Oscillator Hunt Sheet

Worksheet for identifying oscillators in a new domain.

Instructions

For each candidate cyclic process, fill in one row. Not all candidates will become oscillators -- filtering happens after.

Template

#	Process	Frequency (Hz)	Channel	Observable?	Data Source	Layer	Keep?
1			P / I / S	direct / inferred		micro / meso / macro	Y / N
2
3
4
5
6
7
8

Questions per Candidate

1. What cycles? Describe the repeating process. What goes up and comes back down?
2. What frequency? Rough estimate in Hz. Order of magnitude is enough.
3. Continuous or discrete? Continuous waveform (P), event stream (I), or state sequence (S)?
4. Observable or latent? Can you measure it directly from a sensor/API/log? Or must it be inferred from proxy signals?
5. Data source? Sensor name, API endpoint, log field, metric name.
6. Timescale tier? Does it belong to the fastest (micro), middle (meso), or slowest (macro) layer?

Filtering Criteria

Drop candidates that are:

• Not actually cyclic (monotonic trends, one-shot events)
• Unobservable with no viable proxy
• Redundant with another candidate (pick the more direct measurement)
• Outside the frequency range of interest (> 100x faster or slower than the control loop)

Example: Cloud Queue System

#	Process	Frequency	Channel	Observable?	Data Source	Layer	Keep?
1	Queue depth oscillation	0.5 Hz	P	direct	metrics API	micro	Y
2	Request arrival rate	10 Hz	I	direct	access log timestamps	micro	Y
3	Retry burst cycle	0.1 Hz	I	direct	retry log timestamps	micro	Y
4	P99 latency oscillation	0.05 Hz	P	direct	latency histogram	meso	Y
5	Error rate cycle	0.01 Hz	P	direct	error counter	macro	Y
6	Deploy cadence	0.001 Hz	S	direct	deploy events	macro	N (too slow)