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pantheon-ai/frame-problem

Classify a problem using Cynefin triangulation before acting — routes to the right skill chain (investigate, brainstorm, probe, troubleshoot).

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frame-problem-to-investigate-llm.mdreferences/

domain:
complicated
verb:
analyze
constraint-type:
governing
problem:
{problem statement from classification}
scale:
{boulder|pebble}

Frame-Problem to Investigate Handoff

Thinking Trail

  • Considered: {what approaches were weighed during framing}
  • Rejected: {why complex/chaotic didn't fit — governing constraints exist}
  • Surprised by: {signals that this is evolving, not degraded}
  • Triangulation: T1={Keogh level}, T2={predictability}, T3={disassembly} — {convergence}
  • Models used: Cynefin triangulation (Keogh + Predictability + Disassembly)
  • Constraints discovered: {governing constraints — experts/best practices apply, system evolving}

Decisions

  1. Domain: Complicated (governing constraints, evolving system)
  2. Route: investigate → openspec-plan
  3. Rationale: {expertise can solve this — needs deep analysis, not experimentation}

Actions Taken

  • Classified via Q1: governing constraints identified
  • Q1.1: evolving (capacity growing, system improving)
  • Scale determined: {boulder|pebble}

Output

🎯 Complicated → Analyze → /investigate → /openspec-plan | OpenSpec: {yes if boulder} | Scale: {scale}

Domain Transition

From: Confused → To: Complicated Constraint shift: unknown → governing. Best practices exist, experts can solve this. System is evolving — growing capacity, not failing.

For /investigate

  • Problem focus: {specific technical question to investigate}
  • Known constraints: {governing constraints — what rules/patterns apply}
  • Expertise needed: {what domain knowledge is relevant}
  • Expected output: Analysis + recommendation → feeds openspec-plan if boulder
  • Do NOT: Experiment — this domain has known good practices. Analyze, don't probe.

Accumulated Context

Token guidance: target 300 tokens inline. For depth, use references — point to thinking artifact files rather than embedding full content. Soft cap: 600 tokens inline per handoff. If you need more, move detail to a knowledge file and reference it. Accumulated cap: 800 tokens across a chain — compress to 200 at cap (keep: decisions, constraints, rejected paths). References to thinking files do NOT count toward the cap.

references

frame-problem-to-brainstorm-llm.md

frame-problem-to-experiment-llm.md

frame-problem-to-investigate-llm.md

frame-problem-to-probe-liminal-llm.md

frame-problem-to-probe-llm.md

frame-problem-to-troubleshoot-llm.md

SKILL.md

tile.json