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

warm-start

Skill2 starsChanged 6 months agoApache-2.0View on GitHub

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.

1 other project uses a version of this.

Adopted by

Most recently taken up by

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 64
  9. Distinctive:

    When enough information exists to move forward

    line 65

≈ “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

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

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 (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:

  1. A default flow as starting reference
  2. Available tactics with their purposes
  3. Simplification guidance for the warm-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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.