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163,146 tools. Last updated 2026-05-30 15:26

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  • Use when you need structured rows from financial/market/altdata tables. For SEC filing narrative use sec_report_search instead. For company qualitative discovery use company_search instead. For recent news + market events use signal_list instead. Execute a read-only PostgreSQL SELECT query on financial and alternative-data tables. Call `get_table_schema` first to look up columns for a specific table. For alternative-data categories, call `list_tables(categories=[...])` to see tables + columns. SQL constraints: - No CTE (`WITH ... AS`) — use subqueries instead. - Date columns are TEXT — use plain string comparison (`period_end >= '2024-01'`). Never use `::date` cast or `INTERVAL` arithmetic. - No `ROUND(float8, int)` — use `CAST(value AS DECIMAL(10,2))` if rounding is needed. - Structured-data queries: always filter by ticker (`WHERE ticker IN ('A','B','C')`; screening: `ticker NOT LIKE '%-%'` to exclude preferred stock). Alternative data is macro/industry/patent — no ticker filter required. Structured data (tables grouped by domain): ## Market Data - price_volume_history: Historical OHLCV price data, ~32M rows. MUST filter by ticker AND time_frame ('daily'|'weekly'|'monthly') to avoid timeout. Columns: ticker, period_end, time_frame, open, high, low, close, adj_close, volume, turnover, vwap, change, change_percent. Ticker conventions: * STOCK / ETF — bare 1–5 letters (AAPL, MSFT, SPY, QQQ, VOO, TLT). Foreign listings use exchange suffix: 1557.T (JP), 310960.KS (KR). * INDEX — leading "^" (^GSPC=S&P 500, ^DJI, ^IXIC=NASDAQ Composite, ^NDX=NASDAQ 100, ^RUT=Russell 2000, ^VIX). Foreign indices: ^FTSE, ^GDAXI, ^N225, 000001.SS (Shanghai Composite), FTSEMIB.MI. * COMMODITY — [code]+USD or +USX (CLUSD=WTI futures, BZUSD=Brent, NGUSD=natgas, GCUSD=gold, SIUSD=silver, HGUSD=copper, ZCUSX=corn, CTUSX=cotton). Suffix USX = priced in cents. * FOREX — 6 letters base+quote, no separator (EURUSD, USDJPY, GBPUSD, USDCNY). * CRYPTO — [token]+USD (BTCUSD, ETHUSD, SOLUSD, DOGEUSD, USDTUSD, USDCUSD). Pitfalls: 1. Same asset, different tickers: NASDAQ 100 → index ^NDX (~26000) vs ETF QQQ (~640). Pick the one matching user intent. 2. WTI spot ≠ futures. CLUSD here is NYMEX futures, NOT spot (FRED has spot via DCOILWTICO). 3. Tickers with "." or "^" MUST be quoted in SQL: `WHERE ticker = '^NDX'`. - index_price: Real-time price snapshot for 3 major US indices: S&P 500 (^GSPC), Dow Jones (^DJI), NASDAQ 100 (^NDX). - equity_extended_rt: Extended-hours snapshot, one row per ticker (~6.1K US equities; PK=ticker; refreshed every few minutes — see extended_updated_at). Use ONLY for pre-market / after-hours / overnight quotes. Columns: ticker, company_name, market, price_current (last regular-session close), then three parallel blocks pre_* / after_* / overnight_* (price, change_val, change_rate, high, low, volume, turnover), plus extended_updated_at. For regular-session OHLCV history use price_volume_history; for valuation / fundamentals use company_snapshot. Japan tickers are NOT in this view yet — extended-hours data is US-only. ## Fundamentals - financial_statements: Quarterly and annual financial data covering income statement, balance sheet, and cash flow statement. Filter by ticker + fiscal_period ('FY' annual, 'Q1'..'Q4' quarterly). - company_snapshot: Real-time company snapshot, one row per company. Pre-computed metrics (ratios, percentages, per-share figures, growth rates) only — NOT raw statement line items. For raw statements use financial_statements; for qualitative discovery use company_search. ## Earnings - earning_call_summary: Earnings call data with AI-structured summaries (management_highlights, guidance, risks, segment_performance, q_and_a). Filter by ticker + period_end (yyyy-mm). NOT for structured financial numbers. - earning_call_calendar: Earnings conference call schedule with EPS and revenue estimates