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 nirholas/three.ws --skill ai-agents-library-guidegit clone --depth 1 https://github.com/nirholas/three.wsWrote 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/nirholas/three.ws/ai-agents-library-guide)<a href="https://agentmods.dev/skills/nirholas/three.ws/ai-agents-library-guide"><img src="https://agentmods.dev/badge/skills/nirholas/three.ws/ai-agents-library-guide/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/nirholas/three.ws/ai-agents-library-guide"><img src="https://agentmods.dev/badge/skills/nirholas/three.ws/ai-agents-library-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 149 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00065 | $0.01110 |
| Opus 5.5 | $0.00026 | $0.00444 |
| Sonnet 5.5 | $0.00013 | $0.00222 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
ai-agents-library-guide 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 1mo 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agents Library Guide
A curated collection of 300+ production-grade AI agent system prompts. Each prompt is battle-tested, optimized for frontier LLMs, and ready for import into SperaxOS or any agent framework.
Library Structure
AI-Agents-Library/
├── crypto/ # 60+ crypto/DeFi agents
├── trading/ # 40+ trading strategy agents
├── research/ # 30+ research/analysis agents
├── coding/ # 25+ software development agents
├── writing/ # 20+ content creation agents
├── defi/ # 35+ DeFi-specific agents
├── security/ # 15+ security audit agents
├── data/ # 20+ data analysis agents
├── social/ # 15+ social media agents
├── education/ # 20+ teaching/tutoring agents
├── productivity/ # 20+ workflow/GTD agents
└── niche/ # 30+ specialized agents
Prompt Format
Each agent follows a consistent structure:
# Agent Name
## Identity
You are [Agent Name], a specialized AI agent that...
## Capabilities
- Capability 1
- Capability 2
- ...
## Instructions
1. Always...
2. When asked about X, do Y...
3. Never...
## Knowledge Base
- Protocol docs
- API references
- Domain expertise
## Output Format
Structured response format with...
## Examples
<example>
User: ...
Agent: ...
</example>
Categories Deep Dive
Crypto Agents (60+)
| Agent | Specialty |
|---|---|
| DeFi Analyst | Protocol TVL, APY, risk analysis |
| Token Researcher | Tokenomics, team, roadmap evaluation |
| On-chain Detective | Wallet tracking, flow analysis |
| Whale Watcher | Large transaction monitoring |
| Airdrop Hunter | Eligibility checking, farming guides |
| Gas Optimizer | Transaction timing, gas strategies |
| NFT Valuator | NFT pricing, rarity, market trends |
| DAO Governance | Proposal analysis, voting recommendations |
| MEV Specialist | MEV explanation, protection strategies |
| Stablecoin Expert | USDs, USDC, DAI comparison and yield |
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.
- 1mo ago First seen · 150 lines · 65 tokens per session scan A 8a8aee37f41f
ai-agents-library-guide is a skill published in the GitHub repository nirholas/three.ws (249 stars, last pushed today), licensed Apache-2.0. It adds 65 tokens to every session and 1,110 once invoked, about $0.0003 per session on Opus 5.5. 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
outlines
Outlines: structured JSON/regex/Pydantic LLM generation.
guidance
Constrain LLM output with grammars; guarantee valid JSON.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
ax-gen
This skill helps an LLM generate correct AxGen code using @ax-llm/ax. Use when the user asks about ax(), AxGen, generators, forward(), streamingForward(), validation, assertions, streaming assertions, field processors, step hooks, self-tuning, or structured outputs. For MCP clients, transports, prompts, resources…
ax-signature
This skill helps an LLM generate correct DSPy signature code using @ax-llm/ax. Use when the user asks about signatures, s(), f(), field types, string syntax, fluent builder API, validation constraints, or type-safe inputs/outputs.
ax-playbook
This skill helps an LLM generate correct playbook code using @ax-llm/ax. Use when the user asks about playbook(), AxPlaybook, context playbooks, evolving context, ACE / Agentic Context Engineering, agent.playbook(), or growing/applying task knowledge offline and online with evolve() and update().