Results for “spike”
8 skillsCreate Technical Spike
Create time-boxed technical spike documents for researching and resolving critical development decisions before implementation.
36.2k
Spike
Runs focused experiments to validate feasibility of an idea, saving artifacts to .planning/spikes/ and supporting both idea and frontier modes.
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Spike
Runs throwaway experiments to validate feasibility, compare approaches, and surface unknowns before committing to a real build.
2
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Split
Decompose large user stories into thin, independently deliverable vertical slices using the SPIDR method (Spike, Path, Interface, Data, Rules).
7
Scope
Turns product ideas into structured specs for AI tools or stakeholder review, with quick feature specs and full project scopes.
54 · bundle
Strike Zone Analyst
A funnel and account-scoring diagnostic engine for any sales org. Connect a CRM and a product-analytics tool (plus optional enrichment, community, and meeting tools). Three modes. (1) FUNNEL DIAGNOSIS finds leaky conversion gates by channel with per-stage leakage, dollarized leverage points, and cohort velocity. (2) SPRINT PLANNING enriches qualified accounts into a ranked backlog with verified buying committees. (3) SCORING AUDIT finds where your scoring model is missing real ICPs. Trigger on 'funnel diagnosis', 'diagnose the funnel', 'where are we leaking', 'why is [channel] underperforming', 'conversion by channel', 'sprint planning', 'score these accounts', 'find missed ICPs', 'audit the scoring model', or any channel-level cohort-conversion, account-prioritization, or scoring-gap question.
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Brainstorm Experiments Existing
Design low-effort experiments to test product assumptions for an existing product, including prototypes, A/B tests, spikes, and other validation methods.
22.6k
Ml Training Recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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