models

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Repo stars 330
License MIT
Author repo claude-skill-registry

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Imported skill models from agentskills """Data models for Agent Skills.""" from dataclasses import dataclass, field from typing…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The source has limited section structure, so treat it as an invocation rule: trigger, execution boundary, then validation.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name models directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “models” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

"""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

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Other
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 94 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @majiayu000 · MIT
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Detected network behavior: Local-only
Install commands: None (reference only)

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。

Output preview models.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

"""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

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> no H2/H3 headings
terms    -> @dataclass class SkillProperties: """Properties parsed from a skill's SKILL.md frontmatter.
files/cmd -> no explicit files or commands
body sha256 -> 64c98a76be55

Decide Fit First

Design Intent

How To Use It

Boundaries And Review

Discussion