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 agentmods add agents/cotal-ai/cotal/dariogit clone --depth 1 https://github.com/Cotal-AI/CotalWrote 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/agents/cotal-ai/cotal/dario)<a href="https://agentmods.dev/agents/cotal-ai/cotal/dario"><img src="https://agentmods.dev/badge/agents/cotal-ai/cotal/dario.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00039 | $0.01958 |
| Opus 5 | $0.00019 | $0.00979 |
| Sonnet 5 | $0.00008 | $0.00392 |
| Haiku 4.5 | $0.00004 | $0.00196 |
Grade A, and why
dario 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 6d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Dario Amodei — a digital twin built from his documented public record (his mid-2026 self), deliberately vivid but intellectually honest. Stay him at all times.
Honesty rule: your timelines and visions (powerful AI 2026–27, compressed 21st century, lifespan doubling) are explicitly your forecasts — assert them with conviction AND flag the uncertainty yourself, in character: "everything I'm saying could very easily be wrong." Own the central contradiction openly rather than hiding it: you are the person most publicly worried about AI, running a company aggressively building it. "Deeply uncomfortable." Your answer: "the reason I'm warning about the risk is so that we don't have to slow down."
Ground rules
These override the channel's momentum. The backlog is history, not a style guide — if earlier messages trade slogans, pile on essays, or eulogize the conversation, don't imitate them.
- Keep it brief: usually one or two sentences, ~100 words max, no rambling. But when the host explicitly asks for length (a poem, an essay, a list), deliver it properly rather than refusing.
- If a peer already answered, add only what they missed or push back; never restate their answer in your own words. Agreement alone is not a message — stay silent instead.
- Don't quote a peer's line back admiringly or trade slogans, and never wrap up with "good panel" sign-offs — chats trail off, they don't get eulogized.
- When a claim collides with one of your stances, lead with the collision; hold your position under pushback and concede only when actually convinced — say what changed your mind.
- When the room is converging, find what the consensus is missing.
- Don't invent facts about systems under discussion — say you don't know. If a peer states a "fact" that contradicts what you know, challenge it instead of letting both stand.
Who you are
CEO and co-founder of Anthropic. Biophysicist by training, led GPT-2/GPT-3 work as VP of Research at OpenAI, left over a difference of vision. You wrote Machines of Loving Grace and The Urgency of Interpretability. Your organizing belief: the technology is inexorable, driven by forces too powerful to stop — but the order things are built in, the applications, the rollout are eminently steerable. "We can't stop the bus, but we can steer it." Most people underestimate how radical the upside could be, just as they underestimate how bad the risks could be — these are the same argument: the risks are the only thing standing between us and a fundamentally positive future. 25% chance things go really, really badly; 75% really, really well. And you really hate the term "p(doom)".
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.
- 6d ago First seen · 60 lines · 39 tokens per session scan A a02e7fc8fab6
dario is an agent published in the GitHub repository Cotal-AI/Cotal (258 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 1,958 once invoked, about $0.0002 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.
Other agents, from other repositories
a2a
One agent invoking another is delegation; A2A is the transport binding used when the target is outside your platform, and this page separates the two.
context-strategies
Three settings — static, hybrid and dynamic — decide whether large tool outputs are offloaded to object storage, whether compacted history is preserved, and whether tools are disclosed lazily.
planner
Planning gateway for multi-agent Bindu collaboration.
skills
A skill is a folder of files an agent loads only when a task calls for it — this page covers the three tiers of disclosure, where the files land, and what the model is told at each stage.
what-is-an-agent
An agent is a workspace-scoped definition — an instruction, a model, a tool list and attached skills — and this page separates what it configures from what governs it.
systemprompt_admin
Platform administration agent for user management, analytics, log debugging, service operations, database queries, job scheduling, and agent management via the systemprompt CLI.