Structure technical domains into layered, evidence-linked learning paths.
npx skills add https://github.com/aurora-atoms/lattice --skill learning-navigator
Please help me install this Agent Skill. Skill: learning-navigator Source: https://github.com/aurora-atoms/lattice/tree/main/skills/learning-navigator Command: npx skills add https://github.com/aurora-atoms/lattice --skill learning-navigator
This Skill solves the problem of information overload and fragmented understanding when approaching new technical domains or systems by providing a structured, evidence-based learning path.
Use the learning-navigator skill to generate a structured learning path for the target system based on the provided system map and learner goal.
High-intent search queries and answers about installing and using this skill.
To structure a complex technical domain into a learning path, this Skill breaks down system topology into logical layers, linking evidence to prioritize global orientation, critical control points, and failure recovery for actionable practice sequences.
Evidence-linked learning validation ensures technical training is grounded in actual system maps and verified system evidence rather than passive summaries, generating actionable practice sequences and teach-back rubrics for domain mastery.
Designing a curriculum for microservices architecture onboarding requires a bounded learning objective, learner baseline, and verified system evidence to generate layered learning paths covering golden paths, critical failure modes, and validation controls.
To generate actionable practice sequences for technical training, you need a bounded learning objective, a defined learner baseline, and verified system evidence to ensure the structured learning path accurately reflects system behavior.
Use a structured learning path for knowledge management when approaching new technical domains for onboarding, incident preparation, or technical domain mastery where understanding system behavior and failure paths is essential for task execution.
92Bilal26Generate YAML-ready learning objectives aligned with Bloom's taxonomy and CEFR levels.
24kchengYeDiagnose learner state and generate spaced-repetition study schedules with active-recall quizzes.
lciacciMaps and validates product journeys including error recovery paths with Playwright-based end-to-end tests.
InverterNetworkIntegrate Weights & Biases for experiment tracking and hyperparameter optimization.
