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 reverse-information-paradoxgit 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/reverse-information-paradox)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/reverse-information-paradox"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/reverse-information-paradox/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/reverse-information-paradox"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/reverse-information-paradox.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 | $0.00276 | $0.05077 |
| Opus 5 | $0.00138 | $0.02538 |
| Sonnet 5 | $0.00055 | $0.01015 |
| Haiku 4.5 | $0.00028 | $0.00508 |
Grade A, and why
reverse-information-paradox 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 7d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse Information Paradox
Overview
When you buy intelligence from an external AI, you pay for it twice. Once in money — and again in the proprietary knowledge you must surrender to make that intelligence useful. This is the Reverse Information Paradox, a strategic thesis coined by Satya Nadella (Microsoft Chairman & CEO) in a strategy essay published 2026-07-12. Its core claim, in Nadella's words:
"you essentially pay for intelligence twice … once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful." — Satya Nadella, Reverse Information Paradox essay, July 2026
The thesis inverts Arrow's information paradox. Arrow (1962) exposed the seller of information: you cannot know what a piece of information is worth until you've seen it, but once you've seen it you no longer need to pay — so the seller is exposed. Nadella flips the exposure to the buyer/user of AI. To get useful output, the enterprise must feed the model its context: its data, its decision rules, its corrections. As Nadella puts it, "The better you want the model to perform, the more of that knowledge you have to feed it." That feeding leaves a trail — what the essay calls intelligence exhaust: prompts, tool calls, corrections, and evals that together form what coverage of the thesis describes as a record of how an organization works and makes decisions, flowing toward whoever controls the learning infrastructure. Nadella's resolution reframes the exhaust as an asset: "In consuming intelligence, you are creating intelligence. And what you create should belong to you." The prescription is a hard trust boundary inside each enterprise tenant — own your data, evals, memory, adapted model weights, and learning loops — and an orchestration layer decoupled from any single model provider, so no one vendor captures your learning loop.
This is a July-2026 thesis, days old and single-origin (Nadella plus commentary) at authoring time. Treat it as a contemporary strategic argument, not established empirical canon; it has not been through the years of validation that its parent concepts have.
What ships with it
5 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.
- 7d ago First seen · 170 lines · 276 tokens per session scan A 977802e50050
reverse-information-paradox is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 9d ago), licensed MIT. It adds 276 tokens to every session and 5,077 once invoked, about $0.0014 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.
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