yogsoth-ai/de-anthropocentric-research-engine

list-undesirable-effects

List observable undesirable effects with evidence and severity as starting points for constraint/root-cause analysis.

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How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill list-undesirable-effects

Before you start

  • Installation does not include a model subscription, API allowance or permission to access your files.
  • Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

list-undesirable-effects

Purpose

List observable undesirable effects with evidence and severity as starting points for constraint/root-cause analysis.

Input contract

yaml
required: [system_observations, effect_evidence, severity_scale]
optional: [evidence, assumptions, prior_results]
constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]

Procedure

  1. Validate the typed inputs and state the decision this operation must support.
  2. Apply the declared operation to the named object; record intermediate values that affect interpretation.
  3. Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.

Output contract

yaml
produces: [list_undesirable_effects_result, evidence_trace, uncertainties]
delta_fields: [evidence_updates, uncertainties]

Quality gates

  • Inputs are named scientific objects with compatible schemas.
  • Every material result has a derivation or source reference.
  • Fixed statistical criteria remain exact where applicable: $\alpha$ 0.05 and power 0.8.

Failure and counterexamples

Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.

Provenance map

  • intermediate: experiment-execution/undesirable-effect-listing

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