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https://github.com/willmiao/ComfyUI-Lora-Manager.git
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cf898da193
Introduce an agent skill framework for LLM-driven metadata enrichment: - AgentCLI (py/agent_cli/): in-process wrappers around internal services using standard relative imports, eliminating the need for sys.path hacks - LLMService: centralized BYOK (bring-your-own-key) LLM client supporting OpenAI, Ollama, and custom OpenAI-compatible endpoints - PostProcessor: deterministic engine that applies LLM output via AgentCLI (replaces old handler.py + _BASE_MODEL_ALIASES approach) - SkillRegistry: filesystem-based skill discovery (skill.yaml + prompt.md) - AgentService: orchestrates skill execution with WebSocket progress - Frontend AgentManager: WebSocket listeners, skill execution, config UI - Context menu entries (single + bulk) for "Enrich Metadata (Agent)" - Settings UI for AI Provider configuration (BYOK) - Full i18n support across 9 locales Bug fixes found during review: - aiohttp.web.json_response: status_code= -> status= - settings_modal cancelEditApiKey: wrong argument position - AgentManager.isLlmConfigured: allow Ollama without API key - PostProcessor._merge_tags: lowercase all tags to match TagUpdateService
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
"""Skill definition data structures.
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Each skill is described by a :class:`SkillDefinition` that declares its
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input/output schemas, whether it needs an LLM call, and what permissions
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its post-processor has.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional, Tuple
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@dataclass(frozen=True)
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class SkillPermissions:
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"""Declarative permission scope for a skill's post-processor.
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These are auditable constraints — the :class:`AgentService` checks them
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before invoking the handler. They are defense-in-depth, not a sandbox.
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"""
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write_metadata: bool = True
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write_previews: bool = True
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network_domains: Tuple[str, ...] = ()
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@dataclass(frozen=True)
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class SkillDefinition:
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"""Immutable description of an agent skill."""
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name: str
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title: str
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description: str
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llm_required: bool
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input_schema: Dict[str, Any] = field(default_factory=dict)
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output_schema: Dict[str, Any] = field(default_factory=dict)
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model_type_filter: Optional[List[str]] = None
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permissions: SkillPermissions = field(default_factory=SkillPermissions)
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def applies_to_model_type(self, model_type: str) -> bool:
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"""Return ``True`` if this skill can run on the given model type."""
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if self.model_type_filter is None:
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return True
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return model_type in self.model_type_filter
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