vs actuals. `date` column is the scheduled earnings call datetime (UTC timestamptz) — NOT the press-release / 10-Q filing date (the filing typically lands the same day or shortly after). Use eps_actual IS NULL for upcoming calls, IS NOT NULL for reported. ## Analyst Coverage - analyst_ratings: Individual analyst rating events — every re-rating is a separate row. ~565K rows, 519 firms. Filter by ticker; `date` is TEXT (string comparison, no ::date cast). importance >= 4 for high-impact calls. - analyst_ratings_consensus: Per-ticker analyst consensus rollup — one row per ticker, refreshed daily. Columns: strong_buy/buy/hold/sell/strong_sell counts, total_analysts, consensus, pt_consensus/high/low. ## Ownership & Insider Activity - insider_and_institution_activities: Insider trades (Form 3/4/5) and institutional holding changes (13D/G/F, 13F-HR) from SEC EDGAR. Filter by ticker + source ('insider' vs 'institution'). For institutions, filer_name is the holder; shares/market_value give position size. ## Corporate Events (8-K) - executive_change: 8-K executive change events — appointments, departures, reasons, successors, effective dates. Filter by ticker + event_type. - company_deal_events: Single source of truth for corporate deal activity (8-K 1.01/1.02/2.01, S-4, press releases). M&A, financing, material agreements as event-based rows (announced/signed/amended/closed/terminated/waived). Filter by ticker only — do NOT filter by deal_type. One deal may span multiple rows linked via deal_fingerprint. - debt_issuance: 8-K debt issuance events — principal amount, interest rate, maturity, lender, use of proceeds. Use is_current = true for latest version when amendments exist. - securities_offering: 8-K securities offering events — shares, price per share, total proceeds, investors, lock-up. Use is_current = true for latest version when amendments exist. ## Executive Profiles & Compensation - executive_profile: Key executive profiles — name, title, compensation, gender, birth year. One row per executive per company. - executive_compensation: Annual executive compensation breakdown — salary, bonus, stock awards, option awards, total. Filter by ticker + year. ## Alternative Data For alternative-data tables, call list_tables(categories=[...]) first.
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  • Runs a free one-off security scan of the given domain and returns its grade (A–F), scan timestamp, and up to three top-priority issues with a permalink to the full report on siteguardian.io. Use this when the user asks for a quick security check of a domain that is NOT yet under SiteGuardian monitoring, or when they want a fresh assessment before subscribing. Results are cached for two hours, so repeated calls about the same domain return the same snapshot and mark it with cached=True. Do NOT use this for domains already under monitoring by the user — call get_domain_status instead for the account-scoped view with framework tags. Do NOT use this to batch-scan many domains as a competitive-intelligence tool; per-source-IP and per-target rate limits bound usage. This tool does not require authentication.
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  • WHEN: generating a visual diagram of D365 table relationships or security chains. Triggers: 'generate diagram', 'diagramme', 'visualize', 'schéma', 'ER diagram', 'entity-relationship', 'relation diagram', 'security diagram', 'show connections'. Generate visual Mermaid diagrams from D365 F&O knowledge base data. Diagrams render directly in Copilot Chat, Cursor, Claude, and markdown viewers. Types: 'er' (entity-relationship diagram for a table and its relations), 'security' (security chain: Role->Duty->Privilege->EntryPoints -- use when you need a VISUAL Mermaid diagram; for the structured text chain with tables of duties/privileges/entry-points use `trace_security_chain` instead). Note: 'flow' (execution flowchart) is disabled -- static call trees are misleading in D365 due to CoC and event handlers.
