SystemsThinking

• Archetypes.mddoc · 3,084 bytes
• Foundation.mddoc · 887 bytes
• Intents/CausalLoop.mddoc · 2,979 bytes
• Intents/ConceptMap.mddoc · 2,317 bytes
• Intents/FindArchetype.mddoc · 1,755 bytes
• Intents/FindLeverage.mddoc · 2,325 bytes
• Intents/Iceberg.mddoc · 2,852 bytes
• LeveragePoints.mddoc · 1,227 bytes
• SKILL.mdcore · 6,472 bytes
• SKILL.mdcore · 6,472 bytes

# SystemsThinking Skill

SystemsThinking explains recurring behavior through the structure that generates it: iceberg layers, causal loops, archetypes, leverage points and concept maps. Most real runs point it at a recurring problem in the principal's own harness (a metric drifting the wrong way, a library or hook set that keeps growing, a signal that died without anyone noticing), usually chaining CausalLoop, FindArchetype and FindLeverage, with the measured facts already in the request; a few take a real-world system such as a city's water supply or a team's on-call rotation. Many ask for a brief inline pass whose output feeds a larger report. During an acute incident, stabilize first and analyze afterward; incident causal chains belong to RootCauseAnalysis. Art renders the diagrams, and BeCreative generates candidates at a chosen leverage level in one pass, and HillClimb Ideate evolves them over several rounds.

## Ideal State

- Every analysis explains the behavior through the structure that generates it (loops, delays, incentives, flows, beliefs), never through the last event or a person.
- The analysis starts from the facts and paths the request supplies and reads those paths rather than searching for them again. A supplied fact the conclusion rests on is checked at its source, a caller's hypothesis is never taken as fact, and a missing loop link or measured fact is read or measured at its source rather than inferred; any link still inferred is marked. Any search names the paths it covers, never the whole home tree or ~/Projects.
- Evidence and inferred mechanism are told apart in every output, and every recommended intervention is traced through at least one round of its own feedback, with its second-order effects named, before it ships.
- A recommendation is the highest-leverage change that is actually feasible, with a concrete first move and a date to re-evaluate it.
- The recommendation names the specific file, hook, rule, metric or mechanism to change, and carries the input's measured facts (counts, rates, dates) through to it, so its effect can later be checked against the same numbers.
- The actors are whatever actually acts in the system. In a solo operator's AI harness that is hooks, models, doctrine files, scheduled jobs and the principal; an organization, politics or culture is never invented to fit the frame.

## Intent Routing