New Settings → System → LLM Skills area: every LLM-powered capability
(Cinematic & Autofit translation, speech-rate slot fitting, glossary
auto-extract, direction parsing, dictation cleanup) becomes a "skill" the
user can toggle or route to a specific provider (local Ollama/LM Studio vs
a remote key) instead of everything riding the one global active provider.
Backend:
- services/llm_skills.py — skill registry + settings_store persistence
(llm_skill.<id>.enabled / .provider), resolution precedence
override > active > none, resolve_skill_client() (OpenAI-compat client
bound to the effective provider; None when disabled/unconfigured) and
skill_backend() (OffBackend when disabled — the exact no-LLM object every
caller already degrades on).
- All five consumption points wired through the registry; a disabled skill
degrades exactly like "no LLM configured" today (Fast translation
fallback, refinement pass-through, heuristic direction parse, no-llm slot
fit, 503 on glossary auto-extract). No new degradation modes; defaults
(enabled + no override) keep existing setups byte-identical.
- OpenAICompatBackend gains an optional bound provider (None = active, the
historical behavior).
- GET /api/settings/llm-skills + PUT /api/settings/llm-skills/{skill_id}
(404 unknown skill/provider); route snapshot updated.
Frontend:
- LLMSkillsPanel (Sparkles, next to LLM Providers): one row per skill —
i18n name/description, enable toggle, provider Select ("Use active
provider" + configured providers, local ones tagged), ready /
needs-setup badge linking to LLM Providers. All strings via t()
(settings.llmskills_*).
Tests: 30 backend (precedence, per-consumption-point disabled semantics,
endpoint round-trips, validation) + 4 panel render/PUT tests. Docs:
translation-engines.md gains an LLM Skills section.
Co-authored-by: mergetest <test@local>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
