jarvis-at-skic
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37 lines
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37 lines
2.0 KiB
Plaintext
---
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id: skill-selection-precision
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slug: /measurement/skill-selection-precision
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title: Skill selection precision
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description: Definition, collection evidence and limitations for skill selection precision.
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---
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<div className="strategy-content">
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**Target operating model — recommended contracts, not installed collection.** All owner and cadence entries below are proposals to confirm before the pilot, not assigned responsibilities or approved schedules.
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**Prototype calculation — no live telemetry.** The synthetic example calculates this metric; it has no network collectors.
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{/* contract:start */}
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Correct activations among activations on an independently labeled evaluation corpus.
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| Contract | Definition |
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| --- | --- |
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| Numerator | True-positive activations for the requested workflow |
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| Denominator | True-positive plus false-positive activations |
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| Expression | N / D * 100 |
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| Aggregation | percent |
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| Unit | % |
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| Eligible population | All candidate/client labeled evaluation task-trials with known activation and relevance. |
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| Exclusions | Unlabeled/unknown activations excluded with coverage; no cherry-picked failed trials. |
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| Events and source | eval.selection; versioned labeled corpus and runner |
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| Owner / cadence | Data & AI / Each skill/client qualification |
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| Missing evidence | Unknown activation/relevance is unscorable; publish missing count and deny qualification if planned coverage incomplete. |
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| Gaming and limits | Choose easy labels or hide negative cases; freeze corpus and independently review labels. Descriptive signal, not causal AI productivity attribution. Source completeness, task mix and sampling limit inference; report uncertainty. Does not show execution success; precision depends on corpus prevalence. |
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{/* contract:end */}
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Read the shared [collection and safeguards](./collection-and-safeguards.mdx) before collecting data; see the [synthetic example and limits](./synthetic-example.mdx).
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Return to [Skills Measurement](./index.mdx).
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</div>
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