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Automation
Skill

/detect-feedback-loop

Detect and characterize reinforcing/balancing feedback loops, participating variables, delays, and possible breakpoints.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill detect-feedback-loop --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/detect-feedback-loop

Context preview

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

Detect and characterize reinforcing/balancing feedback loops, participating variables, delays, and possible breakpoints.

SKILL.md

detect-feedback-loop.SKILL.md
name: detect-feedback-loop
description: "Detect and characterize reinforcing/balancing feedback loops, participating variables, delays, and possible breakpoints."

detect-feedback-loop

Purpose

Detect reinforcing or balancing feedback loops in a causal or process structure and state the evidence for each loop.

Input contract

required: [causal_graph, node_semantics, edge_polarity]
optional: [time_delays, observed_series, intervention_records]
constraints: [loop classification requires directed edges and polarity or transition evidence]

Procedure

1. Normalize directed relations, polarity, and delays. 2. Enumerate simple cycles and identify reinforcing or balancing sign patterns. 3. Compare loops with observations or intervention evidence where available. 4. Report loop boundaries, uncertain edges, and testable implications.

Output contract

produces: [feedback_loops, loop_classification, supporting_evidence, uncertain_edges, testable_implications]
delta_fields: [findings, evidence_updates, uncertainties, recommended_jumps]

Quality gates

  • Every loop lists its ordered edges and polarity basis.
  • Correlational cycles are not presented as causal loops without qualification.

Failure and counterexamples

Do not infer feedback from a static co-occurrence or omit time direction where it determines loop meaning.

Provenance map

  • `resolved: detect-feedback-loop`
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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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