feat: ContentGuardianAgent, onboarding UX, Team Activity action wiring, docs, agent help modal

ContentGuardianAgent consolidation:
- Merge 3 duplicate classes into single source in specialized/content_guardian.py
- Watchdog audit_committee() with heuristic scoring, coverage gaps, overlaps, alerts
- Remove misleading rejection_rate() helper; use acceptance_rate directly
- Integrate audit + alerts + trend signals into today_workflow_service.py

Team Activity page:
- QualityAuditPanel: health ring, per-agent critiques, coverage gaps, overlaps
- TrendSignalsPanel: opportunity cards with urgency/impact/coverage bars
- AlertBanner: persistent dismiss via POST /alerts/{id}/mark-read
- AgentHelpModal: dialog showing all 8 agents with descriptions, tools, schedule
- QualityAuditPanel action buttons: Fill gap -> /content-planning, Resolve overlap, View CTA on alerts/issues
- TrendSignalsPanel action buttons: Create content from this trend -> /blog-writer with trend context state

Onboarding system:
- Step 4 validation: no auto-pass via basic_ready; requires persona data or explicit progression
- Step 5 validation: logs warning on auto-pass without integration data
- OnboardingCompletionService: single DB session, transactional task creation, upsert pattern
- Business-without-website: nullable website_url on SIFIndexingTask and MarketTrendsTask
- DeepCompetitorAnalysisExecutor: 5-min timeout, 10-competitor cap, asyncio.wait_for
- Persona generation: async with 30s timeout, falls back to scheduler
- OnboardingProgressService.reset_onboarding(): resets session + pauses all DB tasks
- OnboardingControlService.reset_onboarding(): also cancels APScheduler jobs
- FinalStep TaskSchedulingPanel: shows scheduled/failed tasks after completion, 8s auto-redirect
- onboarding_completed agent activity event logged to feed

Documentation:
- docs-site/features/onboarding/: overview, steps, scheduler-tasks, technical-reference (4 pages)
- docs-site/mkdocs.yml: added Onboarding System nav section
- docs-site/features/sif-agents/: overview, agent-directory, committee-system, content-guardian (4 pages)
- docs-site/features/team-activity/: overview, quality-audit, trend-signals, alert-system (4 pages)
- docs-site/features/todays-workflow/: updated overview, technical-architecture, workflow-guide, api-reference

backend/services/llm_providers/main_text_generation.py

@@ -375,9 +375,13 @@ def llm_text_gen(
                         system_prompt=system_instructions
                     )
             elif gpt_provider == "wavespeed":
-                llm_start = time.time()
+                t0 = time.time()
+                logger.warning(f"[llm_text_gen][{flow_tag}] wavespeed: Starting provider init for user {user_id}")
                 if json_struct:
+                    logger.warning(f"[llm_text_gen][{flow_tag}] wavespeed: Importing wavespeed_provider module (lazy import) for user {user_id}")
                     from services.llm_providers.wavespeed_provider import wavespeed_structured_json_response
+                    logger.warning(f"[llm_text_gen][{flow_tag}] wavespeed: Import done, making API call for user {user_id}, import_took={(time.time()-t0)*1000:.0f}ms")
+                    t1 = time.time()
                     response_text = wavespeed_structured_json_response(
                         prompt=prompt,
                         schema=json_struct,
@@ -387,7 +391,10 @@ def llm_text_gen(
                         system_prompt=system_instructions
                     )
                 else:
+                    logger.warning(f"[llm_text_gen][{flow_tag}] wavespeed: Importing wavespeed_provider module (lazy import) for user {user_id}")
                     from services.llm_providers.wavespeed_provider import wavespeed_text_response
+                    logger.warning(f"[llm_text_gen][{flow_tag}] wavespeed: Import done, making API call for user {user_id}, import_took={(time.time()-t0)*1000:.0f}ms")
+                    t1 = time.time()
                     response_text = wavespeed_text_response(
                         prompt=prompt,
                         model=model or "openai/gpt-oss-120b",
@@ -396,8 +403,9 @@ def llm_text_gen(
                         top_p=top_p,
                         system_prompt=system_instructions
                     )
-                llm_ms = (time.time() - llm_start) * 1000
-                logger.warning(f"[llm_text_gen][{flow_tag}] LLM API call took {llm_ms:.0f}ms for user {user_id} (wavespeed)")
+                api_took_ms = (time.time() - t1) * 1000
+                total_ms = (time.time() - t0) * 1000
+                logger.warning(f"[llm_text_gen][{flow_tag}] wavespeed: user={user_id} import_took={(t1-t0)*1000:.0f}ms api_took={api_took_ms:.0f}ms total={total_ms:.0f}ms")
             else:
                 logger.error(f"[llm_text_gen] Unknown provider: {gpt_provider}")
                 raise RuntimeError(f"Unknown LLM provider: {gpt_provider}. Supported providers: google, huggingface, wavespeed")