Apply public choice theory to analyze political decision-making as rational self-interested behavior. Use this skill when the user needs to evaluate government policy failures, rent-seeking costs, voting outcomes, or bureaucratic incentives, especially when the assumption of benevolent government is questionable.
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-public-choice
Public choice applies economic reasoning — rational self-interest, strategic behavior, and equilibrium analysis — to political decision-making. Politicians, bureaucrats, voters, and lobbyists are modeled as utility maximizers, not benevolent social planners. The theory explains phenomena such as rent-seeking, logrolling, pork-barrel spending, regulatory capture, and the systematic divergence between public interest and political outcomes. Buchanan and Tullock's foundational work treats constitutional rules as the ultimate mechanism design problem.
IRON LAW: Public officials are NOT benevolent social planners — they
respond to incentives just like market participants. Policy outcomes
reflect the preferences of those with political power, not the
preferences of society at large.
Step 1 — Identify the Political Market
Map the actors: voters, politicians, bureaucrats, interest groups. Specify what each actor maximizes and the constraints they face (electoral cycles, budget rules, information costs).
Step 2 — Apply the Relevant Model
Choose from: (a) Median Voter Theorem — in single-dimensional, single-peaked preference space, the median voter's preferred policy wins under majority rule; (b) Rent-seeking model — agents spend real resources to capture a transfer, dissipating up to the full value of the rent; (c) Logrolling / vote trading — minorities trade votes across issues to pass legislation that fails majority support on each issue individually; (d) Bureaucracy model — budget-maximizing bureaus produce beyond efficient output.
Step 3 — Estimate Government Failure Costs
Quantify: (a) Tullock rectangle — resources spent on rent-seeking; (b) Allocative distortion from policies that reflect political rather than economic efficiency; (c) X-inefficiency within government agencies lacking competitive pressure. Compare against the market failure the policy aims to correct.
Step 4 — Propose Institutional Remedies
Recommend constitutional or institutional design changes: supermajority requirements, sunset clauses, independent agencies, fiscal rules, transparency mandates, or decentralization (Tiebout competition). Evaluate trade-offs between flexibility and constraint.
## Public Choice Analysis: [Policy / Institution]
### Political Actors
| Actor | Objective | Key Constraint |
|----------------|-----------------------|------------------------|
| Voters | | |
| Politicians | | |
| Bureaucrats | | |
| Interest groups | | |
### Model Applied
- **Framework**: Median voter / Rent-seeking / Logrolling / Bureaucracy
- **Prediction**: [what the model predicts will happen]
- **Observed outcome**: [what actually happens — consistent?]
### Government Failure Costs
| Cost Category | Estimate / Description |
|-----------------------|----------------------|
| Rent-seeking expenditure | |
| Allocative distortion | |
| X-inefficiency | |
### Market Failure vs. Government Failure
- **Market failure being addressed**: [externality / public good / monopoly]
- **Government failure introduced**: [rent-seeking / capture / inefficiency]
- **Net assessment**: [intervention improves welfare? or worsens it?]
### Institutional Recommendations
[Specific reforms with rationale]
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Take asgard-ai-platform/grad-public-choice from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.