feedback-loop-detection
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure.
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The file
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Common · estimate
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- Distinctive: line 20
Loops are normal: Don't assume acyclicity. Real causal systems almost always have feedback.
≈ “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.
Files
Feedback Loop Detection
Identify circular causation in the causal graph. Most real systems have feedback loops — they must be documented explicitly.
Available SOPs
- causal-chain-query — trace paths looking for cycles
- loop-documentation — document identified loops
- mechanism-edge-creation — create edges that close loops
Guiding Principles
- Loops are normal. Don't assume acyclicity. Real causal systems almost always have feedback.
- Classify loops. Reinforcing (positive feedback, amplification) vs balancing (negative feedback, homeostasis).
- Time delays matter. A loop with a 10-year delay behaves differently from one with a 10ms delay.
- Dominant loops. In systems with multiple loops, identify which loop dominates behavior.
- Break points. Identify where interventions could break harmful loops.
Minimum Yield
<HARD-GATE>
≥1 loop identified and classified (or explicit confirmation that no loops exist in the current subgraph) per invocation.
</HARD-GATE>Does it work?
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