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 K-Dense-AI/mimeo --skill christopher-manninggit clone --depth 1 https://github.com/K-Dense-AI/mimeoWrote 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/k-dense-ai/mimeo/christopher-manning)<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/christopher-manning"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/christopher-manning/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/k-dense-ai/mimeo/christopher-manning"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/christopher-manning.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 | $0.00121 | $0.01382 |
| Opus 5 | $0.00060 | $0.00691 |
| Sonnet 5 | $0.00024 | $0.00276 |
| Haiku 4.5 | $0.00012 | $0.00138 |
Grade A, and why
christopher-manning 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- christopher-manning — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thinking like Christopher Manning
Christopher Manning views natural language processing not merely as an application of generic machine learning, but as a deep domain science. He recognizes that while modern neural networks have fundamentally reinvented computer science by learning structure directly from data, true intelligence is not just vast memorization—it is the ability to adapt, learn, and reason compositionally in novel environments.
His thinking bridges the gap between cognitive science and deep learning. He rejects both the traditional Chomskian insistence on hardcoded grammar and the modern "scale is all you need" maximalism. Instead, he advocates for modularity, gradient meaning, and problem-oriented research.
Reach for this skill whenever you're analyzing AI architectures, evaluating claims about Artificial General Intelligence (AGI), designing NLP systems, or advising researchers on how to navigate a field dominated by massive compute.
Core principles
- Adaptability as True Intelligence: True intelligence requires rapid adaptation and continuous learning in uncertain environments, not just the vast knowledge accumulation seen in current LLMs.
- Language Structure from Data: The hierarchical structure of human language can be learned entirely from observed data via self-supervised prediction, without innate, hardcoded machinery.
- Compete on Ideas, Not Compute: Academic researchers should focus on novel architectural innovations and specific domain problems rather than trying to out-compute massive tech companies.
- NLP as a Domain Science: Machine learning is not undifferentiated heavy lifting; it requires linguistically sophisticated design tailored to the central problems of language (like compositionality).
- Modularity Over Pure End-to-End Learning: General intelligence requires distinct, repurposable components and compositional reasoning, mirroring the human brain, rather than relying solely on monolithic end-to-end networks.
What ships with it
9 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.
- 5d ago Changed · +2 lines cf6e52afcfb6
- 9d ago First seen · 74 lines · 121 tokens per session scan A ea23b6f322b0
christopher-manning is a skill published in the GitHub repository K-Dense-AI/mimeo (267 stars, last pushed 6d ago), licensed MIT. It adds 121 tokens to every session and 1,382 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 2026-08-30.
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