models
- Repo stars 330
- License MIT
- Author updated Live
- Author repo claude-skill-registry
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @majiayu000 · MIT
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: models
description: Imported skill models from agentskills """Data models for Agent Skills.""" from dataclasses impo…
category: other
runtime: no special runtime
---
# models output preview
## PART A: Task fit
- Use case: Imported skill models from agentskills """Data models for Agent Skills.""" from dataclasses import dataclass, field from typing import Optional class SkillProperties: """Properties parsed from a skill's SKILL.md frontmatter. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Decide Fit First / Design Intent / How To Use It” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Imported skill models from agentskills """Data models for Agent Skills.""" from dataclasses import dataclass, field from typing import Optional class SkillProperties: """Properties parsed from a skill's SKILL.md frontmatter. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Decide Fit First / Design Intent / How To Use It” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “Decide Fit First / Design Intent / How To Use It”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: models
description: Imported skill models from agentskills """Data models for Agent Skills.""" from dataclasses impo…
category: other
source: majiayu000/claude-skill-registry
---
# models
## When to use
- Imported skill models from agentskills """Data models for Agent Skills.""" from dataclasses import dataclass, field fr…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Decide Fit First / Design Intent / How To Use It” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "models" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Decide Fit First / Design Intent / How To Use It
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}"""Data models for Agent Skills."""
from dataclasses import dataclass, field from typing import Optional
@dataclass class SkillProperties: """Properties parsed from a skill's SKILL.md frontmatter.
Attributes:
name: Skill name in kebab-case (required)
description: What the skill does and when the model should use it (required)
license: License for the skill (optional)
compatibility: Compatibility information for the skill (optional)
allowed_tools: Tool patterns the skill requires (optional, experimental)
metadata: Key-value pairs for client-specific properties (defaults to
empty dict; omitted from to_dict() output when empty)
"""
name: str
description: str
license: Optional[str] = None
compatibility: Optional[str] = None
allowed_tools: Optional[str] = None
metadata: dict[str, str] = field(default_factory=dict)
def to_dict(self) -> dict:
"""Convert to dictionary, excluding None values."""
result = {"name": self.name, "description": self.description}
if self.license is not None:
result["license"] = self.license
if self.compatibility is not None:
result["compatibility"] = self.compatibility
if self.allowed_tools is not None:
result["allowed-tools"] = self.allowed_tools
if self.metadata:
result["metadata"] = self.metadata
return result
Decide Fit First
Design Intent
How To Use It
Boundaries And Review