{"parentUuid":null,"isSidechain":true,"promptId":"43d2c42b-5ddf-4207-ad6a-ea9c89d27d66","agentId":"a47b09b32822459e8","type":"user","message":{"role":"user","content":"Sto continuando una diagnosi urgente iniziata con un altro agente poco fa (che ha trovato la funzione `_carica_file()` in `app/modules/linker/src/gui/ott_sheet.py`, righe 1710-1788, con un push asincrono verso `I:\\SOFTWARE\\PYTHON_SRV\\PYTHON_LOCAL\\MyICR_Suite\\local_db\\parquet\\ott.parquet`).\n\nMauro mi ha appena mandato uno screenshot della vista \"OTT\" dentro Linker: una tabella con colonne DATA IMPORTAZIONE, SEGNALAZ., TITOLO, PROVIDER, TIPO FIN., MONETIZATION, DATA INIZIO, DATA FINE, TIPO, ANNO OTT, STAG., EPIS. OTT, REGISTA OTT, GENERI, PAESI, CODICE, NOTA ICR, PROPOSTA GIRO ICR, NETWORK FREE, FORNITORE DI..., 274 titoli, \"Batch: 2026-09-02\". Tutte le righe visibili hanno DATA IMPORTAZIONE = 02/09/2026 (oggi).\n\nDevo capire: **questa vista/tabella dentro Linker legge i dati da dove?** In particolare:\n1. Legge da un file/tabella LOCALE (es. il `linker.db` SQLite locale, o `ott.parquet` locale appena scritto da `_carica_file()`), oppure legge dal file sul SERVER (`I:\\...\\ott.parquet`)?\n2. Se è locale: significa che vedere \"oggi\" qui NON prova che il push verso il server sia riuscito — potrebbe mostrare solo lo stato locale pre-push.\n3. Cerca la funzione che carica i dati per questa vista specifica (probabilmente in `ott_sheet.py` stesso, un metodo tipo `_load_data()`/`refresh()`/`populate_table()` chiamato all'apertura del tab \"OTT\", o un modello dati collegato alla QTableView). Riporta la query/il path esatto letto.\n4. Se possibile, verifica anche se esiste una colonna o meccanismo per distinguere \"dati importati ma non ancora pushati al server\" da \"dati confermati sul server\" — o se la UI non fa questa distinzione.\n\nRispondi in modo conciso (sotto 250 parole), diagnosi urgente in corso, utente in attesa."},"uuid":"96019284-739f-4591-bc9b-28e61aa559dd","timestamp":"2026-09-02T14:45:46.885Z","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
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{"parentUuid":"e09be619-898a-4ca6-8984-2542391509bf","isSidechain":true,"agentId":"a47b09b32822459e8","attachment":{"type":"skill_listing","content":"- avvia-slave: Comando per lanciare una sessione Claude Code \"slave\" headless con un compito — procedura fissa, nessuna esplorazione. Usare ogni volta che serve avviare una slave, non solo su richiesta esplicita di Mauro (\"crea/avvia una slave che...\").\n- avvio-sessione: Checklist di inizio sessione — identifica l'istanza (Nave/Ufficio), legge memory/MEMORY.md, apre l'hub di archivio/, e su Nave verifica/rilancia sentinelle ed esperti persistenti. Invocare sempre a inizio sessione, idealmente al primo turno — normalmente richiamata da un hook SessionStart, ma va eseguita anche se manca il promemoria dell'hook.\n- chiusura-sessione: Checklist di fine sessione per aggiornare memoria e archivio in modo coerente prima di chiudere — cosa va in memory/MEMORY.md, cosa in archivio/ come storico, cosa in CLAUDE.md, cosa va committato. Usare quando Mauro segnala che sta per chiudere/staccare, o quando si nota che sono passate molte ore/molti argomenti dall'ultimo aggiornamento di memoria.\n- manutenzione-archivio: Scansione meccanica di archivio/ per trovare link rotti, file orfani e hub incompleti — riporta i risultati a Mauro, non corregge nulla in autonomia. Usare su richiesta esplicita di Mauro (\"controlla l'archivio\", \"scansione generale\"), o quando ci si accorge di disordine strutturale mentre si lavora su un'altra cosa.