detect-feedback-loop

SkillDev tools

Lets your agent find reinforcing and balancing feedback loops in a causal diagram and explain the evidence for each.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the detect-feedback-loop skill

About this skill

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

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/detect-feedback-loop/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
501
Forks
41
Last commit
Sep 2026
Advanced
Catalog kind
skill
Key
detect-feedback-loop
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine