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Halowerk Anti-Cheat Scan
x402
Halowerk Anti-Cheat Scan is a paid API for AI agents from gaming.halowerk.com, paid per call via x402, $0.004/call, status unknown (last checked 2026-09-14).
Evaluates player telemetry samples against configurable thresholds for reaction time, actions-per-second, movement speed, and impossible events to produce review-signal flags.
Price / call$0.004
Success—
StatusUnknown
How this service works
Evaluates caller-supplied telemetry samples against declared reaction-time, actions-per-second, movement-speed, and impossible-event rules. Flags are review signals only; the service does not identify software, inspect a device, establish intent, or justify an automatic ban.
Output
Returns per-sample review flags indicating which telemetry entries tripped one or more of the configured rules (reaction time, APS, speed ratio, impossible event). Results are signals for human review — no bans, no device identification, no intent determination are made.
Example use cases
Post-match bot detection sweep
“Run an anti-cheat scan on the 500 telemetry samples from last night's matches — flag samples where reaction time is below 100ms, APS goes over 12, and movement speed exceeds permitted by more than 10%; use all 4 rules.”
Live tournament integrity check
“Scan these 50 telemetry samples from the current tournament round for cheating signals — minimum human reaction is 80ms, max actions-per-second is 10, speed tolerance is 2%, and flag any impossible events; apply all 4 rules.”
Ranked ladder review queue
“I have 1000 gameplay samples from flagged ranked accounts — run them through anti-cheat with a 150ms minimum reaction threshold, 8 APS maximum, 5% speed tolerance, and all 4 review rules enabled so my moderation team can prioritize the worst offenders.”
Technical details
Endpoint
POSThttps://gaming.halowerk.com/v1/anti-cheat-scanWhen to prefer
Choose this endpoint when you need a lightweight, stateless telemetry evaluation step that produces structured review signals without requiring device access or behavioral AI models. Ideal for pipeline stages that pre-filter large batches of gameplay samples before manual moderation or heavier ML-based analysis. Best when you control the telemetry collection and simply need threshold-based rule evaluation at scale.
Side effects
- Empty or malformed samples array returns validation error
- sample_id exceeding 128 characters is rejected
- reaction_ms or speed values outside permitted numeric range cause schema rejection
- review_rule_count outside 1-4 range is rejected
- Batch exceeding 50,000 samples is rejected
- Missing required per-sample fields (reaction_ms, actions_per_second, etc.) cause item-level errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| samples | array | |
| review_rule_count | integer | |
| speed_tolerance_percent | number | |
| minimum_human_reaction_ms | number | |
| maximum_actions_per_second | number |
Tags
x402
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