Seven Powers Hamilton Helmer Moat Taxonomy

Caller independently documented evidence for named moat criteria; market positioning or power not established from score. Independently declared positive requirements, overlapping/unknown classes retained. Caller independently verifies policy, evidence, dates and applicability; not legal/compliance advice or causal business-success prediction.

Description

A moat taxonomy groups independently annotated business characteristics.

A moat taxonomy groups independently annotated business characteristics.^[1]

The rubric supplied here is a caller classification rather than an installed authoritative list of Seven Powers. Use this tool to compare independently annotated features with the stated candidate rules.

When to use Seven Powers Hamilton Helmer Moat Taxonomy

Use this tool to compare independently annotated features with the stated candidate rules. Establish the business period, event definitions, jurisdiction or agreement edition, and independently documented evidence or comparison rubric.

How to use Seven Powers Hamilton Helmer Moat Taxonomy and understand its results

Establish the business period, event definitions, jurisdiction or agreement edition, and independently documented evidence or comparison rubric.
Enter callerSevenPowersClassIds, callerSevenPowersRequirements, observedSevenPowersEvidence, knownSevenPowersFlags, using the field meanings and rules below, then run the tool.
Read compatibleCallerClasses, fullyWitnessedCallerClasses. Interpret the fields using their units, signs and evidence conventions below.
If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.

Input field	Meaning	Value type	Requirement
callerSevenPowersClassIds	Caller seven powers class ids	list of string	Required
callerSevenPowersRequirements	Caller seven powers requirements	list of list of number	Required
observedSevenPowersEvidence	Observed seven powers evidence	list of number	Required
knownSevenPowersFlags	Known seven powers flags; binary 1=known, 0=unknown, in the order described below	list of number	Required

Supply nonempty unique class IDs and a rectangular binary requirement matrix with one row per class and one column per observed feature. Every class must require at least one positive feature. Supply aligned binary observation and known-flag lists. A requirement 0 ignores its column; a requirement 1 permits a class only if the feature is unknown or observed present. Fully witnessed classes require every required feature to be known present. Return all compatible and fully witnessed caller classes in caller order; unknown evidence can leave several candidates, and known absent required evidence excludes a class. The rubric is supplied rather than installed.

compatible={class:each required column is unknown or present}; witnessed={class:each required column is known present}

Use the exact units, boundary rules, ordering and missing-evidence conventions above.

Output field	Meaning	Value type
compatibleCallerClasses	Every supplied class whose required features are unknown or known present	list of string
fullyWitnessedCallerClasses	Every supplied class with all required features known present	list of string

json

{
  "input": {
    "callerSevenPowersClassIds": [
      "a",
      "b"
    ],
    "callerSevenPowersRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedSevenPowersEvidence": [
      1,
      0
    ],
    "knownSevenPowersFlags": [
      1,
      0
    ]
  },
  "output": {
    "compatibleCallerClasses": [
      "a",
      "b"
    ],
    "fullyWitnessedCallerClasses": [
      "a"
    ]
  }
}

Limitations and assumptions

Features and criteria are independent annotations. Compatible candidates and retained unknown evidence do not establish an identity, interpretation, overall pass or permission.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.

Alternative or Complementary methods

Use North Star Metric Selection Tree for this related task: A north-star metric should represent an independently chosen business objective.

Use North Star Guardrail Metrics Counterbalance Pairing when its separate inputs and assumptions fit the question: A north-star improvement can be considered alongside guardrail observations.

References

Seven Powers Hamilton Helmer Moat Taxonomy — tool.io

Similar or alternative tools

North Star Metric Selection Tree
A north-star metric should represent an independently chosen business objective.
North Star Guardrail Metrics Counterbalance Pairing
A north-star improvement can be considered alongside guardrail observations.