Technical details on how Skeleton plotting is implemented within CDendro 
Skeleton plot data is basically created according to the subroutine SKEL1 in John Philip Cropper: TreeRing skeleton plotting by computer in TreeRing Bulletin, Vol. 39, 1979 available at the The TreeRing Society, (Look for "TreeRing Bulletin and TreeRing Research", or try this link.) though there are indeed some small differences as described below. The high pass filter
The Cropper algorithm
Then narrow rings are selected as those being smaller than a limit calculated as
Wide rings are selected as those wider than "the local mean plus ANOTHER percentage of the standard deviation". (Cropper uses the same percentage as when selecting narrow rings!). The function then looks for the greatest preliminary skeleton value, amax. All skeleton values are then scaled by dividing them by amax, so their range becomes 0.01 to 1
"Normal rings" (neither wide nor narrow) are then given the skeleton value = 0.2
The skeleton data array now contains skeleton marker values in the range 0.01 to 1, values for wide rings (=0.6) and values for "normal" rings (=0.2).

Skeleton Chi2During correlation operations the sample data is compared with the reference data at every possible position, i.e. the sample is shifted along the reference while correlation values are calculated at each step. A chisquare ("Chi2") analysis (Burt and Barber 1996) is then also done when enabled by CDendro settings (see below).The Chi2 analysis is done by counting the occurencies of narrow ring hits narrow ring, wide ring hits wide ring, narrow hits wide and wide hits narrow. (I.e. the actual height of the skeleton markers are not considered here, the pure existence of a marker means "this ring is narrow".) "Normal" and "wide" rings are considered the same type of ring (i.e. wide). The Chi2 value is then calculated according to a formula where actual hits are compared to expected hits when there had been no correlation/dependence between the two curves. For a description of The Chi Square Statistic, see e.g. http://math.hws.edu/javamath/ryan/ChiSquare.html The CDendro code for this calculation is published at the end of this section. 
Skeleton correlationWhen enabled by CDendro settings (see picture below, "Skeleton Corr coeff") a normal correlation coefficient can be calculated on Skeleton data.The values for wide rings (=0.6) and for normal rings (=0.2) are set by experiment to give this coefficient as good "behaviour" as possible. 
To experiment somewhat with the parameters for creating skeleton data, install the settings as described below. 
First see that you enable Skeleton plotting through the menu command
Settings/Options for normalization and matching. See that the appropriate "Checktodisplay"checkbox is checked!

To allow for easy experimentation with parameters, see that the checkbox "Show skeleton parameters when plotting" is checked as shown above.

If you change the parameters of the "Skeleton algorithm" this will create more or less skeleton markers and also influence their heights.
The algorithm follows the description by Cropper, though with this implementation we can also adjust the proportion of thick rings  Cropper had the same proportion for both thin and thick rings.

The topmost black diagram is created by a living person. The other black diagrams are output from Croppers program when using various parameters. The coloured diagrams come from CDendro. They differ a little from Croppers, probably because I have used another prefiltering technique than Cropper. 
Public Function calcSkeletonChi2(ByRef refChain As ringChain, ByVal startIxOfSample As Integer, ByVal startIxOfREF As Integer, ByVal blockLng As Integer, ByRef becameBlockLng As Integer, ByRef actualOverlap As Integer) As Single On Error GoTo onError Dim k As Integer Dim overlapLng As Integer = 0 Dim skeletonOfSample, skeletonOfREF As Single Dim widesOrOrdinaryInRef As Integer = 0 'includes rings classified as normal or wide, i.e. skeletonvalue = iswide or skeletonvalue = isOrdinary Dim narrowsInRef As Integer = 0 ' 0 < skeletonvalue <= 1 Dim widesOrOrdinaryInSample As Integer = 0 Dim narrowsInSample As Integer = 0 ' 0 < skeletonvalue <= 1 Dim widesHitWide As Integer = 0 Dim narrowsHitNarrow As Integer = 0 Dim narrowsHitWide As Integer = 0 Dim widesHitNarrow As Integer = 0 If refChain Is Nothing Then Return 0 Dim isWide As Single = frmAlgoritm.p_skeletonWide Dim isOrdinary As Single = frmAlgoritm.p_skeletonOrdinary Dim isGap As Single = frmAlgoritm.p_skeletonNoData becameBlockLng = 0 For k = 0 To blockLng  1 If Me.skeletonLength(k + startIxOfSample, skeletonOfSample) AndAlso refChain.mySkeleton.skeletonLength(k + startIxOfREF, skeletonOfREF) Then If skeletonOfSample <> isGap And skeletonOfREF <> isGap Then 'no zero gap in either segment overlapLng += 1 If skeletonOfREF = isWide Or skeletonOfREF = isOrdinary Then widesOrOrdinaryInRef += 1 ElseIf skeletonOfREF > 0 Then narrowsInRef += 1 End If If skeletonOfSample = isWide Or skeletonOfSample = isOrdinary Then widesOrOrdinaryInSample += 1 ElseIf skeletonOfSample > 0 Then narrowsInSample += 1 End If If skeletonOfSample = isWide Or skeletonOfSample = isOrdinary Then If skeletonOfREF = isWide Or skeletonOfREF = isOrdinary Then widesHitWide += 1 ElseIf skeletonOfREF > 0 Then widesHitNarrow += 1 End If ElseIf skeletonOfSample > 0 Then If skeletonOfREF = isWide Or skeletonOfREF = isOrdinary Then narrowsHitWide += 1 ElseIf skeletonOfREF > 0 Then narrowsHitNarrow += 1 End If End If End If becameBlockLng += 1 'includes any gaps End If Next actualOverlap = overlapLng If overlapLng = 0 Then Return 0 Dim wideFreqInRef As Single = 0 Dim narrowFreqInRef As Single = 0 If widesOrOrdinaryInRef > 0 Then wideFreqInRef = widesOrOrdinaryInRef / overlapLng If narrowsInRef > 0 Then narrowFreqInRef = narrowsInRef / overlapLng Dim wideFreqInSample As Single = 0 Dim narrowFreqInSample As Single = 0 If widesOrOrdinaryInSample > 0 Then wideFreqInSample = widesOrOrdinaryInSample / overlapLng If narrowsInSample > 0 Then narrowFreqInSample = narrowsInSample / overlapLng Dim chi2 As Single = 0 Dim expected As Single expected = wideFreqInRef * wideFreqInSample * overlapLng If expected > 0 Then chi2 = (widesHitWide  expected) * (widesHitWide  expected) / expected End If expected = wideFreqInSample * (1  wideFreqInRef) * overlapLng If expected > 0 Then chi2 += (widesHitNarrow  expected) * (widesHitNarrow  expected) / expected End If expected = (1  wideFreqInSample) * wideFreqInRef * overlapLng If expected > 0 Then chi2 += (narrowsHitWide  expected) * (narrowsHitWide  expected) / expected End If expected = (1  wideFreqInSample) * (1  wideFreqInRef) * overlapLng If expected > 0 Then chi2 += (narrowsHitNarrow  expected) * (narrowsHitNarrow  expected) / expected End If Return chi2 onError: Return 0 End Function 