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>
P0 — Cinematic/Autofit silently no-op'd on argos/nllb/openai. Those three
branches returned BEFORE _maybe_cinematic, so only the deep_translator
fall-through reached the refine/fit pass. A user on the DEFAULT Argos engine
who picked Cinematic/Autofit got plain Fast output with a success toast and
no quality_used/cinematic_skipped/rate_ratio. All three now route through
_maybe_cinematic. provider=openai is already an LLM translation, so it skips
the reflect/adapt re-refine (new already_llm flag) but still stamps
rate-ratio badges and runs the Autofit fit pass; the dialect it baked into
its translate prompt is now reported applied.
P1 — the Autofit fit pass ran one blocking adjust_for_slot per segment in the
merge loop, OUTSIDE any budget (a 50-seg dub vs a slow provider spun
~50×timeout unbounded). New speech_rate.adjust_for_slot_many fans it out
concurrently under a wall-clock deadline SHARED with the cinematic refine;
segments still running at the deadline degrade to their literal with
rate_error='fit-budget'. Also set max_retries=0 on the OpenAI clients used
for translate/refine/fit so a 429 + Retry-After can't sleep through the budget.
P2 — glossary auto-extract's no-LLM message now points at Settings → LLM
Providers (was the stale TRANSLATE_BASE_URL/TRANSLATE_API_KEY). Provider error
bodies on the glossary auto-extract, the OpenAI translate-segment path, and the
DeepL/Microsoft translate-segment path are now scrubbed
(core.scrub.scrub_provider_error) — they could echo the API key / a user_id.
DubTab re-polls LLM availability on window focus / visibility so configuring a
provider in Settings lifts the Cinematic gate without a remount. Documented
LLM_DEFAULT_PROVIDER in docs/dubbing/translation-engines.md.
Tests: fail-before/pass-after for argos+cinematic (refine runs), argos+cinematic
no-LLM (cinematic_skipped), argos Fast (rate_ratio stamped), openai+autofit
budget bound, and provider-error scrubbing on the translate + glossary paths.
Co-authored-by: mergetest <test@local>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
