warm-start

Research

Simplified crystallization strategy for users who have a general research direction (e.g., "I'm interested in LLM reasoning") but lack specificity. Simplifies actor profiling and landscape reconnaissance, then proceeds through direction narrowing, obstacle analysis, goal decomposition, and north-star synthesis. Use when the user's first message reveals a general area but not a specific problem.

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/warm-start/SKILL.md

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Warm Start Strategy

The user has a general direction — they know the field or area but not the specific problem.

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 (simplified) → landscape-reconnaissance (simplified or skipped)
→ direction-narrowing → obstacle-analysis → goal-decomposition → north-star-synthesis

This is a reference, not a mandate. How to simplify, how much to simplify, whether to skip entirely — these are your decisions. This strategy suggests simplification as the default posture, but you judge based on what the user's initial message reveals.

Simplification Guidance

actor-profiling: The user's stated direction already reveals partial context. Focus on resources, constraints, and intentionality rather than exhaustive background exploration.
landscape-reconnaissance: The user already knows the general field. You may skip broad scanning and go directly to direction-narrowing, or do a targeted scan of the specific sub-area they mentioned.

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:

A default flow as starting reference
Available tactics with their purposes
Simplification guidance for the warm-start context
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.

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
actor-profiling	Understand who the user is — background, resources, constraints, and deep motivations. Produces an ActorProfile that informs all downstream decisions. Use this tactic at the start of any crystallization process to build a model of the user's capabilities, limitations, and intent.
direction-narrowing	Focus within the user's chosen field(s). Identify specific sub-directions through deep paper and web research, then present ranked candidates. Use after landscape-reconnaissance has identified fields of interest.
goal-decomposition	Structure the user's chosen direction into a formal goal tree using KAOS-style AND/OR decomposition. Validate feasibility against ActorProfile and ObstacleReport. Use after obstacle-analysis confirms the direction is viable.
landscape-reconnaissance	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.
north-star-synthesis	Converge all accumulated context into a crystallized North Star statement and structured ResearchBrief. Performs self-review before presenting to user. Use as the final tactic in any start mode — this is where everything comes together.
obstacle-analysis	Identify what blocks the user from pursuing their chosen direction, assess severity, propose mitigations with search-validated evidence, and get user acceptance. Use after direction-narrowing has identified a specific direction.