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  • Returns the full list of domains under continuous SiteGuardian monitoring for the authenticated account. Each entry includes the domain, current security grade (A–F), timestamp of the last completed scan, and a relative dashboard URL. Use this when the user asks what they are monitoring, wants an inventory summary, or needs to look up a specific domain's exact spelling before calling get_domain_status / get_drift_events / get_fix_recommendations. The list is scoped entirely by the API key — there is no filter parameter to widen or narrow the result. Do NOT use this to enumerate domains the user does not own or monitor — it only returns their own inventory. Do NOT call it to trigger a scan (it does not); use scan_domain for one-off checks. Requires a valid API key.
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  • Pro/Teams — return the authenticated user's architect.validate run history with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Three lookup modes: (1) `run_id=<id>` returns a SINGLE run with the full persisted result_json — use this to RECOVER a result when your MCP client tool-call timed out before architect.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every architect.validate call so you have the recovery handle even when your client gives up early. (2) `repository=<name>` returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE calling architect.validate again on the same repository — they tell you which principles regressed since the last run, so you can focus the new review on what is actually changing. Auth: Bearer <token>. Pro or Teams plan required.
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  • WHEN: a user encounters an error message, infolog error, or runtime exception in D365. Also handles business-language error explanation when audienceType='business'. Triggers (developer): 'fix this error', 'what causes', 'exception thrown', 'infolog error', 'update conflict', 'outside tts', 'number sequence'. Triggers (business): 'what does this error mean', 'explain this error to me', 'user gets error X', 'que signifie cette erreur', 'message d\'erreur', 'what should the user do when they see this error'. Find known D365 F&O error patterns matching an error message or symptoms description. Matches against a built-in database of common errors (transaction conflicts, security issues, number sequences, posting errors, batch problems, etc.), resolves D365 label IDs from error text (e.g. user sees 'Number sequence not set up' -> finds @SYS70535 -> finds the throwing code), and searches the indexed codebase. Returns root causes, step-by-step resolution, label matches, and source code locations. [~] When the error text contains a D365 label ID (e.g. '@SYS12345'), call `search_labels` first to resolve the label text, then call this tool with the resolved text. Set audienceType='business' for a plain-language explanation targeted at end users instead of developers.
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  • Use to discover which SEC filings exist for a ticker before searching content. For the actual content use sec_report_search instead. List indexed SEC filings for a given ticker with a summary header. Returns: summary (period coverage, per-type counts) + table of up to 50 filings (fiscal_year, fiscal_quarter, filing_type, filing_date, period_start, period_end). filing_types filter: omit for main reports only (10-K, 10-Q, 20-F, S-1, DEF 14A and /A amendments; excludes 8-K/6-K); pass [] for all indexed types; pass explicit allowlist to override.
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  • Return a textbook-tier explainer of reliability fundamentals: the four reliability functions R(t)/F(t)/f(t)/h(t), MTBF vs MTTF vs MTTR, the availability identity A = MTBF/(MTBF+MTTR), the bathtub curve, and series/parallel system reliability. No inputs. Use when a user asks 'what is reliability theory' / 'explain MTBF' / 'how does availability work' / 'what's a hazard rate'. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md (the canonical ChiAha reliability primer). Quote sections verbatim; do not paraphrase reliability theory from training-data recall.
