Widget entries with unquoted multi-line YAML scalars (e.g. "text: two samurais...\n continuation") were not parsed, leaving gallery image prompts empty. Add a third branch for plain scalar format alongside the existing quoted and >- folded block handlers.
- Add extract_gallery_images() to parse YAML widget entries from README
frontmatter, convert relative image URLs to absolute HF URLs, and
build civitai.images-compatible entries with prompt metadata
- LLM now extracts recommended_width/recommended_height from README
(e.g. "Best Dimensions"), used as gallery image dimensions
- extract_gallery_images() accepts default_width/height parameters,
falling back to 512x512 when LLM provides no recommendation
- Frontend ShowcaseView.js: defensive NaN guard for 0 width/height
- post_processor: consistently merge civitai updates across triggers,
description, and gallery blocks with distinct variable names
- SKILL.md: add recommended_width/recommended_height to output schema
- 62 tests pass, including gallery extraction and dimension tests
- Add identify_model_type() helper to determine lora/checkpoint/embedding
- Pass priority_tags from user settings to LLM prompt for tag relevance
- SKILL.md: instruct LLM to exclude technical/generic HF tags, cross-reference
against priority_tags; forbid ['None'] placeholder for trigger words
- post_processor: fix preview_url not updated after download (now writes local
.webp path to metadata); write trigger words to civitai.trainedWords instead
of top-level; sanitize ['None']/'null'/'n/a' placeholder values to []
- download_preview() now returns str | None (local path) instead of bool
- Update tests for new return type and nested civitai.trainedWords structure
Merge skill.yaml (metadata) and prompt.md (prompt template) into a
single SKILL.md file with YAML frontmatter, matching the agent-skill
convention used by opencode and Claude Code.
- Add frontmatter parser (_parse_skill_file) to SkillRegistry
- Remove skill.yaml, prompt.md, empty skills/__init__.py
- Remove obsolete load_handler method
- Update tests for new format and cleaned-up fields
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
