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Description
Sequelbox's Tachibana4 dataset tests agentic coding skills with real-world, challenging tasks across multiple programming languages. Synthetic prompts utilize varied personas, experience levels, and communication styles to maximize real-world flexibility. The dataset was last updated on May 7, 2026.
Use Cases
Benchmarking code generation models based on real-world, challenging agentic tasks
Training models for multi-language code generation based on diverse programming topics
Evaluating model adaptability based on prompts with varied personas and communication styles
Assessing code generation robustness based on tasks designed for different experience levels
Strengths
Focuses on real-world, challenging agentic coding tasks
Prompts utilize a variety of personas, experience levels, and communication styles
Covers a variety of programming languages and topics
Limitations
Description metadata is limited; actual data quality requires manual inspection after download
Column-level documentation is absent; field semantics must be inferred after download
Row count is unknown, which may limit suitability assessment
Provenance
Source
sequelbox
Collection Method
Likely contains synthetically generated prompts and tasks.
Freshness
Last updated 2026-05-07 01:09:32; freshness should be verified
License is unknown; terms of use must be verified before application.