hot-start
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/hot-startThe user already knows their direction. Your job is to structure it, not explore alternatives.
SKILL.md
.github/skills/hot-startView on GitHub ↗
--- name: hot-start description: Minimal crystallization strategy for users who already have a specific research topic or problem (e.g., "I want to improve CoT faithfulness in LLMs") and need structuring into a formal North Star. Heavily simplifies or skips exploration tactics, focusing on obstacle analysis, goal decomposition, and synthesis. Use when the user's first message reveals a specific, actionable research direction. --- # Hot Start Strategy The user already knows their direction. Your job is to structure it, not explore alternatives. ## Questioning Protocol All SOPs in this strategy follow these rules: - One question at a time — never overwhelm with multiple questions - Prefer multiple choice when possible — easier to answer - Always allow "unsure" / "TBD" as legitimate answers - Always ask WHY — not just "what do you want" but "why do you want it" - After user answers: confirm understanding before continuing - If user's answer reveals new information: immediately follow up - If user declines to answer (privacy): accept, note that downstream work becomes broader/more iterative ## Available Tactics | Tactic | Purpose | |--------|---------| | actor-profiling | Understand who the user is | | landscape-reconnaissance | Broad, shallow field exploration | | direction-narrowing | Focus within chosen field(s) | | obstacle-analysis | Identify and mitigate barriers | | goal-decomposition | KAOS-style AND/OR goal structuring | | north-star-synthesis | Converge into North Star + ResearchBrief | ## Default Flow (reference only) ``` actor-profiling (heavily simplified) → landscape-reconnaissance (skipped or minimal) → direction-narrowing (heavily simplified) → obstacle-analysis (simplified) → goal-decomposition → north-star-synthesis ``` This is a reference, not a mandate. The user already knows their direction. landscape-reconnaissance and direction-narrowing may only need a few searches for context — or may be skipped entirely if the user's topic is already well-defined. ## Simplification Guidance - **actor-profiling**: Focus only on resources and constraints relevant to the stated topic. Skip broad background exploration. - **landscape-reconnaissance**: Usually skippable. Only invoke if you need context about the field to properly structure the user's goal. - **direction-narrowing**: Usually skippable. The user has already narrowed. Only invoke if their stated topic is still too broad for a single North Star. - **obstacle-analysis**: Focus on the specific obstacles to their stated direction, not hypothetical alternatives. ## Iteration Points - From obstacle-analysis: may return to landscape-reconnaissance, direction-narrowing, or obstacle-analysis itself - From goal-decomposition: may return to landscape-reconnaissance, direction-narrowing, obstacle-analysis, or goal-decomposition itself ## How to Use This Strategy You are the general. This strategy gives you: 1. A default flow as starting reference 2. Available tactics with their purposes 3. Simplification guidance for the hot-start context 4. Iteration points where backtracking makes sense What you decide: - Whether to execute a tactic fully or partially - Whether to skip a tactic entirely - Whether to invoke individual SOPs directly (bypassing tactic framing) - When to iterate and where to return to - When enough information exists to move forward The only non-negotiable: the process ends with north-star-synthesis producing a North Star + ResearchBrief that the user confirms.