\n- processa-promemoria: Intercetta i promemoria che Mauro scrive su Apple Reminders (arrivano su Microsoft To Do tramite l'account agganciato) e li smista al volo in conversazione — thread in memory/MEMORY.md se serve una casa architetturale, o li lascia dov'è (Attività/Outlook Calendar restano la fonte viva). Consulta anche il calendario (stesso account) per contesto su scadenze/impegni. Modello: check-in periodico, non per-item — usare quando Mauro segnala di aver aggiunto promemoria, chiede di controllare Reminders/calendario, a inizio sessione (passo 5 di `avvio-sessione`), o quando un messaggio inizia con `@gate` (bypassa Reminders, il testo va processato con lo stesso triage).\n- design: Create a design canvas — a multi-artboard visual design published as an Artifact that runs Claude Design's canvas editor (an early preview of Claude Design inside Claude Code). You DRAFT the design as .dc.html artboards laid out on one pan/zoom canvas; where saving is enabled for the user's account they refine every element visually (click-to-select, a properties panel, inline text editing, undo/redo) and Save publishes a new version for everyone, otherwise they get a view-and-export (PNG/PDF) preview of your draft. Good for UI mockups and screen flows, landing pages, marketing and social graphics, and print pieces — posters, flyers, brochures as single-page artboards; memos and reports as one flowing artboard. Use when someone wants a design, mockup, wireframe, UI or screen design, landing page, poster, flyer, brochure, banner, card, one-pager, or any visual layout they would rather tweak by hand than in code. Only for CREATING or re-seeding a canvas; an existing one is edited in its published Artifact.\n- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- artifact-design: Design guidance and fundamentals for Artifacts. - Load before writing any artifact, including a skill-instructed Markdown one — Markdown is never a shortcut past the design pass.\n- artifact-diagramming: Diagramming know-how for Artifacts — when a picture earns its place, how to draw one that shows the real mechanism, and the inline-SVG mechanics that keep it legible in both themes.\n- artifact-capabilities: Runtime capabilities a published Artifact page can be granted — behavior static HTML cannot provide on its own, such as the page reading live or connected data, remembering what people do on it (a poll, a sign-up sheet, a checklist, a document edited in place — it saves new versions of itself), keeping state shared across viewers, knowing who is viewing, asking Claude a question of its own, storing files people add, or handing the viewer a file to save. Serves this user's live capability roster and the typed call definitions. Load it whenever the user asks for an artifact needing any such runtime behavior.\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings; ultra: deep multi-agent review in the cloud); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review. For ultra on a GitHub.com PR target, --post asks to post the finished review’s findings to the PR as a single comment from the user’s GitHub account (not a review; the launch dialog still confirms in interactive sessions, while non-interactive mode posts on the flag alone) and --no-post hides that option.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- run: Launch and drive this project's app to see a change working. 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{"parentUuid":"83a3cbc8-2ea6-41f8-ab0e-da3e094843e9","isSidechain":true,"agentId":"a47b09b32822459e8","message":{"model":"claude-sonnet-5","id":"msg_011CeenyiuVTqUX5WkxFJaNN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U6Xq5VVPiXf2oviE6A86Dh","name":"Bash","input":{"command":"grep -n \"def \\|parquet\\|SELECT\\|linker.db\\|self\\.model\\|populate\\|refresh\\|load_data\\|setModel\\|read_parquet\\|read_sql\\|self\\.df\\b\" /mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/gui/ott_sheet.