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/wrg32786/aigent-os/learnnpx skills add wrg32786/aigent-os --skill learngit clone --depth 1 https://github.com/wrg32786/aigent-osWrote 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/wrg32786/aigent-os/learn)<a href="https://agentmods.dev/skills/wrg32786/aigent-os/learn"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/learn.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 | $0.00073 | $0.01175 |
| Opus 5 | $0.00036 | $0.00588 |
| Sonnet 5 | $0.00015 | $0.00235 |
| Haiku 4.5 | $0.00007 | $0.00118 |
Grade A, and why
Learn 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 3d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn
The learning compounding skill. Most principals lose the value of a learning sweep within 48 hours — they read three repos, watch one talk, evaluate two tools, and a week later can't remember what they decided about any of them. /learn captures the structured outcome of every such sweep.
When to use
- The principal has just read a repo, paper, talk, or tool description
- The principal is evaluating something but doesn't have an immediate need
- The principal asks "should I be using X?" or "what did we decide about Y?"
- After any session where the input was learning, not doing
- Triggered by Caddy on prompts like: "/learn", "I just read", "evaluating", "looked into", "should I use", "what did we decide about", "captured this", "interesting tool", "considered tool"
How to execute
Step 1 — Take the input
The principal provides:
- Subject: the thing being evaluated (tool, technique, concept, repo, paper, idea)
- Source: where it came from (URL, talk title, conversation, repo name)
- Snapshot: 2-5 sentences on what the thing is and what it claims to do
If any field is missing, ask one focused question to get it.
Step 2 — Locate it on the modern AI stack
Reference [[Modern AI Infrastructure Stack]]. Place the subject at the right layer:
- Application
- Persistence
- Messaging
- Isolation
- Compute
- Observability
- Orchestration
If it doesn't fit cleanly on the stack, it's either a doctrine concept (note that explicitly) or an artifact-of-AI-trends category (e.g., "agent framework", "prompt-engineering pattern").
Step 3 — Assess relevance
Three categories:
- ADOPT — fits an actual current bottleneck. Action follows.
- HOLD — could fit in a future state, but no current bottleneck. Capture the trigger condition.
- REJECT — wrong category, wrong scale, or violates a load-bearing constraint. Capture the reasoning.
- MONITOR — interesting but no clear path. Re-evaluate next quarter.
Step 4 — Capture reconsideration triggers (for HOLD only)
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.
- 3d ago First seen · 128 lines · 73 tokens per session scan A 8a41d07392be
Learn is a skill published in the GitHub repository wrg32786/aigent-os (17 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 1,175 once invoked, about $0.0004 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 2026-09-01.
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