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  • Evolutionary Symbolic Regression (PySR). Discovers algebraic equations y = f(x1, x2, ...) from feature/target data. Returns a Pareto front ranked by the complexity/accuracy tradeoff. Slower than SINDy (10-60s); searches often terminate early on convergence. For differential equations from time series, use sindy_run instead. Pricing: free tier up to 100 rows × 8 features, 60s timeout. Beyond that, $0.25 + $0.03 per 100 extra rows + $0.01 per extra feature squared, timeout up to 300s (5 min), via x402 (USDC on Base) or MPP/Stripe. MPP/Stripe adds a flat $0.35 per-transaction fee (Stripe processing), so the MPP challenge amount in a `payment_required` response is $0.35 higher than the x402 amount for the same base price; x402 gets the lower rate. Omit `payment` for free-tier requests; paid requests without a valid credential receive a `payment_required` result with pricing and accepted schemes. Full pricing: occam://pricing Advisory limits: jobs over 50,000 rows or 20 features are accepted but may not converge; response carries a top-level `warning`. Operators: fixed supported set only — custom operators (e.g. 'inv(x) = 1/x') are rejected. Unary: sin, cos, tan, exp, log, log2, log10, sqrt, abs, sinh, cosh, tanh. Binary: +, -, *, /, ^. See also prompt `supported_operators`. Loss metric: `loss` (in `pareto_front[].loss` and `best_loss`) is mean squared error between model prediction and `y` on the full training set — not RMSE, and not normalized by Var(y). A threshold appropriate for one dataset scales with y's magnitude, so set `loss_threshold` with that in mind (e.g. for y values near 1.0, 1e-6 is a tight fit; for y near 1000, the equivalent is 1.0). Early termination: set `loss_threshold` to stop at your noise floor. The server also stops when the search stalls (<1% improvement in the last third of the budget); disable with `stall_detection=false`. Response `stop_reason` is one of: loss_threshold, stall, timeout, natural. If `feature_names` is supplied, its length must equal the number of columns in `X`; a mismatch is rejected with a validation error. Follow-up: call `pysr_uncertainty` with a chosen expression and the same dataset for bootstrap confidence intervals on its fit constants and optional prediction bands. Rate limit: 10 requests/hour per IP, 200/hour global, max queue depth 20 (shared with sindy_run and pysr_uncertainty). Response (success) includes `pareto_front[]` (each with `complexity`, `loss`, `expression`, `expression_latex`), `best_expression`, `best_expression_latex`, `best_loss`, `best_complexity`, `stop_reason`, `elapsed_seconds`, `queue_seconds` (>0 = server saturated; use as backoff signal), optional `warning`, optional `_meta` (MPP receipt). Full response and payment-required schemas: occam://tool-schemas Example request: X=[[0.0], [1.0], [2.0], [3.0]], y=[1.0, 3.0, 5.0, 7.0], feature_names=["x"], max_complexity=10, timeout_seconds=15 Policy: occam://privacy-policy — Citation: occam://citation-info
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  • Record mocks for V1 repo-mode API tests using the V1-native CLI command `keploy sandbox local record`. Runs the dev's app under the keploy eBPF agent, drives the V1 chained-CRUD tests from `keploy/api-tests/<resource>/test.yaml`, captures every outbound call (DB queries, Redis ops, downstream HTTP) as mocks, and lays them out at `<app_dir>/keploy/<suite-name>/{tests/, mocks.yaml, config.yaml}` in the standard OSS test-set tree. On success, mocks upload to the Keploy canonical pool by content hash; the hash lands in config.yaml so a teammate's later replay fetches the same bytes. CRITICAL — DO NOT CONFUSE WITH `keploy record sandbox`: * `keploy sandbox local record` (V1, repo-mode) ← this is what the playbook below uses * `keploy record sandbox` (legacy, cloud-mode) ← DO NOT call this for V1 The two are entirely different commands. Cloud-mode requires server-side suites (queried via --suite-ids) — V1 repo-mode reads tests from the local filesystem and never registers them in the cloud. If the dev is in repo storage mode (verify via devloop_resolve_storage's source=persisted, mode=repo), V1 is the ONLY correct sandbox path. STRICT — TIME-FREEZING DOES NOT APPLY TO RECORD. Recording MUST use the dev's regular (prod) Dockerfile or native binary. NEVER spawn the app via Dockerfile.keploy / "-f docker-compose.keploy.yml" / "-tags=faketime" build during record. The faketime binary writes wrong timestamps into captured mocks (it reads time from the offset file, not the wall clock) and the entire capture becomes corrupt — recovery requires re-recording from scratch with the prod binary. If a previous replay failed with expired-JWT and the dev wants to "fix" it, the fix is to re-RUN the replay with --freezeTime, NOT to re-record. The recorded mocks captured against the prod binary are exactly what replay's clock-rewind is designed to validate; touching the record path defeats the whole mechanism. ONLY call this with an explicit dev opt-in. The valid triggers: * Dev directly asks ("capture mocks", "sandbox record", "rerecord the users mocks"). * Post-resource menu (Step 5 of devloop_generate_resource_flow) — dev picks "Capture mocks so CI runs in seconds". * get_session_report shows mock_mismatch_dominant=true AND the dev says yes to your "rerecord?" prompt. Pre-conditions: * Dev's app must NOT already be running (keploy spawns its own copy of the app under the agent's eBPF hooks via the -c command). If a server is up at the target port, KILL IT first or the agent's network capture won't see the traffic. * Real downstream deps (MySQL, Redis, Kafka, etc.) MUST be running — the capture proxies through to them on first contact so the recorded mocks contain real responses. * The test YAML must exist at <app_dir>/keploy/api-tests/<resource>/test.yaml. Returns a playbook for `keploy sandbox local record` with the V1 flag surface: --test-dir, --app-url, -c (spawn command), --container-name (docker-compose only), --skip-mock-upload (offline), --skip-report-upload (offline). Mocks land per-suite at keploy/<suite-name>/. NDJSON progress at --progress-file for the standard tail-til-done loop.
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  • Search D365 F&O labels across all indexed languages. Given text (e.g. 'Sales order'), finds the matching label ID (@SYS12345). Given a label ID (e.g. '@SYS12345' or '@SYS:12345'), finds the text in all languages. Accepts both D365 short form (@SYS124480) and colon form (@SYS:124480) -- both are normalized automatically. Searches across 1 000 000+ label entries. WORKFLOW: call search_labels first to resolve the label text, then call find_references with the same label ID to find ALL X++ objects (forms, tables, classes, reports) that use it in their code or metadata. Languages: en-US and fr are loaded at startup. Other languages (de, nl, ar, es, zh...) are loaded on-demand -- first call ~15s, then instant.
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  • Comparable sales from Land Registry Price Paid Data. Defaults return the standard residential set: - property_type=None means residential (F+D+S+T). Pass "F"/"D"/"S"/"T"/"O" for a single type, or "ALL" to disable type filtering (firehose). - transaction_category defaults to "A" (standard sales). Pass None to include category-B (bulk transfers, non-standard conveyances). - filter_outliers=False by default; set True for IQR-trimmed stats AND transaction list (1.5*IQR rule, needs >=4 prices). limit caps returned transactions (max 200). enrich_epc attaches EPC floor area and price-per-sqft to each transaction — slower but richer.
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  • Given Weibull two-parameter (β, η), return all the closed-form summary statistics: MTTF (η·Γ(1+1/β)), B10 / B50 / B90 life, characteristic life (just η, surfaced explicitly), and — if evaluateAtT supplied — R(t), F(t), and hazard h(t) at that time. Pure-math, fully deterministic. Use when the user has a fit and wants the numbers downstream tools normally compute (don't recompute these from training-data recall — call this tool). ANTI-FABRICATION: every number is an exact closed-form value. Quote verbatim.
