second order decision mapper
Stress-test decisions by mapping hidden ripple effects, feedback loops, and long-term systemic risks.
$5
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentssecond order decision mapper
Example session with this skill installed
Map the second-order effects of implementing a mandatory 'return to office' 3 days a week for a 500-person engineering team that has been fully remote for three years.
- Read your context and instructions
- Compiled the second order decision
I have analyzed the decision to implement a 3-day RTO policy. The following report maps the causal transition from immediate logistics to long-term organizational health.
Second-Order Decision Analysis: Engineering RTO Policy
Causal Map Table
| Category | Effect | Direction | Confidence | Magnitude |
|---|---|---|---|---|
| Behavioral | Commute-related fatigue reducing late-afternoon deep work blocks. | – | High | Moderate |
| Incentives | Senior devs living far away are incentivized to seek remote competitors. | – | High | Large |
| Feedback Loop | Reduced office occupancy -> less "serendipity" -> lower perceived value of RTO. | – | Medium | Moderate |
| Adaptation | Teams consolidate meetings into RTO days, creating "meeting fatigue" spikes. | ± | High | Moderate |
| Externalities | Local businesses near HQ see 15% revenue lift on Tue-Thu. | + | High | Small |
| Delayed Effects | Erosion of hiring pipeline geographic diversity over 12-18 months. | – | Medium | Large |
| Reversibility | High cost; reversing requires re-hiring for remote or closing office space. | – | High | Large |
| Risk | Emergence of "shadow remote" teams where managers ignore policy to retain talent. | – | Medium | Moderate |
Feedback Loop Diagram
[Policy: RTO] -> [Direct: Increased Face Time] -> [Second-Order: Commute Stress] -> [Result: Lower Morale] -> [Action: Performance Dip] -> [Feedback: Management increases RTO pressure] (Vicious Cycle)
Scenario Comparison
- Current (3-day RTO): Net negative on retention, net positive on cross-functional alignment. High fragility.
- Do Nothing (Full Remote): Net neutral. Maintenance of current talent pool, potential silo risk.
- Optional Hubs: Net positive. Maintains flexibility while providing infrastructure for those who want it.
Fragility Ranking (Top 3 Sensitive Assumptions)
The "Commute Tolerance" Assumption: If engineers value flexibility over salary more than estimated, turnover will exceed 20%.
2. The "Collaboration" Assumption: If whiteboarding doesn't actually speed up velocity, the RTO cost has no ROI.
3. The "Market Parity" Assumption: If top-tier competitors remain remote, this decision is highly fragile.
Next steps
- Review the Monitoring Dashboard: Track weekly voluntary attrition and Glassdoor sentiment trends.
- Conduct a 'pre-mortem' with team leads to identify potential "shadow remote" pockets.
- Establish a 90-day review period with a clear threshold for reversing the policy if velocity drops >10%.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Linear thinking ignores the ripple effects of business decisions. Direct wins often mask hidden costs, perverse incentives, and long-term systemic failures that emerge months after implementation.
What it does
- Maps direct first-order outcomes to behavioral responses and new incentives.
- Identifies reinforcing and balancing feedback loops that stabilize or destabilize systems.
- Evaluates externalities, delayed effects, and the cost of reversibility.
- Estimates effect direction and confidence levels without using false numerical precision.
- Ranks decision fragility by identifying which assumptions flip the outcome if proven wrong.
Why this beats prompting it yourself
Basic prompts focus on immediate pros and cons. This skill enforces a rigorous causal web methodology across eight specific categories like adaptation and tail risk, ensuring you don't miss "unknown unknowns." It structures the output into a monitoring dashboard and fragility ranking that a standard chat response lacks.
Use cases
- Evaluating a new pricing model or discount strategy for hidden churn triggers.
- Assessing the impact of a remote-work policy on long-term culture and hiring.
- Stress-testing a pivot to a new technology stack for technical debt and ecosystem lock-in.
- Analyzing the externalities of a new product feature on existing user segments.
Does not provide high-precision quantitative modeling or statistical simulations. Requires clearly defined decisions and target populations to generate relevant causal maps.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
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- 3
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