HALOWERK aialignwerk

HALOWERK aialignwerk. Bezahlung über x402 in USDC auf Base Mainnet.

Skills

• Measures lexical evidence coverage for caller-supplied claims.
  Tokenizes each claim and its supplied evidence, removes a small declared English stop-word list, and reports the fraction of unique claim tokens present in evidence. Coverage below a supplied threshold is flagged. This is lexical grounding screening only: overlap does not prove truth, low overlap does not prove hallucination, and the service does not retrieve or validate evidence.
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• Measures whether a response becomes more lexically aligned with a supplied leading position.
  Compares term-frequency cosine similarity between a leading user position and paired independent/conditioned responses, then reports the positive similarity shift. It is a reproducible surface-form signal, not proof of agreement, truthfulness, motive or sycophancy; paraphrases and legitimate corrections can evade or trigger it.
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• Quantifies change between supplied initial and current goal-weight vectors.
  Aligns caller-labeled dimensions, computes cosine similarity and a normalized L1 difference, and flags drift against a supplied cosine threshold. It does not infer goals from behavior, decide which goal is correct or detect deception; results depend entirely on the supplied vector representation.
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• Screens a supplied prompt for common instruction-bypass and secret-request patterns.
  Applies a small fixed set of defensive regular-expression categories and returns category names, a bounded risk score and a review recommendation. It does not execute, transform or forward the prompt. Pattern matching is incomplete and can produce false positives; it should be one signal in layered controls, not the sole access decision.
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• Compares supplied agent resource usage with explicit per-agent budgets.
  Divides observed CPU, memory-time, network, tool-call and wall-time usage by caller-supplied budgets, reports exceeded dimensions and ranks observations by the maximum ratio. It does not collect telemetry, infer intent, identify inefficient algorithms or terminate an agent.
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• Computes aggregate outcome-rate disparities across caller-supplied groups.
  Calculates 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, fairness, causation or legal compliance and can conceal intersectional or sampling effects.
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• Aggregates supplied capability-test outcomes by category and overall.
  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.
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• Measures pairwise action coordination using observed agreement and Cohen-style kappa.
  Requires 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. Coordination can be benign or task-driven; this metric does not prove communication, intent or collusion.
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• Redacts common credential and personal-data patterns from supplied memory text.
  Replaces private-key blocks, credential assignments, email addresses, Luhn-valid payment-card candidates, IPv4 addresses and phone-like digit strings, returning only sanitized text and per-category counts. The request archive stores only input field names and the result archive stores only this method via the endpoint factory. Pattern redaction is incomplete: it cannot identify every secret, name, address or contextual identifier and must not be treated as irreversible anonymization.
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• Calculates Brier score and binned calibration error for supplied predictions.
  Compares 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 class imbalance or prove calibration beyond the supplied sample.
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How to call

https://aialign.halowerk.com

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