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 farmfriend-labs/agent-skills-farming --skill learning-from-experiencegit clone --depth 1 https://github.com/farmfriend-labs/agent-skills-farmingWrote 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/farmfriend-labs/agent-skills-farming/learning-from-experience)<a href="https://agentmods.dev/skills/farmfriend-labs/agent-skills-farming/learning-from-experience"><img src="https://agentmods.dev/badge/skills/farmfriend-labs/agent-skills-farming/learning-from-experience/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/farmfriend-labs/agent-skills-farming/learning-from-experience"><img src="https://agentmods.dev/badge/skills/farmfriend-labs/agent-skills-farming/learning-from-experience.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00142 |
| Opus 5 | $0.00000 | $0.00071 |
| Sonnet 5 | $0.00000 | $0.00028 |
| Haiku 4.5 | $0.00000 | $0.00014 |
Grade A, and why
learning-from-experience 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 12d 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.
What it actually says
Learning From Experience
Track and learn from farm experiences for continuous improvement.
Purpose
Experience is valuable but forgotten: what worked last season, what didn't work, why certain decisions succeeded, lessons learned the hard way. Institutional memory is lost without documentation. This skill tracks experiences and lessons.
Problem Solved
Capabilities
Instructions
Usage by AI Agent
Usage by Farmer
Tools
Environment Variables
Real-World Examples
Example 1: Basic Usage
Example 2: Advanced Features
Example 3: Integration
Safety Considerations
Troubleshooting
Manufacturer and Research References
Legal Considerations
Maintenance and Updates
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.
- 12d ago First seen · 40 lines · 0 tokens per session scan A 2fdad73e10cd
learning-from-experience is a skill published in the GitHub repository farmfriend-labs/agent-skills-farming (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 142 tokens. 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 2026-08-31.
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