HALOWERKAialignwerk
capability eval
Computes weighted pass rates and weighted scores from caller-labeled test outcomes, with deterministic category summaries. It does not run tests, validate labels, measure untested capabilities or establish deployment safety; the result is only as representative as the supplied evaluation set.
POST https://aialign.halowerk.com/v1/capability-evalEingaben
| Feld | Typ | Pflicht | Beschreibung |
|---|---|---|---|
| test_cases | array | Ja |
Vollständiges Eingabeschema anzeigen
{
"type": "object",
"required": [
"test_cases"
],
"additionalProperties": false,
"properties": {
"test_cases": {
"type": "array",
"minItems": 1,
"maxItems": 100000,
"items": {
"type": "object",
"required": [
"case_id",
"category",
"passed",
"score",
"weight"
],
"additionalProperties": false,
"properties": {
"case_id": {
"type": "string",
"minLength": 1,
"maxLength": 128
},
"category": {
"type": "string",
"minLength": 1,
"maxLength": 128
},
"passed": {
"type": "boolean"
},
"score": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"weight": {
"type": "number",
"minimum": 1e-06,
"maximum": 1000000
}
}
}
}
}
}Veröffentlichtes Eingabebeispiel
{
"test_cases": [
{
"case_id": "math-1",
"category": "reasoning",
"passed": true,
"score": 0.92,
"weight": 1
},
{
"case_id": "math-2",
"category": "reasoning",
"passed": false,
"score": 0.41,
"weight": 1
},
{
"case_id": "tool-1",
"category": "tool-use",
"passed": true,
"score": 0.88,
"weight": 2
}
]
}Ergebnis & Dokumentation
Veröffentlichtes Ergebnisschema anzeigen
{
"$ref": "https://aialign.halowerk.com/v1/capability-eval/schema#/outputSchema"
}Veröffentlichtes Ergebnisbeispiel öffnen
Maschinenlesbare Dokumentation dieses Hubs
Quelle dieser Seite: der veröffentlichte HALOWERK-Katalog. Stand: 2026-11-01, 15:19 UTC.
Weitere Endpunkte dieses Hubs
- Aialignwerk0,005 USDC
agent collusion
POSThttps://aialign.halowerk.com/v1/agent-collusionRequires the same agents in each supplied round, computes each pair’s observed action agreement, expected agreement from marginal action frequencies and chance-corrected kappa, then flags high positive agreement against a supplied threshold
- Aialignwerk0,004 USDC
bias audit
POSThttps://aialign.halowerk.com/v1/bias-auditCalculates each group positive-outcome rate, compares it with an explicit or automatically selected reference group, and reports rate differences and selection-rate ratios. Aggregate disparity metrics do not establish discrimination, fairne
- Aialignwerk0,003 USDC
confidence calib
POSThttps://aialign.halowerk.com/v1/confidence-calibCompares scalar confidence with binary correctness, calculates mean squared Brier loss, partitions confidence into caller-selected equal-width bins and reports expected and maximum calibration error. It does not validate labels, correct cla