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":0,"cache_read_input_tokens":47216,"output_tokens":173,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":173,"cache_read_input_tokens":47216,"cache_creation_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Ceenyi1fQwJzU1it735hr","attributionAgent":"general-purpose","type":"assistant","uuid":"a5b7d490-e698-4a46-864f-cfa413968bc5","timestamp":"2026-09-02T14:45:54.852Z","effort":"medium","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"a5b7d490-e698-4a46-864f-cfa413968bc5","isSidechain":true,"promptId":"43d2c42b-5ddf-4207-ad6a-ea9c89d27d66","agentId":"a47b09b32822459e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U6Xq5VVPiXf2oviE6A86Dh","type":"tool_result","content":"5:salva in OTT_REPOSITORY (linker.db), poi aggancia dati da parquet\n83:# ── Gruppo ONAIR — da parquet (38-52) ────────────────────────────────────\n271:    def __init__(self, parent=None):\n280:    def mousePressEvent(self, event):\n293:    def get_selected_cols(self) -> list[int]:\n296:    def paintSection(self, painter: QPainter, rect, logicalIndex: int):\n322:        text = self.model().headerData(\n337:# ── Worker unico: DB load + fetch parquet ─────────────────────────────────\n340:    \"\"\"Fetcha dati parquet per un singolo codice IMDB e li restituisce per una riga.\"\"\"\n344:    def __init__(self, linker_db, row: int, imdb: str):\n346:        self._db   = linker_db\n350:    def run(self):\n395:    \"\"\"Carica OTT filtrato e aggancia i parquet in un unico passaggio.\"\"\"\n401:    def __init__(self, linker_db, mode: str,\n404:        self._db              = linker_db\n409:    def run(self):\n468:    def _fetch_aggregated(self, df, splits: dict | None = None) -> dict:\n469:        \"\"\"Costruisce fetch_cache {imdb: merged_dict} con i dati parquet.\n489:    def _fetch_aggregated_filtered(self, unique_codes: list) -> dict:\n533:    def _fetch_aggregated_full(self, unique_codes: list, splits: dict | None = None) -> dict:\n537:        def _t(key, fn):\n572:def _build_fetch_cache(unique_codes, imdb_to_rifer,\n599:def _iso_to_dmy(s: str) -> str:\n607:def _df_to_map(df, key_col: str) -> dict:\n616:def _df_to_list_map(df, key_col: str) -> dict:\n633:    def __init__(self, linker_db, dev_mode: bool = False,\n634:                 ott_parquet_server_path: str = '', linker_server_path: str = '',\n637:        self._db                       = linker_db\n639:        self._ott_parquet_server_path  = ott_parquet_server_path\n653:    def _ott_save(self, fn, *args, **kwargs):\n674:    def _setup_ui(self):\n778:    def eventFilter(self, obj, event):\n799:    def _on_cell_double_clicked(self, row: int, col: int):\n815:    def _toggle_filter_bar(self):\n818:    def _set_filter_bar_visible(self, visible: bool):\n826:    def _apply_filters(self):\n845:    def _copy_selection(self):\n857:    def _on_hheader_context_menu(self, pos):\n873:    def _on_vheader_context_menu(self, pos):\n886:    def _copy_columns_hdr(self, cols: list[int]):\n897:    def _copy_rows_hdr(self, rows: list[int]):\n905:    def _on_context_menu(self, pos):\n929:    def _inserisci_imdb(self, row: int):\n965:        # Fetch parquet in background solo in modalità filtrato\n969:    def _launch_single_row_fetch(self, row: int, imdb: str):\n982:    def _on_single_row_done(self, row: int, imdb: str, merged: dict):\n987:    def _open_valutazioni_dialog(self, row: int):\n1015:    def _update_prop_giro_cell(self, row: int, imdb: str):\n1026:    def _open_annotation_dialog(self, row: int, col: int):\n1096:    def _on_annotation_item_changed(self, item):\n1137:    def _esporta_sheet(self):\n1159:        def _hdr_color(col_idx: int) -> str:\n1224:    def _build_toolbar(self) -> QWidget:\n1358:    def _load_filtered(self, mode: str, fetch_aggregati: bool = False, debug_mode: bool = False):\n1359:        \"\"\"Avvia caricamento filtrato + aggancio parquet in background.