abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Precisely characterize an anomaly by contrasting observation with expectation, quantifying deviation where possible, recording conditions, and excluding obvious/trivial explanations before generating hypotheses.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill characterize-anomaly -a windsurf -y
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 an observation that departs from an expected pattern and separate signal from measurement or protocol artifact.
required: [observation, reference_pattern, condition_records] optional: [uncertainty_estimates, replication_records, artifact_hypotheses] constraints: [anomaly status requires a defined comparison and condition context]
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.
produces: [anomaly_description, comparison_basis, explanation_set, discriminating_evidence] delta_fields: [findings, evidence_updates, uncertainties, recommended_jumps]
Do not label a rare value anomalous without a comparison distribution or ignore changed measurement conditions.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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