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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-troubleshoot-llm.mdreferences/

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

Frame-Problem to Troubleshoot Handoff

Thinking Trail

  • Considered: {what approaches were weighed during framing}
  • Rejected: {why complex/chaotic didn't fit — governing constraints exist}
  • Surprised by: {degradation signals — errors, declining quality}
  • Triangulation: T1={Keogh level}, T2={predictability}, T3={disassembly} — {convergence}
  • Models used: Cynefin triangulation (Keogh + Predictability + Disassembly)
  • Constraints discovered: {governing constraints — known system, degrading behavior}

Decisions

  1. Domain: Complicated (governing constraints, degraded system)
  2. Route: troubleshoot → stabilize → re-frame
  3. Rationale: {system was working, now failing — diagnostic expertise applies}

Actions Taken

  • Classified via Q1: governing constraints identified
  • Q1.1: degraded (quality declining, system failing)
  • Scale determined: {boulder|pebble}

Output

🎯 Complicated → Analyze → /troubleshoot → stabilize → re-frame | OpenSpec: no | Scale: {scale}

Domain Transition

From: Confused → To: Complicated Constraint shift: unknown → governing. System has known good state — it's degrading from that state. Diagnostic expertise applies.

For /troubleshoot

  • Symptom: {what's broken or degrading}
  • Known good state: {what it looked like when working}
  • Recent changes: {what changed before degradation started}
  • Diagnostic approach: Search-first — check learnings database, then systematic diagnosis
  • Do NOT: Redesign — stabilize first, then re-frame if deeper changes needed

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