Swamp Adoption

Conduct a structured discovery interview to map the user's domain landscape. Guide the conversation through these phases: 1. SYSTEMS INVENTORY Ask: "What systems do you interact with daily? Include APIs, databases, SaaS platforms, CLI tools, and internal services." For each system, capture: name, type, and how they authenticate. 2. INTERACTION PATTERNS For each system, ask: "What do you DO with this system?" Capture verbs (read, write, deploy, monitor, rotate, audit), direct

Design a swamp extension based on the discovered landscape. Guide the conversation through these phases: 1. SCOPE CONFIRMATION Read the landscape resource. Present suggestedFirstExtension and reasoning. Ask: "Does this match your priority? If not, which system should we tackle first?" If a 'system' argument is provided, use that as the target. 2. METHOD DISCOVERY For the target system, review its interactions from the landscape. Map each verb to a method name (e.g., "read certi

Generate implementation files from an extension design. Reads the extensionDesign resource and produces: - manifest.yaml with proper CalVer version, labels, platforms - deno.json with standard task configuration - extensions/models/<name>/mod.ts with Zod schemas, resource definitions, method stubs - extensions/models/<name>/mod_test.ts with structure and argument validation tests The scaffold provides a working starting point that passes deno check and deno lint. Each generated file includes T

Suggest the next extension to build based on landscape analysis and the current design. Reads the landscape and the current extensionDesign resource. Filters out the system covered by the current design, then ranks remaining systems by: 1. Pain level (blocking > significant > minor > none) 2. Uses the highest pain across all interactions per system Logs advisory output with the recommendation and reasoning. Does not write any resource — purely advisory.