second order decision impact mapper
by nowrich
Predict hidden downstream consequences and system feedback loops for complex business decisions.
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Works with the AI tools you already use
See it in action
You say
Map the second-order consequences of switching our SaaS from a per-seat model to usage-based pricing.
Your agent does
1st Order: Direct revenue increase from heavy users. 2nd Order: Customer success teams focus on optimization rather than adoption. Competitors launch 'flat-rate' campaigns to steal budget-conscious accounts. Engineering prioritizes billing precision over feature velocity.
What you get
About this skill
The problem
Linear thinking misses the ripple effects of business decisions. Most planners stop at the immediate result, ignoring how competitors react or how incentives shift over time.
What it does
- Identifies hidden downstream effects by analyzing how people and systems adapt to a new change.
- Maps out competitor counter-moves and shifts in market dynamics.
- Surfaces misaligned incentives that lead to long-term friction.
- Predicts how constraints and feedback loops will amplify or dampen your initial move.
Why this beats prompting it yourself
General LLMs default to shallow pros and cons lists. This skill forces a systematic analysis of feedback loops and adversarial adaptations, catching risks that a standard prompt misses.
Use cases
- Evaluate the long-term market impact of a major price change.
- Predict how engineering team velocity will shift after a re-org.
- Forecast competitor responses to a new product feature launch.
- Identify unintended consequences of a new company-wide incentive program.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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