Apply Agency Theory (Jensen and Meckling, 1976) to diagnose principal-agent problems — moral hazard, adverse selection — and design governance mechanisms to align interests. Use this skill when the user needs to analyze conflicts of interest between owners and managers, design incentive or monitoring structures, evaluate corporate governance effectiveness, or when they ask 'how do we ensure managers act in shareholders interest', 'why is this incentive plan failing', or 'what governance mechanisms reduce agency costs'.
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-strat-agency
Agency theory addresses the relationship where one party (principal) delegates work to another (agent) whose interests may diverge. Jensen and Meckling (1976) formalized how the separation of ownership and control creates agency costs: monitoring costs, bonding costs, and residual loss.
IRON LAW: Agency costs are unavoidable — the goal is to minimize
TOTAL agency costs (monitoring + bonding + residual loss).
Eliminating one cost type often increases another. Optimal
governance minimizes the sum, not any single component.
Key assumptions:
| Problem | When | Mechanism |
|---------|------|-----------|
| Moral hazard | Post-contract; agent effort is unobservable | Hidden action |
| Adverse selection | Pre-contract; agent type is unobservable | Hidden information |
| Hold-up | Post-investment; agent exploits lock-in | Relationship-specific investment |
## Agency Analysis: [Context]
### Principal-Agent Map
| Principal | Agent | Delegation | Key Conflict |
|-----------|-------|-----------|--------------|
| [who] | [who] | [what] | [goal divergence] |
### Agency Problem Diagnosis
- Type: [moral hazard / adverse selection / both]
- Information asymmetry: ...
- Observable vs unobservable: ...
### Governance Mechanisms
| Mechanism | Type | Cost | Expected Effect |
|-----------|------|------|-----------------|
| [name] | [monitoring/bonding/incentive] | [est.] | [reduction in...] |
### Total Agency Cost Assessment
- Monitoring costs: ...
- Bonding costs: ...
- Estimated residual loss: ...
Analyzing CEO compensation: principal (shareholders) faces moral hazard (CEO effort unobservable). Design combines outcome-based incentives (stock options aligned with long-term value) with behavior-based monitoring (independent board, audit committee). Evaluates trade-off between monitoring intensity and incentive pay.
Proposing "more monitoring" without considering that excessive monitoring increases costs and may crowd out intrinsic motivation. Agency theory requires minimizing total agency costs, not maximizing control.
Skill converted from mcp-deploy-manage-agents.prompt.md
Use this skill when the user wants to launch a new AltClaw, OpenClaw, PicoClaw, or Ottie deployment through Cloud Claw. Covers the same user-facing fields and constraints exposed in the Cloud Claw UI, using the local altllm cloud-claw-* commands. Do NOT use for post-launch lifecycle tasks like start/stop/delete/logs; use cloud-claw-manage-vm.
Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents in Azure AI Foundry.
Build MCP (Model Context Protocol) servers on Cloudflare Workers with tools, resources, and prompts.
Chain agent outputs as inputs in sequential or parallel pipelines for data flow orchestration
Audit cloned or reimplemented websites for fidelity gaps, tracking scripts, source-brand and language residue, placeholders, and risky external dependencies. Use before handoff or deployment, or when asked to review a website clone for cleanup and readiness.
> Install and operate Hermes Tweet, a Hermes Agent plugin for X/Twitter research, timeline reading, tweet analysis, and approval-gated tweet actions. Use this skill when installing Hermes Tweet, researching X/Twitter accounts, monitoring launch signals, investigating mentions, auditing giveaways, or preparing guarded tweet actions. Use proactively when a Hermes Agent workflow needs current X/Twitter context. Requires XQUIK_API_KEY for read and action tools.
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Take asgard-ai-platform/grad-strat-agency 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.