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  • AZURE DEVOPS ONLY -- Fetch a Work Item and assemble ALL technical context needed for D365 F&O expert analysis. [~] PRIORITY TRIGGER: 'analyse le workitem', 'analyse la tâche', 'analyse le FDD/RDD/CR/IDD', 'read the work item', 'check the bug', 'look at ticket', 'review task', '#1234', 'WI#', 'WI ', 'item #'. NEVER for: labels (@SYS/@TRX/@FIN), X++ code lookup, AOT objects -- use search_labels / search_d365_code instead. ## WHAT THIS TOOL RETURNS Raw structured context only -- NOT a finished analysis. The tool returns: 1. Work item metadata (title, description, repro steps, acceptance criteria, comments) 2. D365 standard KB object details: fields, methods, code snippets for every matched object 3. Custom code on disk (Aprolis extension): existing CoC methods, extension bodies 4. Chain of Command / relation graph for all impacted objects ## YOUR JOB AS COPILOT AFTER CALLING THIS TOOL You MUST synthesize the raw context into a precise developer-ready analysis IN FRENCH. Write it in a professional tone, as if authored by a senior D365 consultant -- no emojis, no icons. The analysis must contain these sections: 1. **Compréhension du besoin** -- résume ce que le client demande en 2-3 phrases claires 2. **Analyse technique** -- identifie la cause racine en croisant le besoin + les objets KB + le code custom 3. **Instructions de développement** -- liste ordonnée et précise : quel objet, quelle méthode, quoi modifier - Si une extension custom existe sur disque -> pointer exactement quelle méthode à modifier - Si pas d'extension -> indiquer quel CoC créer, sur quel objet standard, quelle méthode 4. **Estimation** -- chiffrage en heures/jours selon la complexité détectée 5. **Commentaire ADO** -- Texte markdown sans icônes, prêt à poster sur le WI analysé UNIQUEMENT. IMPORTANT: never post (never call ado_post_comment) on any linked/related work item -- only on the analyzed WI. Requires DEVOPS_ORG_URL + DEVOPS_PAT env vars.
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  • 落札・入札関連の士業 (司法書士・弁護士・不動産鑑定士・土地家屋調査士) を返す. Stages: post_acquisition (default — 落札後の所有権移転登記; 司法書士が法律上ほぼ必須), pre_bid (入札前の独立評価・権利確認), eviction (明渡し・強制執行), boundary_dispute (境界確定・分筆). Information-only — not a paid intermediary. Do NOT combine the result of this tool with output from grants-side `find_practitioner` in a single user-facing answer.
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  • Form 3 / 4 / 5 / 144 line items for a US public company. Returns each transaction (or initial holding / proposed sale) with the insider's name, role, transaction code, share count, price, and notional. Filters by lookback window, transaction code (P=purchase, S=sale, A=grant, M=option exercise, F=tax withholding, etc.), insider role, and minimum share threshold. Institutional tier only — sample / sp500 / pro return ENTITLEMENT_DENIED with an upgrade link.
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  • WHEN: developer wants to see what custom/extension objects exist in their model. Triggers: 'list my custom objects', 'what have we customized', 'show ISV objects', 'list custom model', 'what objects are in our model'. List all D365 F&O objects in the custom/extension model directory on disk. Reads the file system directly -- always reflects the latest uncommitted state. Pass `customModelPath` to specify a model directory; or set it once via the `D365-Custom-Model-Path` header in your .mcp.json (applies to all tool calls automatically).
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  • WHEN: mapping the technical D365 objects behind a business process, or understanding which tables/forms implement a flow. Triggers: 'processus métier', 'Order-to-Cash', 'Procure-to-Pay', 'Record-to-Report', 'business process flow', 'qui est impliqué dans', 'map the process', 'flux du processus', 'quels objets dans le flux'. Map a D365 F&O business process to its complete object chain. For known processes (Order-to-Cash, Procure-to-Pay, Record-to-Report, Plan-to-Produce, Inventory-Management, Hire-to-Retire, Project-Accounting, Asset-Lifecycle): shows every step with forms, tables, classes, entities, reports, and security roles involved. For any other object name: traces all dependencies (tables, classes, forms, entities) from that entry point. Produces a Mermaid process flow diagram. Use 'list' to see all known process mappings. NOT for a single object's FK relations only -- use `find_related_objects` for that (faster and more precise).
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