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Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Build AI data deduplication systems. TRIGGERS - Use when user needs help with ai-deduplication related tasks.

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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Build AI data deduplication systems. TRIGGERS - Use when user needs help with ai-deduplication related tasks.

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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Find prospects by role, industry, company size, and location, then add approved contacts to an Apollo sequence, a series of outreach emails. The workflow enriches details, creates contacts with deduplication, and reports enrollment results.

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• Finding prospects by role and company criteria
• Adding a batch of contacts to an Apollo sequence
• Loading another batch into an existing sequence

Convert Snowflake dbt SQL models to BigQuery through a bulk migration workflow. Preserve Jinja template logic while adapting configuration, explicit type conversions, JSON access, and deduplication patterns, with translated files saved in their directory structure.

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• Migrating Snowflake dbt models to BigQuery in bulk
• Preserving dbt references and incremental processing logic
• Adapting JSON, array, and STRUCT access during migration

Audit server-side advertising measurement by comparing browser and server events, checking duplicate handling, consent, and data quality. Reconcile platform diagnostics with source logs and business conversions, then return prioritized findings, owners, and verification steps.

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• Investigate missing or duplicated conversion events
• Compare browser and server event data
• Review consent and data handling before transmission

Audit server-side paid-media measurement including server-side tag management, platform conversion APIs, event taxonomy, browser/server deduplication, consent, hashing, data quality, observability, and privacy. Use for server-side tracking, sGTM, server-side tagging, Meta CAPI, TikTok Events API, event_id, deduplication, pixel debugging, first-party measurement, or conversion data loss.

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Deploy DefectDojo as a centralized vulnerability management dashboard with scanner integrations, deduplication, metrics tracking, and Jira ticketing workflows.

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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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Deploy DefectDojo as a centralized vulnerability management dashboard with scanner integrations, deduplication, metrics tracking, and Jira ticketing workflows.

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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Deploy DefectDojo as a centralized vulnerability management dashboard with scanner integrations, deduplication, metrics tracking, and Jira ticketing workflows.

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梳理GitHub、Linear及本地通知，按重要程度和负责人设计接收渠道，合并重复事件，并明确哪些应即时提醒、汇总发送、进入待办列表或仅记录日志。

• 统一多个工具的通知流程
• 减少CI失败的重复提醒
• 区分紧急提醒与汇总通知

Organize parallel code reviews across security, performance, architecture, testing, and accessibility. It helps assign review areas, merge duplicate findings, apply consistent severity ratings, and produce one report with locations, impacts, suggested fixes, and prioritized actions.

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• Assigning review areas for a code change
• Merging duplicate findings from multiple reviewers
• Aligning severity ratings across review reports

Build a central vulnerability dashboard with DefectDojo to combine scanner findings, remove duplicates, and track remediation. Includes Docker deployment, automated scan imports from CI/CD pipelines, Jira ticket integration, and API queries for reporting metrics.

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• Combining findings from multiple security scanners
• Importing scan results automatically from CI/CD pipelines
• Connecting vulnerability findings to Jira tickets

Research prospective customers from public sources, add company and contact details, score their fit, and merge duplicate records. Organize the findings into prioritized lead lists with source information, using CSV, JSON, or Markdown tables.

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• Find companies matching an ideal customer profile
• Add funding and hiring details to lead records
• Score prospects to set sales priorities

Turn rough meeting notes, customer requests, and product ideas into structured backlog entries, a list of possible improvements. Capture who needs each idea and why, categorize it, and update matching entries with new evidence.

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• Capturing feature ideas after a meeting
• Recording requests from customers or sales
• Logging product gaps found in competitor research

Process SARIF files, a standard format for static analysis results, to filter alerts, remove duplicates, combine findings from multiple tools, and produce reports. It also supports comparing runs and integrating result checks into CI/CD pipelines.

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• Combine findings from multiple security scanners
• Filter alerts by severity and remove duplicates
• Compare earlier and current results for new issues

Add, extend, or review OpenAI Ads conversion tracking for purchases, signups, and other confirmed outcomes. The skill covers browser Pixel events, server-side Conversions API events, duplicate-event prevention, secret handling, and local verification with a setup report.

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• Add tracking for completed purchases and signups
• Send conversion events from a server
• Prevent duplicate counting across Pixel and CAPI

Paper Retrieval Agent - Multi-database paper fetching from Semantic Scholar, OpenAlex, arXiv Handles rate limiting, deduplication, and PDF URL extraction Use when: fetching papers, searching databases, paper retrieval Triggers: fetch papers, retrieve papers, database search, Semantic Scholar, OpenAlex, arXiv

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Smart backward and forward citation following via Semantic Scholar, with relevance filtering and deduplication

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