“riva”的搜索结果

Write Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.

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Guides compliance with Australia's Privacy Act 1988 including the 2024 reform amendments. Covers automated decision-making transparency, children's privacy code, individual rights expansion, enforcement strengthening, and the Australian Privacy Principles (APPs). Keywords: Australia Privacy Act, APPs, OAIC, automated decisions, children privacy code, privacy reform.

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Create an Estuary derivation that maintains a sliding time window of events (e.g. "transfers in the last 24h", "failed logins in the last 5 minutes") using internal SQLite state and readDelay for time-based expiration. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "windowing derivation", "derivation for last-24h events", "rolling-window derivation", "sliding-window transformation", "fraud-detection derivation", "rate-limit derivation", or "time-window derivation".

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Design privacy-preserving analytics systems using differential privacy, k-anonymity, l-diversity, and t-closeness. Covers privacy budget allocation with epsilon tracking, references Google DP library, OpenDP, and Apple PPML. Includes Python differential privacy implementation for GDPR-compliant statistical analysis.

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Create an Estuary derivation for stateless filtering, field selection, and per-document field transformation. Use for WHERE clauses, projecting fields, computing new fields from existing ones, or data cleansing. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (renaming alone is usually a projection, not a derivation). Use when user says "filter-transform derivation", "derivation to filter by status", "transformation with WHERE clause", "derive computed fields", "cleanse data with a derivation", or "stateless derivation".

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Create an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".

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Turn scattered research equations and notes into a structured derivation document. Clarify the target, assumptions, and notation; distinguish exact steps from approximations and interpretations; and report missing conditions or logical gaps when the derivation cannot be supported.

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• Organize scattered equations into a coherent derivation
• Choose the central quantity for a theory
• Separate exact steps, approximations, and interpretations

Set up a privacy legal plugin through an interview and existing documents. Capture applicable regulations, data processing agreement positions, and privacy impact assessment formats in shared settings, with options to resume setup or recheck integrations.

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• Setting up the privacy plugin for the first time
• Capturing team standards from existing privacy documents
• Resuming or revising an incomplete setup interview

Update one part of an existing privacy practice profile through a guided conversation. Change risk preferences, contract review positions, contacts, privacy assessment formats, or personal data request procedures, with an explanation of how the change affects later outputs.

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• Update privacy contacts and escalation routes
• Revise data processing agreement review criteria
• Change personal data request response procedures

처리방침·이용약관 자동 생성 진입점. 호출 즉시 6개 하위 스킬(privacy-kr·privacy-eu·privacy-us·privacy-jp·privacy-global·privacy-global-jp)을 번호 메뉴로 제시하고, 번호 입력 즉시 해당 스킬 인터뷰로 직행한다.

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Use this skill when a published privacy notice may no longer match what the company does, or someone asks "is our privacy policy accurate", "check our privacy notice against the RoPA", "privacy notice review", "Article 13 and 14 check", "does our website privacy policy cover our vendors and cookies" or "update the privacy notice for the new product". Maps every collection point, compares each notice with the processing record, the vendor list and what the site and product actually collect, classifies what is missing, wrong, vague or stale, and drafts a redline plus the practice fixes that a notice edit cannot solve.

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Create an Estuary derivation with custom SQLite state (internal tables) to make per-document decisions based on accumulated history — account balances, inventory levels, approval workflows, deduplication. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "stateful derivation", "derivation to track balance", "transformation to approve based on state", "derive an approval workflow", "deduplication derivation", or "build a stateful derivation".

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Create an Estuary derivation that joins two or more collections on a shared key, merging their fields into a single enriched collection. Use for data enrichment, denormalisation, building wide tables for BI, and real-time CDC joins. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (a destination-side SQL join or BI tool view is often simpler). Use when user says "join-collections derivation", "derivation to join two collections", "transformation to enrich with customer data", "derive a wide table", "lookup-join derivation", or "build a join derivation".

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Draft a privacy policy based on the data your product collects and the regions it serves. Receive a summary, full policy draft, and customization notes that identify clauses requiring review by a privacy lawyer.

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• Drafting a privacy policy for a new service
• Updating documentation after data practices change
• Organizing privacy requirements for EU or California users

Compare saved privacy reviews or proposed data uses with current privacy commitments. Identify missing or conflicting disclosures, distinguish required from advisable updates, and draft suggested wording with recommendations on when to make changes.

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• Check whether a proposed data use is covered by the privacy policy
• Review saved privacy assessments for policy gaps
• Compare consent banners and app store labels with the policy

Triage proposed personal data uses to determine whether they can proceed, need a privacy impact assessment, require a legally mandated assessment, or must stop. Results explain policy conflicts, applicable triggers, and conditions to address before proceeding.

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• Checking whether a new feature needs a privacy assessment
• Reviewing proposed data uses involving a vendor
• Finding conflicts with privacy policy commitments

Maps the US federal privacy landscape including sectoral laws (HIPAA, GLBA, FERPA, COPPA, FCRA, ECPA, VPPA), FTC Section 5 enforcement, proposed federal comprehensive legislation, and the interaction between federal and state privacy regimes. Keywords: federal privacy, HIPAA, GLBA, FERPA, COPPA, FCRA, FTC, sectoral, preemption.

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Guides privacy program effectiveness measurement including leading and lagging indicators, KPI definition, benchmarking methodology, executive reporting formats, board-level privacy dashboards, and metric-driven program improvement. Covers operational, compliance, risk, and strategic privacy metrics across the program lifecycle. Keywords: privacy metrics, KPIs, benchmarking, executive reporting, dashboard, program effectiveness.

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Guides privacy program maturity assessment using the AICPA/CIPT Privacy Maturity Model with five levels: Ad Hoc, Repeating, Defined, Managed, and Optimized. Covers assessment methodology across ten privacy domains, scoring criteria, gap analysis, maturity roadmap generation, and benchmarking against industry peers. Keywords: privacy maturity, AICPA, maturity model, assessment, roadmap, benchmarking.

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Guides implementation of the NIST Privacy Framework IDENTIFY function covering ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Maps NIST PF controls to GDPR requirements for dual-framework compliance. Keywords: NIST Privacy Framework, IDENTIFY function, ID.BE, ID.DA, ID.IM, ID.RA, privacy risk assessment, data actions.

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Implements HIPAA Privacy Rule requirements for research uses of protected health information under 45 CFR §164.512(i). Covers IRB and Privacy Board waivers of authorization, individual authorization for research, limited data set and data use agreements, preparatory to research provisions, and decedent research provisions. Keywords: HIPAA research, IRB waiver, Privacy Board, authorization, limited data set, preparatory research, de-identification, Common Rule.

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Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local differential privacy models.

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Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.

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Implements federated learning architecture patterns for GDPR compliance. Covers secure aggregation protocols, differential privacy integration, communication protocols, and privacy-by-design distributed ML training. Keywords: federated learning, distributed training, secure aggregation, differential privacy, privacy-preserving ML.

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