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

north-star-synthesis

Skill2 starsChanged 6 months agoApache-2.0View on GitHub

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.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    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:

    If self-review finds issues: return to specific tactic/SOP for targeted fix (not full re-run)

    line 27
  2. Distinctive:

    If passes: present North Star + ResearchBrief to user

    line 28

≈ “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

North Star Synthesis

Converge all accumulated context into North Star + ResearchBrief.

Available SOPs

SOPPurposeExecution
crystallize-north-starFormulate the North Star statement with userdialogue
generate-research-briefAggregate all context into structured ResearchBriefsubagent
final-validationSelf-review + present to user for confirmationdialogue

Methodology Guidance

  • crystallize-north-star checks: specific? ambitious? achievable?
  • generate-research-brief aggregates all context (ActorProfile, FieldPanorama, RankedCandidates, ObstacleReport, GoalTree, North Star, key references, key terms)
  • final-validation performs self-review before presenting:
    1. Placeholder scan: any TBD / empty sections?
    2. Internal consistency: do sections contradict?
    3. Completeness: critical information gaps?
    4. Ambiguity: statements interpretable two ways?
  • If self-review finds issues: return to specific tactic/SOP for targeted fix (not full re-run)
  • If passes: present North Star + ResearchBrief to user

Output (Tactic-Level Aggregation, Presented to User)

North Star (one sentence) + ResearchBrief (structured context document)

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.