\"\"\"\n1389:    def _clear_load_thread(self):\n1393:    def _on_all_done(self, df, last_date: str, fetch_cache: dict):\n1405:            self._populate_table()\n1414:        self._populate_table()\n1423:    def _on_debug_done(self, df, timings: dict):\n1434:        self._populate_table()\n1463:    def _update_btn_states(self, mode: str):\n1474:    def _apply_filter(self, mode: str):\n1499:        # Nascondi colonne parquet se completo senza aggregati\n1501:        _parquet_cols = [c for c in _ICR_COLS + _IMP_COLS + _ONAIR_COLS + _DIRITTI_COLS + _EMGEN_COLS + _EMTEM_COLS + _BO_COLS\n1503:        hide_parquet = is_completo and not load_aggregati\n1504:        for col in _parquet_cols:\n1505:            self.table.setColumnHidden(col, hide_parquet)\n1509:    def _populate_table(self):\n1519:            def _put(col, val, editable=False, bg=None):\n1563:            # ONAIR group — IMDB da Excel (già noto), resto da parquet dopo aggancia\n1572:            # Valutazioni — sempre vuote (populate da show_valutazioni)\n1581:    def _toggle_valutazioni(self):\n1589:    def _importa_valutazioni(self):\n1624:    def _move_val_cols_after_codice(self):\n1636:    def _reset_val_cols_to_front(self):\n1644:    def _on_sort_indicator_changed(self, logical_index: int, order):\n1648:    def _restore_sort(self, sort_col, sort_order):\n1658:    def show_valutazioni(self, gemma_lookup: dict):\n1686:    def hide_valutazioni(self):\n1710:    def _carica_file(self):\n1716:                \"  1. Pull di linker.db dal server → locale\\n\"\n1717:                \"  2. Scrittura di ott.parquet con il nuovo file\\n\"\n1718:                \"  3. Push di linker.db + ott.parquet sul server\\n\\n\"\n1736:            # Step 1 (DEV): pull linker.db dal server per avere OTT_EXT aggiornato\n1743:                    logger.info(\"_carica_file: pull linker.db dal server completato\")\n1745:                    logger.warning(f\"_carica_file: pull linker.db fallito: {pull_err}\")\n1755:            # Scrivi ott.parquet (replace-all)\n1756:            ott_path = self._db.catalog.ott_parquet_path\n1758:            _pd.DataFrame(records).to_parquet(str(ott_path), index=False)\n1765:            self._db.catalog.refresh_ott_ext(self._db.sqlite_con)\n1769:            # Push ott.parquet sul server in background\n1770:            op = self._ott_parquet_server_path\n1772:                def _push_ott():\n1775:                        logger.info(f\"ott.parquet pushato su {op}\")\n1777:                        logger.warning(f\"Push ott.parquet fallito: {e}\")\n1790:    def _parse_excel(self, path: str) -> list[dict]:\n1827:    def _on_fetch_done(self, imdb_rows: list, data: dict):\n1896:def _set(table: QTableWidget, row: int, col: int, text: str,\n1912:def _fmt_iso_date(val) -> str:\n1921:def _parse_date_str(val: str) -> str:","is_error":false}]},"uuid":"5ac1bb07-7e02-4249-93de-e03d18b99254","timestamp":"2026-09-02T14:45:54.887Z","sourceToolAssistantUUID":"a5b7d490-e698-4a46-864f-cfa413968bc5","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"5ac1bb07-7e02-4249-93de-e03d18b99254","isSidechain":true,"agentId":"a47b09b32822459e8","message":{"model":"claude-sonnet-5","id":"msg_011Ceenysf6frv4fDTubHh47","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":0,"cache_read_input_tokens":49954,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CeenyrtDamJURyub6jsW3","attributionAgent":"general-purpose","type":"assistant","uuid":"92e27819-c823-40c8-9712-fcdcde10deeb","timestamp":"2026-09-02T14:45:57.627Z","effort":"medium","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"92e27819-c823-40c8-9712-fcdcde10deeb","isSidechain