Open-source training material

Open synthetic datasets for multimodal AI training.

Engineered by Yatin Taneja, these datasets provide large-scale agentic tasks for reasoning, planning, clarification, defensive security, creative software operation, and professional media workflows.

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Synthetic AI training datasets
Small edge AI agent preparing web-search reasoning for a frontier model
Reasoning and retrieval planning / 260,293 rows

Edge Agent Reasoning WebSearch 260K

A 260K-row reasoning corpus for training small and edge-deployed agents to deconstruct complex requests, identify uncertainty, build verification plans, and generate expert web-search queries before a stronger frontier model executes the task.

  • 260,293total observations
  • 692.1Mdataset tokens across schema columns
  • 646.9Magentic reasoning tokens
  • 1.47Bgeneration tokens spent
  • 7Dcombinatorial prompt matrix
  • 1Bvalid permutation search space

Every row contains a dense 2,000 to 5,000-word reasoning trajectory that trains a model to pause, audit itself, and plan verification before execution.

View Dataset PageHugging Face
Creative professional AI agent operating multimedia workstations
Creative software actuation / 1,070,917 operations

Creative Professionals Agentic Tasks 1M

A 1.07M-operation synthetic task matrix for training multimodal agents to interpret high-level creative intent and translate it into software-native actions across design, 3D, video, audio, brand, photography, and engineering tools.

  • 1,070,917agentic command operations
  • 243.2Mfull-schema dataset tokens
  • 231.6Mprompt-only task tokens
  • 527.2Mgeneration tokens spent
  • 36professional archetypes
  • 17macro-categories

Each task is written as a first-person, technical execution command directed at an AI co-pilot embedded inside a specialized creative application.

View Dataset PageHugging Face
White-hat security agent monitoring threat intelligence dashboards
Defensive security reasoning / 596,295 prompts

White Hat Security Agent Prompts 600K

A practitioner-perspective corpus of 596K contextual security prompts that teaches models to reason from inside live defensive operations: incident response, red-team simulation, threat intelligence, post-mortems, CISO review, and AI safety.

  • 596,295security prompts
  • 131named threat categories
  • 76.8M+threat-scenario search space
  • 5impact severity tiers
  • 211average prompt words
  • 100%schema density target

Prompts are framed from active operational roles rather than textbook Q&A, including SOC analysts, CISOs, threat hunters, red-teamers, and trust-and-safety operators.

View Dataset PageHugging Face
AI media engineer operating audio and video production systems
Media production agents / 1,029,459 operations

Audio/Video Engineering Agentic Tasks 1M

A focused 1.03M-operation dataset for training agents inside DAW and NLE environments, built around mid-session troubleshooting, dense conversational instructions, timeline reasoning, routing changes, sonic repair, color matching, and edit execution.

  • 1,029,459audio/video task operations
  • 156Mprompt-only task tokens
  • 459.8Mgeneration tokens spent
  • 25professional archetypes
  • 2time-based software environments
  • 127.75average words per instruction

The dataset captures the messy, high-pressure language of professional audio engineers, composers, colorists, video editors, sound designers, and post-production operators mid-session.

View Dataset PageHugging Face
Adversarial AI safety agent reviewing intent-risk signals
Intent safety and clarification / 242,454 safety analyses

Adversarial Agent Intent Safety Analysis 240K

A 242,454-row adversarial safety corpus for command-and-control models, guardrail classifiers, and red-team agents. Each record separates a request's plausible surface interpretation from its deeper capability footprint, then produces an intent audit and authorization-focused clarifying questions.

  • 242,454adversarial safety records
  • 149.7M+exact cl100k_base tokens
  • 480.6Mgeneration and context tokens processed
  • 496atomic adversarial objectives
  • 126critical risk vectors
  • 808.5Mpossible threat permutations

Every row follows a tripartite safety structure: a naive surface interpretation, a forensic intent analysis that maps the requested specifications to their capability footprint, and targeted clarifying questions that test authorization, compliance, and ethical boundaries.

View Dataset PageHugging Face

Knowledge Hub

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