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Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

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Deploys, queries, and debugs AWS Marketplace usage-based (PAYG) metering — the pipeline (ResolveCustomer, BatchMeterUsage, EventBridge via SAM) and querying/debugging metering records, statuses, failures, discrepancies. Covers Concurrent Agreements (CustomerAWSAccountId + LicenseArn), multi-product, CloudTrail Event History (90 days; never CloudTrail Lake), Seller Reports (Billed Revenue/Usage columns + CSV/SDDS parsing), existing-stack detection, metering region for BatchMeterUsage/MeterUsage (DisabledApiException), metered AMI/Container + IAM roles, where registration data lives (customer-profile table), legacy ProductCode + CustomerIdentifier, DuplicateRecord/CustomerNotSubscribed, bad LicenseArn. Also SCOPES adjacent asks — flat-rate/SaaS-Subscription "do I still meter", CCP included-units + overage, contract term/annual renewal, forecasting future bills/usage — with the right answer or a one-line redirect. Use for any BatchMeterUsage/ResolveCustomer or SaaS usage-billing question.

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Folder structure for a new Expo app. Use when scaffolding or laying out a new Expo project with Expo Router, or deciding where a file should live in one. For new projects only — never restructure an existing app to match.

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Run and control a user's app on a remote iOS/Android simulator hosted on EAS cloud. Read before running any `eas simulator:*` commands - it has the current syntax for this experimental API. Use whenever the user needs a simulator they can't run locally - 'run my app on a cloud simulator', 'use eas simulator to run/install/screenshot my app', 'I'm on Linux/Cursor and need an iOS device', 'no sim on this box / headless CI', 'let an agent click through my app and screenshot it', 'test my dev build on a remote sim with live reload', 'stream a sim to my browser' - even when they don't say 'EAS Simulator' or 'cloud'. On a host WITHOUT a local simulator (Linux, CI, cloud sandbox) it's the default; on macOS, do NOT auto-trigger for a plain 'run on the simulator' - use it only for a cloud/remote/shareable sim, an iOS version they lack, or an agent-driven session. NOT for local sims (expo run:ios, Xcode, Android Studio), EAS Build/Update, web preview, or physical devices.

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Run an evidence-based production-readiness review for an Attio integration across identity, scopes, data ownership, retries, webhooks, observability, rollback, and support. Use when preparing to enable production Attio traffic. Trigger with "Attio production checklist", "Attio go-live", or "review Attio release".

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Build and operate an Attio webhook receiver with raw-body HMAC verification, fast acknowledgement, durable queues, idempotent processing, retry awareness, and reconciliation. Use when implementing or repairing Attio event delivery. Trigger with "Attio webhooks", "Attio events", or "verify Attio signature".

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Build an AssemblyAI local loop with synthetic fixtures, recorded contracts, and an optional bounded live lane. Use when developing without exposing audio or uncontrolled credits. Trigger with "AssemblyAI local dev" or "mock AssemblyAI".

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Build AssemblyAI CI with offline REST, Streaming v3, webhook, gateway, secret, and lifecycle tests plus a tightly gated live lane. Use when adding automated verification. Trigger with "AssemblyAI CI" or "contract test".

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Analyze and tune AssemblyAI model choice, audio delivery, polling, Streaming v3 turns, concurrency, and downstream work. Use when pursuing measured latency or throughput goals. Trigger with "tune AssemblyAI" or "AssemblyAI latency".

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Analyze and gate an AssemblyAI integration across contracts, security, reliability, cost, observability, retention, and rollback. Use when preparing a launch or material change. Trigger with "AssemblyAI production checklist" or "go live".

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Execute Apollo.io production deployment checklist. Use when preparing to deploy Apollo integrations to production, doing pre-launch verification, or auditing production readiness. Trigger with phrases like "apollo production checklist", "deploy apollo", "apollo go-live", "apollo production ready", "apollo launch checklist".

