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 endowment-effectgit 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/endowment-effect)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/endowment-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/endowment-effect/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/endowment-effect"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/endowment-effect.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 | $0.00122 | $0.02077 |
| Opus 5 | $0.00061 | $0.01038 |
| Sonnet 5 | $0.00024 | $0.00415 |
| Haiku 4.5 | $0.00012 | $0.00208 |
Grade A, and why
endowment-effect 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 8d 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.
Endowment Effect
Overview
People demand roughly 2× more to give up something they own than they would pay to acquire the identical thing — purely because they own it. Ownership converts a transaction from a potential gain into a potential loss, and losses loom ~2× larger than gains (prospect theory). The effect kicks in within 30 seconds of possession; customization and personalization amplify it.
Two operating directions: Leverage — trigger buyer endowment via free trials, personalization, and data import to raise willingness-to-pay. Counteract — in M&A or negotiation, identify the seller's endowment premium and bridge it with earnouts, neutral reference prices, and exchange framing.
Composes with loss-aversion-prospect-theory, status-quo-bias, anchoring, batna-zopa.
When to Use
- Pricing a product, subscription, or asset and needing to understand buyer willingness-to-pay dynamics
- Designing a free-trial or onboarding flow and deciding how much personalization to front-load
- Negotiating an acquisition where the seller's asking price significantly exceeds comparables
- Advising a founder or asset owner on why their valuation differs from market offers
- Structuring earnouts or deferred consideration to bridge a valuation gap
- Detecting why a team is reluctant to abandon a feature or strategy they built (IKEA effect variant)
- Deciding "build vs. buy" on AI — a team overvaluing its in-house model, dataset, or codebase versus a stronger/cheaper external foundation model, or a founder anchoring on a peak AI valuation in M&A/wind-down talks
Not when: valuation difference is genuine information asymmetry; pure commodity with transparent market price; evaluating policy-level defaults (use status-quo-bias).
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete negotiation, pricing, or product design problem → run The Process directly.
- Coach mode: user is new or trying to understand a valuation discrepancy → guide step by step.
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.
- 8d ago First seen · 126 lines · 122 tokens per session scan A 7e32c727653b
endowment-effect is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 122 tokens to every session and 2,077 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…