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Automation
Skill

/characterize-anomaly

Precisely characterize an anomaly by contrasting observation with expectation, quantifying deviation where possible, recording conditions, and excluding obvious/trivial explanations before generating hypotheses.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/characterize-anomaly

Context preview

The summary Claude sees to decide when to auto-load this skill.

Precisely characterize an anomaly by contrasting observation with expectation, quantifying deviation where possible, recording conditions, and excluding obvious/trivial explanations before generating hypotheses.

SKILL.md

characterize-anomaly.SKILL.md
name: characterize-anomaly
description: "Precisely characterize an anomaly by contrasting observation with expectation, quantifying deviation where possible, recording conditions, and excluding obvious/trivial explanations before generating hypotheses."

characterize-anomaly

Purpose

Characterize an observation that departs from an expected pattern and separate signal from measurement or protocol artifact.

Input contract

required: [observation, reference_pattern, condition_records]
optional: [uncertainty_estimates, replication_records, artifact_hypotheses]
constraints: [anomaly status requires a defined comparison and condition context]

Procedure

1. Define the expected pattern and comparison basis. 2. Quantify the departure with uncertainty and condition alignment. 3. Test plausible data, protocol, and mechanism explanations. 4. Classify the anomaly and identify discriminating follow-up evidence.

If the anomaly is reproducible and not explained by a recording artifact, consider `generate-competing-hypotheses` as the next tactic.

Output contract

produces: [anomaly_description, comparison_basis, explanation_set, discriminating_evidence]
delta_fields: [findings, evidence_updates, uncertainties, recommended_jumps]

Quality gates

  • The reference pattern and departure measure are explicit.
  • Artifact explanations are checked before causal interpretations.

Failure and counterexamples

Do not label a rare value anomalous without a comparison distribution or ignore changed measurement conditions.

Provenance map

  • `resolved: characterize-anomaly`
Read more
Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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