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Configure CI/CD pipelines for Apify Actor builds and deployments. Use when automating Actor deployment via GitHub Actions, running integration tests against the live Apify API, or building CI/CD for scrapers that push to the Apify platform. Trigger with "apify CI", "apify GitHub Actions", "apify automated deploy", "CI apify", "apify pipeline", "auto deploy actor".

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Production readiness checklist for Anima design-to-code pipelines. Use when deploying automated design-to-code services, preparing CI/CD Figma-to-code automation, or validating output quality before production. Trigger with: "anima production", "anima go-live", "anima prod checklist".

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Clone websites to React/HTML code and customize Anima output with AI. Use when converting live websites to code, customizing generated components, or building design-system-aware code from URL screenshots. Trigger with: "anima website to code", "anima URL clone", "anima AI customization", "website to react", "clone website to code".

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Run a fail-closed production readiness review for an Algolia-backed search release. Use when promoting new indices, settings, credentials, UI behavior, or event tracking. Trigger with "Algolia production checklist", "search go-live", or "Algolia launch review".

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Make an evidence-backed go or no-go decision for an Alchemy-backed production release. Use when reviewing a launch or material integration change. Trigger with "Alchemy production checklist", "Alchemy go-live review", or "is this Alchemy integration ready".

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Operate Alchemy Notify webhooks with raw-body HMAC verification, idempotent intake, durable processing, and safe lifecycle changes. Use when implementing or repairing blockchain event delivery. Trigger with "Alchemy webhook", "Alchemy Notify", or "verify X-Alchemy-Signature".

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Implement and verify callbacks only from an approved tenant-specific Abridge event contract, with replay and PHI protections. Use when vendor implementation documents authorize event delivery. Trigger with "build the Abridge event handler".

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Issue an evidence-backed production decision across Adobe auth, entitlements, API versions, data custody, async reliability, spend, observability, and rollback. Use before launch or a material integration change. Use when the task requires adobe production readiness decision. Trigger with "Adobe production checklist", "approve Adobe launch", or "Adobe go-live review".

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Build deterministic CI gates for Adobe adapters without giving fork jobs credentials or spending against live creative/document APIs. Use when testing integration changes. Trigger with "test Adobe in CI", "Adobe contract tests", or "gate Adobe deploy".

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Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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Rigorous, defensible email-spoofability verdict and SPF supply-chain risk analysis computed from published DNS alone. Deepens the record-level SPF/DMARC/DKIM/BIMI/MTA-STS/DNSSEC fetch recipes in the offensive-osint arsenal (§16.14) with the reasoning that section doesn't do: a priority-ordered composite verdict for whether an attacker can actually land header-From-spoofed mail in an inbox, and by which vector (exact-domain vs subdomain) — grounded in the single most-misunderstood distinction in email security: the envelope MAIL FROM that SPF authenticates vs the visible header From: that only DMARC governs. Explains precisely why SPF -all/~all alone is NOT spoof-proof without DMARC enforcement, and why SPF +all bypasses DMARC even under p=reject pct=100. Covers RFC 7208 §4.6.4's 10-DNS-lookup / 2-void-lookup PermError fail-open condition with a runnable stdlib-only lookup-counter script, plus the SPF-include-takeover supply-chain vector (an attacker re-registering a dead include inherits SPF-pass authority over the victim domain) with strict transient-vs-NXDOMAIN discrimination discipline so a temporary SERVFAIL is never mistaken for a takeover lead. Fully passive: DNS TXT reads only, no mail sent, no RCPT TO probe, no API keys. Use when auditing a domain's real spoofing resistance (not just its published records), explaining to a client why 'we have SPF -all' does not mean they're covered, investigating an SPF PermError or an unusually long include chain, evaluating a dead SPF include as a takeover lead, or writing a defensible spoofability finding for a deliverable.

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Guide for running tests in the openai-dotnet repository. Use this when asked to run, debug, or validate tests, or when writing new tests. Explains test modes (Playback, Record, Live), how to identify recorded vs non-recorded tests, environment variable configuration, and what to do when recordings are missing or stale.

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Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.

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