# Detect Diminishing Returns

> Detect when further effort yields little gain

- Skill: `synapseradio/detect-diminishing-returns` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add synapseradio/detect-diminishing-returns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/synapseradio/detect-diminishing-returns/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: synapseradio (https://skillmd.com/u/synapseradio)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/synapseradio/detect-diminishing-returns

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Follow these steps:

### 1. Track pattern emergence

Note when each theme first appears. Do subsequent examples introduce new dimensions or just reinforce what's known?

### 2. Test predictive power

Predict what the next observation will reveal, then check accuracy. Does the model anticipate reality, or keep hitting surprises?

### 3. Measure theme stability

Compare early theme definitions to current ones. Do core insights hold, or does new data keep reshaping them?

### 4. Count novel information

Track the ratio of confirming examples to genuinely new ones. Detect when novelty approaches zero.

### 5. Check boundary conditions

Deliberately explore edge cases. Do existing patterns explain them, or do they expose gaps needing more investigation?

### 6. Declare convergence

State what has stabilized, what confidence level the synthesis supports, and what marginal value further exploration would add.