadata frontmatter + instructions
|-- tools.yaml # Required when metadata.dcc-mcp.tools points here
|-- scripts/ # Optional: tool implementation scripts
| `-- create_locator.py
`-- references/ # Optional: recipes, examples, and long-form docs
|-- RECIPES.md
`-- NOTES.md
Current Tool Contract
Generated tools.yaml entries follow the modern contract:
- Local tool names are snake_case and client-safe. Do not use dotted names.
- Loaded tools are published as
\x3Cskill-name>__\x3Ctool_name>when namespacing is needed. input_schemaandoutput_schemaare declared explicitly.executionissyncorasync; useasyncfor deferred/long-running work.affinityis explicit. Usemainfor host API or scene mutation work andanyfor pure work.enforce_thread_affinity: trueis emitted so adapter dispatch stays honest.annotationsuse MCP hints: read-only, destructive, idempotent, open-world, and deferred.
Authoring Workflow
- Decide whether the skill is infrastructure, domain, thin-harness, or example.
- Give the skill a kebab-case name and each local tool a snake_case name.
- Keep host API calls inside scripts, with lazy imports so discovery works without the host running.
- Declare
execution,affinity,timeout_hint_secs, schemas, annotations, and failure recovery chains intools.yaml. - Put long examples, recipes, and host-specific notes under
references/. - Validate with
validate_skill_dirordcc_mcp_core.validate_skill()before loading it in an adapter. - If the desired behavior requires parsing core internals or adapter-private YAML at runtime, stop and request a core API instead.
Read AUTHORING_WORKFLOW.md and DCC_TOOL_CONTRACTS.md before changing a production skill package.
Validation Rules
The validator checks:
- SKILL.md exists and is readable
- YAML frontmatter is well-formed
- Required fields:
name,description - Name format: kebab-case, \x3C=64 chars, matches directory name
- Field lengths: description \x3C=1024, compatibility \x3C=500
- Tool declarations: non-empty names, no duplicates, snake_case client-safe format
- Script files:
source_filereferences exist inscripts/ - Sidecar files:
metadata.dcc-mcp.tools/groups/promptsreferences exist - Dependencies:
metadata.dcc-mcp.dependsconsistency - Spec compliance: non-standard top-level keys are frontmatter errors; dcc-mcp-core extensions must live under
metadata.dcc-mcp.*and point to sibling files
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install dcc-mcp-skills-creator - 安装完成后,直接呼叫该 Skill 的名称或使用
/dcc-mcp-skills-creator触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Dcc Mcp Skills Creator 是什么?
Infrastructure skill - create, validate, scaffold, and review DCC-MCP skills for the dcc-mcp-core ecosystem. Use when authoring SKILL.md, tools.yaml, scripts... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 36 次。
如何安装 Dcc Mcp Skills Creator?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install dcc-mcp-skills-creator」即可一键安装,无需额外配置。
Dcc Mcp Skills Creator 是免费的吗?
是的,Dcc Mcp Skills Creator 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Dcc Mcp Skills Creator 支持哪些平台?
Dcc Mcp Skills Creator 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Dcc Mcp Skills Creator?
由 Hal(@loonghao)开发并维护,当前版本 v0.17.30。