 OK. THE TECH-RECON SKILL DOES NOT WORK. 

Here are the things that are wrong with it: 
1. The entities that we are tracking in the database are not important - the premise of the design work identified some concepts as important but they do not serve the needs of the application. 
2. The actual goal of the skill is not clear. The dashboard design MUST provide a comparative insight into the organization, design, implementation, and performance of surveyed technology IN RELATION TO the goal that we are attempting to pursue or investigate. For each investigation (represented by a collection), we need to carefully understand exactly what success looks like. Thus a key part of Alhazen skill building should be identifying the goal of the curation task in order to build a dashboard that helps understand and tackle that goal. 
3. There is not nearly enough emphasis on detailed ingestion of all relevant material from the resources in question. The sensemaking is abbreviated and terse. It cannot be understood in isolation. It needs to be exhaustive and all relevant material should be copied to the cache for processing by Claude Code's native file based tools. Lean into the use of Claude's native abilities here. 
4. So - not sure how to do this, but we need to somehow defer the structure of the analysis so that we can be flexible based on what the goals of the task are. Currently, the system is just completely useless without a far more sophisticated and flexible representation around the final output. It might be necessary to store analysis + visualization code in the database that could be run in the dashboard on demand.  
 
We are going to start a complete reimagining / recreation of the techrecon skill as 'tech-recon'. This requires us to investigate and think carefully about the best architecture and approach for this process as an exemplar curation method.

1. We need an extensive interview with the end user before building an investigation. This permits us to write up a detailed specification of what the dashboard is designed to do in terms of user experience. Not sure how to represent this functionality in a way that will work, but that is what we need the system to do.   
2. I want to focus on the discovery and ingestion phase so that we do extensive research online about the topic at hand. That means for this skill, we should identify the primary knowledge sources and our methods for capturing them, preferably in the cache, with the TypeDB system tracking their URLs and file location. 
3. We need EXTENSIVE SENSEMAKING over ingested data with lots of evidence and understanding of resources in intelligent ways. This should be somewhat open ended without a predefined schema, so that the subsequent analysis can be well-defined and iterative to search for multiple patterns and methods.
4. Analysis and synthesis will be different for each tech investigation. Come up with a good plan to be able to build analysis workflows and javascript visualizations (using https://observablehq.com/plot/what-is-plot) dynamically in the dashboard.  

Make notes on the process in a separate MD file that I can read and we'll update the skill-builder prompts after we're complete. (so keep track of what the skill builder is currently doing). I want to think carefully about how we split the task up and how we might split the prompt between subskills or agents. 
This is going to be an iterative process and we'll take multiple runs at it. 