dcc-mcp-skills-creator

name	dcc-mcp-skills-creator
description	Infrastructure skill - create, validate, scaffold, and review DCC-MCP skills for the dcc-mcp-core ecosystem. Use when authoring SKILL.md, tools.yaml, scripts, groups, prompts, or skill taxonomy. Not for creating a full DCC-MCP adapter repository - use dcc-mcp-creator.
license	MIT-0
compatibility	Python 3.7+, dcc-mcp-core 0.17+
allowed-tools	Bash Read Write Edit
metadata	{"dcc-mcp":{"dcc":"python","version":"0.17.47","layer":"infrastructure","search-hint":"create dcc mcp skill, validate skill, scaffold skill, SKILL.md, tools.yaml, scripts, groups, prompts, skill taxonomy","tools":"tools.yaml","skill-reference-docs":["references/*.md"]},"openclaw":{"homepage":"https://github.com/loonghao/dcc-mcp-core/blob/main/skills/dcc-mcp-skills-creator/SKILL.md"}}

DCC-MCP Skills Creator

A first-class meta-skill for creating, validating, and reviewing DCC-MCP skill packages. It bundles scaffold/validation tools together 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\to\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 <skill-name>__<tool_name> when namespacing is needed.
• input_schema and output_schema are declared explicitly.
• execution is sync or async; use async for deferred/long-running work.
• affinity is explicit. Use main for host API or scene mutation work and any for pure work.
• enforce_thread_affinity: true is emitted so adapter dispatch stays honest.
• annotations use MCP hints: read-only, destructive, idempotent, open-world, and deferred.

Authoring Workflow

1. Decide whether the skill is infrastructure, domain, thin-harness, or example.
2. Give the skill a kebab-case name and each local tool a snake_case name.
3. Keep host API calls inside scripts, with lazy imports so discovery works without the host running.
4. Import dependency-light runtime helpers from dcc_mcp_core.skills_helper first: JSON/YAML codecs, bounded HTTP helpers, safe file/path helpers, validation, cancellation checks, and result helpers.
5. Declare execution, affinity, timeout_hint_secs, schemas, annotations, and failure recovery chains in tools.yaml.
6. Put long examples, recipes, and host-specific notes under references/.
7. Validate with validate_skill_dir or dcc_mcp_core.validate_skill() before loading it in an adapter.
8. 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, <=64 chars, matches directory name
• Field lengths: description <=1024, compatibility <=500
• Tool declarations: non-empty names, no duplicates, snake_case client-safe format
• Script files: source_file references exist in scripts/
• Sidecar files: metadata.dcc-mcp.tools/groups/prompts references exist
• Dependencies: metadata.dcc-mcp.depends consistency
• 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
• Skill helper adoption: validate_skill_dir emits skill-helper-adoption warnings when scripts import avoidable dependencies covered by dcc_mcp_core.skills_helper, such as requests, httpx, PyYAML, or local JSON/HTTP/file/path helper modules