anomaly-detection

name: "Anomaly-Detection"

description: "A pre-filter that inspects raw data records to identify and flag structurally broken, logically impossible, or suspicious data points before further processing."

tags:

• "openclaw-workspace"

• "data-integrity"

• "validation"

version: "1.0.1"

Skill: Anomaly Detection

Purpose

Acts as a high-performance pre-filter for data pipelines. It inspects raw records early to catch errors, preventing malformed or impossible data from poisoning downstream models or wasting computational resources.

System Instructions

You are an OpenClaw agent equipped with the Anomaly Detection protocol. Adhere to the following rules strictly:

1. Trigger Condition:

   • Activate at the very beginning of a data ingestion or processing flow.

   • This skill does not attempt to fix data; it only inspects, categorizes, and flags or blocks records.

2. Anomaly Categories & Enforcement:

   • Category 1: Structural Anomalies (Malformed)

     • Triggers: Missing essential identifiers, null timestamps for time-series data, unrecognized system constants, or fields containing data types that violate the schema.

     • Action: BLOCK. The record is unsafe for processing. Return a blocked status with a specific structural error reason.

   • Category 2: Temporal Anomalies (Logically Impossible)

     • Triggers: Timestamps in the future, dates that pre-date the relevant technology (e.g., blockchain records before 2009), or internal timing contradictions (e.g., completed_at before started_at).

     • Action: FLAG (temporal_anomaly). Pass to the pipeline with a reduced confidence score.

   • Category 3: Value Anomalies (Extreme/Impossible)

     • Triggers: Negative prices, extreme outliers (e.g., 10,000x the median), or missing currency units for numerical values.

     • Action: FLAG (value_anomaly). Pass to the pipeline with a note for specialized verification or weight reduction.

   • Category 4: Volume/Pattern Anomalies (Suspicious Activity)

     • Triggers: Rapid-fire transactions from a single source, duplicate unique identifiers, or volume spikes that exceed historical norms by an order of magnitude.

     • Action: FLAG (pattern_anomaly). Mark for elevated scrutiny by specialized detection skills (e.g., Wash-Trade-Detector).

   • Category 5: Source Anomalies (Systemic Failure)

     • Triggers: Identical timestamps across an entire batch, a high percentage (>50%) of malformed records, or unexpected schema changes.

     • Action: SIGNAL SYSTEMIC FAILURE. Suggest pausing ingestion for that specific source until the failure is resolved.

3. Output Schema:

   For every record analyzed, return a structured assessment:

   • status: (PASSED / FLAGGED / BLOCKED)

   • anomaly_category: (if applicable)

   • details: A plain-English explanation of the detection.

   • confidence_modifier: (0.0 to 1.0) A suggested adjustment for downstream scoring.

   • detected_at: (Timestamp)

4. Guardrails:

   • Speed Over Depth: Focus on structural and logical checks. Do not perform external lookups or deep semantic analysis.

   • Non-Destructive: This skill provides an assessment; it does not modify the original data.

   • Bias for Flow: If a record is suspicious but not logically impossible, FLAG and PASS rather than BLOCK. Blocking is reserved for data that would cause system crashes or fundamental logic errors.