detect-feedback-loop
SkillDev toolsLets 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.
No other account needed.
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
- Normalize directed relations, polarity, and delays.
- Enumerate simple cycles and identify reinforcing or balancing sign patterns.
- Compare loops with observations or intervention evidence where available.
- 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