":true,"agentId":"a47b09b32822459e8","message":{"model":"claude-sonnet-5","id":"msg_011Ceenysf6frv4fDTubHh47","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JaRAZypxTZDHPCnFjNncH9","name":"Read","input":{"file_path":"/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/gui/ott_sheet.py","offset":395,"limit":75},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":0,"cache_read_input_tokens":49954,"output_tokens":268,"output_tokens_details":{"thinking_tokens":119},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":268,"cache_read_input_tokens":49954,"cache_creation_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CeenyrtDamJURyub6jsW3","attributionAgent":"general-purpose","type":"assistant","uuid":"50f7e8e9-9431-40ce-80b4-1349d6eb18ac","timestamp":"2026-09-02T14:45:59.056Z","effort":"medium","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"50f7e8e9-9431-40ce-80b4-1349d6eb18ac","isSidechain":true,"promptId":"43d2c42b-5ddf-4207-ad6a-ea9c89d27d66","agentId":"a47b09b32822459e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JaRAZypxTZDHPCnFjNncH9","type":"tool_result","content":"395\t    \"\"\"Carica OTT filtrato e aggancia i parquet in un unico passaggio.\"\"\"\n396\t    all_done   = pyqtSignal(object, str, dict)  # (DataFrame | None, last_date, fetch_cache)\n397\t    debug_done = pyqtSignal(object, dict)       # (DataFrame | None, {step: seconds}) — solo in debug_mode\n398\t    progress   = pyqtSignal(int)                # 0-100\n399\t    finished   = pyqtSignal()\n400\t\n401\t    def __init__(self, linker_db, mode: str,\n402\t                 fetch_aggregati: bool = False, debug_mode: bool = False):\n403\t        super().__init__()\n404\t        self._db              = linker_db\n405\t        self._mode            = mode\n406\t        self._fetch_aggregati = fetch_aggregati\n407\t        self._debug_mode      = debug_mode\n408\t\n409\t    def run(self):\n410\t        import time\n411\t        from datetime import date as _date\n412\t        try:\n413\t            today = _date.today().isoformat()\n414\t            anno_min = int(today[:4]) - 5\n415\t\n416\t            self.progress.emit(5)\n417\t            t_pre0 = time.perf_counter()\n418\t            is_filtrato = self._mode.endswith('_filtrato')\n419\t            last   = self._db.get_ott_ext_last_date() if is_filtrato else None\n420\t            cutoff = self._db.get_ott_ext_last_dates(3) if is_filtrato else None\n421\t            t_pre  = time.perf_counter() - t_pre0\n422\t\n423\t            t0 = time.perf_counter()\n424\t            df = self._db.get_ott_joined_for_mode(self._mode, anno_min, cutoff)\n425\t            t_join = time.perf_counter() - t0\n426\t\n427\t            self.progress.emit(15)\n428\t            fetch_cache: dict = {}\n429\t            t_agg = None\n430\t            agg_err_msg = None\n431\t\n432\t            splits  = None\n433\t            run_agg = not self._debug_mode and not df.empty and (\n434\t                self._mode.endswith('_filtrato') or self._fetch_aggregati)\n435\t            if run_agg:\n436\t                splits = None\n437\t                t1 = time.perf_counter()\n438\t                try:\n439\t                    fetch_cache = self._fetch_aggregated(df)\n440\t                except Exception as agg_err:\n441\t                    agg_err_msg = str(agg_err)\n442\t                    logger.error(f\"OTT aggregation fallita (non critica): {agg_err}\", exc_info=True)\n443\t                finally:\n444\t                    t_agg = time.perf_counter() - t1\n445\t\n446\t            self.progress.emit(100)\n447\t\n448\t            if self._debug_mode:\n449\t                self.debug_done.emit(df if not df.empty else None, {\n450\t                    't_pre':    t_pre,\n451\t                    