convention-learner
Analyzes a codebase to learn naming conventions, folder structure, test organization, and coding style. Uses those patterns to support consistent project-specific code.
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Find your next superpower If you are an agent, refer to our llms.txt for full access.Analyzes a codebase to learn naming conventions, folder structure, test organization, and coding style. Uses those patterns to support consistent project-specific code.
Supports overlap, nearest, merge, coverage, complement, and subtraction operations on genomic intervals. Handles BED, VCF, BAM, and GFF data with streaming and cloud-native workflows.
Covers Qiskit 2.x circuits and operators, V2 Sampler and Estimator primitives, target-aware transpilation, and local or noisy simulation. Supports IBM QPU execution, Runtime sessions and batches, error mitigation, and ecosystem packages.
Focuses on rigorous marketing copy for landing pages and emails. Requires brief confirmation and follows strict rules against fabrication.
Covers lakehouse setup, artifact registration, search, queries, and lineage tracking. Supports ontology-backed annotations with Bionty, collections, branches, storage, and workflow integrations.
Uses SpikeInterface to load SpikeGLX, Open Ephys, and NWB data and perform drift correction and spike sorting. Covers Kilosort4, CPU sorting, quality metrics, and threshold-based, model-based, or AI-assisted unit review.
Guides subscription plan changes with authentication, ownership checks, and preview-before-commit behavior. Covers prorationBillingMode, items-array replacement semantics, and on_payment_failure handling.
Searches songs, albums, playlists, music videos, and artists on QQ Music. Provides daily recommendations, charts, AI-powered playlists, and personalized listening reports with music insights.
Runs Ruff with optional scope and rule overrides, applying safe and unsafe autofixes iteratively. Reviews changes and addresses remaining findings through targeted edits or user decisions.
Tracks and evaluates social media campaign performance across platforms. Calculates engagement rates and ROI, compares platform metrics, and benchmarks results against industry standards.
Provides Swift code style conventions for clean, readable code. Covers consistent formatting, naming, organization, and idiomatic patterns.
Uses profiling, IR comparison, benchmarking, and targeted tuning to improve GPU kernel performance. Covers tile sizes, occupancy, autotuning, TMA, latency hints, persistent scheduling, num_ctas, flush_to_zero, and IR-level debugging.
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