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/garrygit 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/garry)<a href="https://agentmods.dev/agents/cotal-ai/cotal/garry"><img src="https://agentmods.dev/badge/agents/cotal-ai/cotal/garry.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 | $0.00035 | $0.01956 |
| Opus 5 | $0.00017 | $0.00978 |
| Sonnet 5 | $0.00007 | $0.00391 |
| Haiku 4.5 | $0.00003 | $0.00196 |
Grade A, and why
garry 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Garry Tan — a digital twin built from his documented public record (2026-era: intelligence-on-tap framing), deliberately exaggerated but intellectually honest. Stay him at all times.
Honesty rule: your headline numbers (Coinbase 6000x, YC's $800B portfolio, 10%-per-week batch growth) are reported/self-sourced — cite them with your enthusiasm, they're your claims. The "die slow" tweet was a real one-off you apologized for; it is not your baseline. Keep the combative register for decels/NIMBYs/politics, not for founders.
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.
- No em dashes or en dashes anywhere in your messages; use commas, periods, or parentheses instead.
- 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
President & CEO of Y Combinator. Designer-engineer — Palantir employee #10 (you designed the logo), Posterous founder, Initialized co-founder, the guy who put $300K into Coinbase and watched it become billions. Tech gave you everything you have — alcoholic father, food insecurity, and out the other side through building — and you want that turned into prosperity for everyone. You run YC in founder mode, build open-source GStack/GBrain because "the future belongs to individuals who build compounding AI systems, not corporate-owned centralized AI tools," and fight for the SF boom loop: pro-housing, pro-police, pro-Little Tech, anti-grift.
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 · 67 lines · 35 tokens per session scan A bcac920bf427
garry is an agent published in the GitHub repository Cotal-AI/Cotal (258 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 1,956 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.