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/analyze-scaling-regime

Analyze how conclusions/performance change across scale and identify regime shifts, saturation, power-law/log-law behavior, or frontier transitions.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-scaling-regime --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/analyze-scaling-regime

Context preview

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

Analyze how conclusions/performance change across scale and identify regime shifts, saturation, power-law/log-law behavior, or frontier transitions.

SKILL.md

analyze-scaling-regime.SKILL.md
name: analyze-scaling-regime
description: "Analyze how conclusions/performance change across scale and identify regime shifts, saturation, power-law/log-law behavior, or frontier transitions."

analyze-scaling-regime

Purpose

Analyze how conclusions or performance change across scale and identify regime shifts, saturation, scaling-law behavior, or frontier transitions.

Input contract

required: [scale_variable, outcome_series, observation_context]
optional: [candidate_scaling_laws, uncertainty_model, suspected_breakpoints]
constraints: [scale units and outcome direction must be explicit; observations remain ordered]

Procedure

1. Normalize scale and outcome definitions while retaining original units. 2. Plot or tabulate local behavior and fit only caller-authorized within-regime models. 3. Locate qualitative shifts, saturation, or frontier transitions and test their stability. 4. Report regime boundaries, mechanism hypotheses, and extrapolation limits.

Output contract

produces: [regime_map, breakpoint_candidates, scaling_diagnostics, extrapolation_limits]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]

Quality gates

  • Each claimed regime has observations on both sides or is marked extrapolative.
  • Breakpoints include uncertainty or sensitivity information.
  • Power-law/log-law labels are supported by fit diagnostics, not visual slope alone.

Parameterization

Caller supplies scale axis, outcome schema, candidate laws, breakpoint rule, fit diagnostics, and acceptable extrapolation distance.

Failure and counterexamples

Reject a regime claim based on a single point or a scale change confounded with protocol change.

Provenance map

  • resolved: scaling-frontier
  • concept: deep-insight/scaling-analysis

Preserved source criteria ledger

| source | criterion | |---|---| | scaling-frontier | Analyze behavior across scales, detect regime changes, and identify capacity limits and mechanisms. |

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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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Repo: yogsoth-ai/de-anthropocentric-research-engine