't_join':   t_join,\n452\t                    't_agg':    t_agg,\n453\t                    'agg_err':  agg_err_msg,\n454\t                    'splits':   splits or {},\n455\t                    'n_record': len(df),\n456\t                })\n457\t            else:\n458\t                self.all_done.emit(df if not df.empty else None, last or '', fetch_cache)\n459\t        except Exception as e:\n460\t            logger.error(f\"OTT _LoadWorker: {e}\", exc_info=True)\n461\t            if self._debug_mode:\n462\t                self.debug_done.emit(None, {'error': str(e)})\n463\t            else:\n464\t                self.all_done.emit(None, '', {})\n465\t        finally:\n466\t            self.finished.emit()\n467\t\n468\t    def _fetch_aggregated(self, df, splits: dict | None = None) -> dict:\n469\t        \"\"\"Costruisce fetch_cache {imdb: merged_dict} con i dati parquet."}]},"uuid":"7dd177ca-8637-4838-954d-c806e159368d","timestamp":"2026-09-02T14:45:59.116Z","sourceToolAssistantUUID":"50f7e8e9-9431-40ce-80b4-1349d6eb18ac","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"7dd177ca-8637-4838-954d-c806e159368d","isSidechain":true,"agentId":"a47b09b32822459e8","message":{"model":"claude-sonnet-5","id":"msg_011CeenzC5NS22M8zYWc4oBW","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"EoQECqgBCBEYAipAQxRV92YTB/vxhZkSaxbrGJ2EskoedYYLHo+N1Z+whfWyv28n/at6XwEhKE3E0FY22gsc9K9P1sXv3RMjyuxoJDIPY2xhdWRlLXNvbm5ldC01OABCCHRoaW5raW5nWiQzZDFjY2UyZS0wNjY0LTQwZDYtODQxNC1iN2U1MjhhY2UyYjFyEFzXaGFxco142qPhtdJazWmIAQGoAart4NQGsAECEgzC1rc5jx9UQlVFYMUaDBPNCHVdGNdBcKMN0CIwLlkLkYfKmxcS0tr40hiPpKq0/ZgUoAx4Z2vysy/i7hWSR/fTHjXyj7A/vwEof5taKogCGihAqrdrI7vzM4yhqhmrQIPsYYVZ5kfgyF5k8A7ZRK8Klh5HrVV2Sh9OrIL8rvfz8WP9cBUL57oC4HJcJxki1ZkuEFJCbCe1RDHfW+i9v0Ddv92I3MCdmyyv5j0qydN9WEatQ+M0leBV1rw3i0hT0s90N5QpaLkAU222arX6H/dpkwKnH+G9bxg1UPj7mwD5+VBFvFOhucx7o+qIcT45Afb1rWmM5VGvAIBhtT8M8TX3EJxeO9hFItIr0J8amR++e29csjYllTyh+ApI+vnTrT0fUDMofcJ9Pm7TARm0PAkheiZmoTYoLpDZ/khkkdxdDzIVpvIgeoS0Xw6HjC9N2hNQuxnXo0F0GAE="}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":0,"cache_read_input_tokens":59411,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CeenzB7po3Cac4Gtsz7sz","attributionAgent":"general-purpose","type":"assistant","uuid":"5b79f6e5-b0e4-4f9d-a575-aefac936bc02","timestamp":"2026-09-02T14:46:02.129Z","effort":"medium","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"5b79f6e5-b0e4-4f9d-a575-aefac936bc02","isSidechain":true,"agentId":"a47b09b32822459e8","message":{"model":"claude-sonnet-5","id":"msg_011CeenzC5NS22M8zYWc4oBW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xp9Az8PX3Cs1WhUaBcBkt4","name":"Bash","input":{"command":"grep -rn \"def get_ott_joined_for_mode\\|def get_ott_ext_last_date\\|ott_parquet_path\\|def refresh_ott_ext\" /mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/ 2>/dev/null"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":0,"cache_read_input_tokens":59411,"output_tokens":255,"output_tokens_details":{"thinking_tokens":99},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":255,"cache_read_input_tokens":59411,"cache_creation_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CeenzB7po3Cac4Gtsz7sz","attributionAgent":"general-purpose","type":"assistant","uuid":"af6a7857-7086-4475-a38e-4f421d948608","timestamp":"2026-09-02T14:46:02.948Z","effort":"medium","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"af6a7857-7086-4475-a38e-4f421d948608","isSidechain":true,"promptId":"43d2c42b-5ddf-4207-ad6a-ea9c89d27d66","agentId":"a47b09b32822459e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Xp9Az8PX3Cs1WhUaBcBkt4","type":"tool_result","content":"/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/parquet_db.py:747:    def ott_parquet_path(self) -> Path:\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/parquet_db.py:769:    def refresh_ott_ext(self, sqlite_con) -> int:\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/parquet_db.py:773:    def get_ott_joined_for_mode(self, mode: str,\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/parquet_db.py:777:        ott_f = str(self.ott_parquet_path).replace('\\\\', '/')\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/parquet_db.py:778:        if not self.ott_parquet_path.exists():\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/linker_db.py:621:    def get_ott_ext_last_date(self) -> str | None:\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/linker_db.py:645:    def get_ott_ext_last_dates(self, n_batches: int = 3) -> list[str]:\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/linker_db.py:676:    def get_ott_joined_for_mode(self, mode: str,\n/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/gui/ott_sheet.py:1756:            ott_path = self._db.catalog.ott_parquet_path","is_error":false}]},"uuid":"843f2f5d-395e-4078-9888-20c5a1628d31","timestamp":"2026-09-02T14:46:03.004Z","sourceToolAssistantUUID":"af6a7857-7086-4475-a38e-4f421d948608","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"843f2f5d-395e-4078-9888-20c5a1628d31","isSidechain":true,"agentId":"a47b09b32822459e8","message":{"model":"claude-sonnet-5","id":"msg_011CeenzUmTuCdFBUWbPB9wt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HaseGHmF7zers8ciEJTn6b","name":"Read","input":{"file_path":"/mnt/ssd/data/Dropbox/adrian/PYTHON/MyICR_Suite/app/modules/linker/src/database/parquet_db.py","offset":740,"limit":50},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":0,"cache_read_input_tokens":60451,"output_tokens":146,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":146,"cache_read_input_tokens":60451,"cache_creation_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CeenzTxMYEFcXigZLM9Jx","attributionAgent":"general-purpose","type":"assistant","uuid":"22b0b334-285f-4f62-a3f7-f1395ad64147","timestamp":"2026-09-02T14:46:05.217Z","effort":"medium","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"22b0b334-285f-4f62-a3f7-f1395ad64147","isSidechain":true,"promptId":"43d2c42b-5ddf-4207-ad6a-ea9c89d27d66","agentId":"a47b09b32822459e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HaseGHmF7zers8ciEJTn6b","type":"tool_result","content":"740\t            return pd.DataFrame()\n741\t\n742\t    # ── OTT (Parquet) + OTT_EXT (SQLite in-memory cache) ────────────────────\n743\t\n744\t    _SVOD_PROVIDERS = ('netflix', 'amazon prime video', 'apple tv plus', 'apple tv+')\n745\t\n746\t    @property\n747\t    def ott_parquet_path(self) -> Path:\n748\t        return self.parquet_path / 'ott.parquet'\n749\t\n750\t    def register_ott_ext(self, sqlite_con) -> int:\n751\t        \"\"\"Carica OTT_EXT da SQLite in una tabella DuckDB in-memory.\n752\t        Chiamato al boot del Linker e dopo ogni scrittura su OTT_EXT.\"\"\"\n753\t        try:\n754\t            df = pd.read_sql(\n755\t                \"SELECT mediaset_id, provider, data_creazione, \"\n756\t                \"segnalazioni_automatiche, imdb_override FROM OTT_EXT\",\n757\t                sqlite_con,\n758\t            )\n759\t            con = self._get_con()\n760\t            con.register('_ott_ext_tmp', df)\n761\t            con.execute(\"CREATE OR REPLACE TABLE ott_ext AS SELECT * FROM _ott_ext_tmp\")\n762\t            con.unregister('_ott_ext_tmp')\n763\t            logger.info(f\"ParquetCatalog: ott_ext caricato ({len(df)} righe)\")\n764\t            return len(df)\n765\t        except Exception as e:\n766\t            logger.error(f\"register_ott_ext: {e}\")\n767\t            return 0\n768\t\n769\t    def refresh_ott_ext(self, sqlite_con) -> int:\n770\t        \"\"\"Ricarica ott_ext dopo scritture su OTT_EXT (imdb_override, segnalazioni).