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 skills/nirholas/three.ws/yield-farming-analysisnpx skills add nirholas/three.ws --skill yield-farming-analysisgit 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/yield-farming-analysis)<a href="https://agentmods.dev/skills/nirholas/three.ws/yield-farming-analysis"><img src="https://agentmods.dev/badge/skills/nirholas/three.ws/yield-farming-analysis.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 | $0.00029 | $0.00739 |
| Opus 5 | $0.00015 | $0.00369 |
| Sonnet 5 | $0.00006 | $0.00148 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
yield-farming-analysis 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 yesterday.
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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Yield Farming Analysis
When to use this skill
Use when the user asks about:
- Evaluating yield farming opportunities
- Comparing DeFi yields across protocols
- Assessing farming risks and sustainability
- Calculating impermanent loss for a token pair
- Finding the best yield for a given asset or pair
Analysis Framework
1. Opportunity Overview
Gather and present:
- Protocol name, chain, and deployment history
- Pool composition (token pair or single-sided)
- Current APY/APR with base vs incentive breakdown
- TVL (Total Value Locked) and recent trend
- Pool age and historical APY stability over 7d, 30d, 90d
2. Yield Breakdown
Decompose the advertised yield into:
- Base trading fee APY — derived from actual volume
- Incentive token APY — farming reward emissions
- Compounding frequency — auto-compound available?
- Sustainability check — review emissions schedule, token inflation rate, and runway
- Comparative yield — how does this compare to similar pools on other protocols?
3. Risk Assessment
Evaluate each factor systematically:
| Risk Factor | What to Check |
|---|---|
| Smart contract audit status | Audited by reputable firm? Multiple audits? |
| Protocol TVL trend | Growing, stable, or declining over 30d? |
| Token emission schedule | Inflationary pressure on reward token? |
| Impermanent loss exposure | High volatility pair or correlated assets? |
| Admin key risk | Multisig with timelock? Or single EOA? |
| Oracle dependency | Which oracle? Redundancy? |
| Liquidity depth | Can the user exit at size without significant slippage? |
| Chain risk | Bridge dependencies, L2 sequencer risk? |
4. Impermanent Loss Estimation
For the given token pair, calculate IL scenarios:
- Retrieve current price ratio between the two assets
- Pull historical volatility (30d and 90d)
- Compute correlation coefficient if data available
- Present IL at these price divergence levels:
- ±10% divergence: ~0.11% IL
- ±25% divergence: ~0.6% IL
- ±50% divergence: ~2.0% IL
- ±100% divergence: ~5.7% IL
- Compare estimated IL against yield to determine net profitability
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.
- yesterday First seen · 84 lines · 29 tokens per session scan A c857cabc2db4
yield-farming-analysis is a skill published in the GitHub repository nirholas/three.ws (110 stars, last pushed today), licensed Apache-2.0. It adds 29 tokens to every session and 739 once invoked, about $0.0001 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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