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yogsoth-aiyogsoth-ai/north-star-crystallization/skills/hot-start/SKILL.md

hot-start

Skill2 starsChanged 6 months agoApache-2.0View on GitHub

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.

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 9 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈9

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    All SOPs in this strategy follow these rules:

    line 12
  2. Distinctive:

    Prefer multiple choice when possible — easier to answer

    line 15
  3. Distinctive:

    Always allow "unsure" / "TBD" as legitimate answers

    line 16
  4. Distinctive:

    Always ask WHY — not just "what do you want" but "why do you want it"

    line 17
  5. Distinctive:

    After user answers: confirm understanding before continuing

    line 18
  6. Distinctive:

    If user's answer reveals new information: immediately follow up

    line 19
  7. Distinctive:

    If user declines to answer (privacy): accept, note that downstream work becomes broader/more iterative

    line 20
  8. Distinctive:

    When to iterate and where to return to

    line 67
  9. Distinctive:

    When enough information exists to move forward

    line 68

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

TacticPurpose
actor-profilingUnderstand who the user is
landscape-reconnaissanceBroad, shallow field exploration
direction-narrowingFocus within chosen field(s)
obstacle-analysisIdentify and mitigate barriers
goal-decompositionKAOS-style AND/OR goal structuring
north-star-synthesisConverge 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.

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