Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add deciqAI/knowledge-skills --skill principal-agentgit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/deciqai/knowledge-skills/principal-agent)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/principal-agent"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/principal-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/principal-agent"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/principal-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00114 | $0.01721 |
| Opus 5 | $0.00057 | $0.00860 |
| Sonnet 5 | $0.00023 | $0.00344 |
| Haiku 4.5 | $0.00011 | $0.00172 |
Grade A, and why
principal-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Principal–Agent Problem
Overview
One party (the principal) delegates to another (the agent) whose interests differ and whose actions can't be fully observed — producing agency cost: monitoring spend + agent bonding spend + residual loss. Formalized by Jensen & Meckling (1976). Structure produces the behavior, not character — so the fix is structural.
Composes with signaling-games, repeated-games-reputation, prisoners-dilemma, and okr-goal-setting.
When to Use
- Board reviewing executive compensation; outsourcing or contractor decisions
- Employees/executives behaving in ways that puzzle leadership
- New joint venture, LP-GP fund, or platform marketplace being structured
- Someone says "agency cost," "moral hazard," "skin in the game," "fiduciary duty"
- Deploying an autonomous AI agent, sizing AI capex/adoption, or facing AI-native competition where you delegate to a system whose objective and actions you can't fully observe (alignment / guardrails / human-in-the-loop)
Not when: fully aligned interests + fully observable behavior; contract design cost exceeds the agency cost it would prevent.
Coaching Novices (Adaptive Front Door)
- Engine mode: concrete case → run The Process directly.
- Coach mode: unfamiliar or no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-liner: when one party delegates to another whose interests differ and actions are unobservable, the agent will systematically act in ways the principal didn't want — cure is structure, not character.
- Check fit: fully aligned + fully observable → no agency problem.
- Elicit their specific relationship — who is principal, who is agent, what does each really want?
[WAIT — do not advance until user responds]
- Probe: what can the principal not observe? which misalignment dominates (effort / risk / time horizon / info asymmetry / multitasking)?
[WAIT — do not advance until user responds]
- Close: name the specific misalignment and one structural lever (incentive, observability, or selection).
[WAIT — do not advance until user responds]
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 126 lines · 114 tokens per session scan A 1473faf0586f
principal-agent is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 114 tokens to every session and 1,721 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
edge-tts
Text-to-speech conversion using uvx edge-tts for generating audio from text. Use when (1) User requests audio/voice output with the "tts" trigger or keyword. (2) Content needs to be spoken rather than read (multitasking, accessibility, driving, cooking). (3) User wants a specific voice, speed, pitch, or format for TTS…
mcp-deepwiki
Skills for accessing and searching docs in DeepWiki/GitHub’s public code repositories can help users understand open-source project source codes, and users can also ask questions directly about the code docs.
tianqi
A weather-lookup workflow for Chinese locations, covering forecasts, hourly conditions, weather warnings, and daily-life indexes.
compliance-check
Compliance pre-flight for a feature, campaign, or initiative — maps the data and activity involved, checks applicable regimes (privacy/GDPR-style, consumer protection, marketing rules, sector-specific), lists required approvals and notices, builds a gap list with remediation owners, and ends in a go/no-go…
plan-payroll
Plans payroll cash: true loaded cost per hire (gross plus employer taxes, benefits, tools), a payroll calendar with cutoffs and cash-out dates, a scenario table for new hire vs raise vs contractor-vs-employee, and a payroll-to-revenue check against rough industry bands. Use when the user asks "can I afford to hire"…
ui-ux-pro-max
Builds an end-to-end UI system for a product — design tokens (color scale, type scale, spacing, radii, shadows), a component inventory covering every interaction state, a layout grid, and WCAG contrast checks — emitted as CSS variables plus a component spec doc. Use when the user says "set up a design system", "create…