landscape-reconnaissance
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/landscape-reconnaissanceExplores candidate research fields to identify opportunities
- Helps users discover available fields in cold-start or warm-start scenarios
- Uses web search and subagent workflows to gather and synthesize data
- Balances breadth of exploration with consideration of competition and novelty
- Presents a synthesized overview and asks user to select 1-2 fields of interest
SKILL.md
.github/skills/landscape-reconnaissanceView on GitHub ↗
--- name: landscape-reconnaissance description: Broad, shallow exploration of candidate research fields. Understand what's out there before narrowing. Use when the user needs to discover which fields are available to them — especially in cold-start and warm-start scenarios. --- # Landscape Reconnaissance Broad, shallow field exploration. Understand the landscape of possibilities before narrowing. ## Available SOPs | SOP | Purpose | Execution | |-----|---------|-----------| | generate-candidate-fields | Generate candidate fields from ActorProfile | subagent | | broad-web-search | Scan web for each candidate field | import: web-search | | landscape-synthesis | Synthesize search results into FieldPanorama | subagent | | present-and-ask | Present panorama to user, get field selection | dialogue | ## Methodology Guidance - If iteration is needed, expand breadth (more fields, more searches), never depth - Depth is direction-narrowing's job - You decide when enough information exists to synthesize ## Hard Constraints - `broad-web-search`: brave_web_search count=10 per call, at least 150 total results before synthesis - `landscape-synthesis`: Don't only chase niche/novel combinations. Must also consider direct frontal competition in hot fields. The ambition to tackle hard problems head-on must be present. ## Output (Tactic-Level Aggregation) `FieldPanorama[] + user's selected 1-2 fields of interest`