\"\"\"\n771\t        return self.register_ott_ext(sqlite_con)\n772\t\n773\t    def get_ott_joined_for_mode(self, mode: str,\n774\t                                anno_min: int = 0,\n775\t                                cutoff: str = None) -> pd.DataFrame:\n776\t        \"\"\"JOIN DuckDB tra ott.parquet e ott_ext (in-memory) per la modalità richiesta.\"\"\"\n777\t        ott_f = str(self.ott_parquet_path).replace('\\\\', '/')\n778\t        if not self.ott_parquet_path.exists():\n779\t            logger.warning(\"get_ott_joined_for_mode: ott.parquet non trovato\")\n780\t            return pd.DataFrame()\n781\t\n782\t        tipo = 'movie' if 'movie' in mode else 'show'\n783\t        p    = ', '.join(f\"'{v}'\" for v in self._SVOD_PROVIDERS)\n784\t        p    = f\"({p})\"\n785\t\n786\t        def _date_filter(cutoff):\n787\t            if not cutoff:\n788\t                return ''\n789\t            if isinstance(cutoff, list):"}]},"uuid":"bbcdfd99-791b-45fd-94ea-6c1f0276e431","timestamp":"2026-09-02T14:46:05.273Z","sourceToolAssistantUUID":"22b0b334-285f-4f62-a3f7-f1395ad64147","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}
{"parentUuid":"bbcdfd99-791b-45fd-94ea-6c1f0276e431","isSidechain":true,"agentId":"a47b09b32822459e8","message":{"model":"claude-sonnet-5","id":"msg_011Ceenzekjt4PDBhnWdjeQh","type":"message","role":"assistant","content":[{"type":"text","text":"Ho la conferma completa. Diagnosi:\n\n**1. Legge dal file LOCALE**, non dal server. `get_ott_joined_for_mode` (in `parquet_db.py:773`) fa un JOIN DuckDB tra:\n- `self.ott_parquet_path` = `self.parquet_path / 'ott.parquet'` → **locale** (`local_db/parquet/ott.parquet`), NON `I:\\...\\ott.parquet`\n- `ott_ext` (tabella DuckDB in-memory, caricata da SQLite `linker.db` locale via `register_ott_ext`)\n\nChiamata dal worker `_LoadWorker.run()` (righe 409-424 di `ott_sheet.py`), lanciato al popolamento del tab OTT.\n\n**2. Sì — \"oggi\" nella UI non prova il push riuscito.** In `_carica_file()` (righe 1710-1788) la sequenza è: (a) scrivi `ott.parquet` locale (riga 1758, `to_parquet` sincrono), (b) `refresh_ott_ext` locale (riga 1765), (c) **poi** ricarica la tabella (che quindi mostra già il dato locale), (d) push verso `I:\\...\\ott.parquet` in background thread separato (righe 1769-1777), con solo un log su successo/fallimento, nessun feedback in UI. Quindi Batch 2026-09-02 con 274 righe è garantito solo localmente — il push potrebbe essere ancora in corso o fallito silenziosamente (log: `\"Push ott.parquet fallito: {e}\"`).\n\n**3-4. Nessuna distinzione UI tra \"importato localmente\" e \"confermato sul server\".** Non esiste colonna né indicatore — la vista OTT è puramente locale, il risultato del push è visibile solo nei log applicativi, non riflesso da nessun campo della tabella.\n\n**Verifica consigliata**: controllare i log dell'app (o timestamp/mtime del file server `I:\\SOFTWARE\\PYTHON_SRV\\PYTHON_LOCAL\\MyICR_Suite\\local_db\\parquet\\ott.parquet`) per confermare se il push in background è effettivamente andato a buon fine, indipendentemente da cosa mostra la griglia."}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":0,"cache_read_input_tokens":61640,"output_tokens":778,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":778,"cache_read_input_tokens":61640,"cache_creation_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Ceenzdtea4fJHUhuwcFrF","attributionAgent":"general-purpose","type":"assistant","uuid":"ad36c57b-5123-4cc9-997a-be13409e63a6","timestamp":"2026-09-02T14:46:15.967Z","effort":"medium","userType":"external","entrypoint":"claude-vscode","cwd":"/mnt/ssd/data/Dropbox/adrian","sessionId":"5a14f7dc-5efb-4f59-aa45-206d46b6349c","version":"2.1.241","gitBranch":"master","slug":"glowing-hugging-wilkes"}