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/robinslange/learning-loop/helpnpx skills add robinslange/learning-loop --skill helpgit clone --depth 1 https://github.com/robinslange/learning-loopWhat 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 | $0.00023 | $0.03372 |
| Opus 5 | $0.00012 | $0.01686 |
| Sonnet 5 | $0.00005 | $0.00674 |
| Haiku 4.5 | $0.00002 | $0.00337 |
Grade A, and why
help 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 2d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Help: Learning Loop Commands
Overview
Present the guide below when the user runs /learning-loop:help or asks what the learning loop can do. Adapt the level of detail to context: if they seem experienced, lean on the quick reference at the end. If they're new, walk them through the narrative.
When to Use
/learning-loop:help: show all commands- When the user asks "what can the learning loop do?"
- When suggesting a next action and the user seems unsure of their options
Output
Present this guide:
Learning Loop
The learning loop turns conversations into lasting knowledge. Ideas start rough, get refined through research, and settle into permanent notes in your vault. Every command serves one of three jobs: bringing ideas in, making them stronger, or keeping things tidy.
Start here
Curious about something? Run /learning-loop:discovery.
/learning-loop:discovery "spaced repetition"
It searches your vault for what you already know, researches the web for what you don't, and walks you through the topic interactively. You steer: it digs. At the end, key insights land in your inbox as atomic notes.
Want to just browse without saving anything? Add --surf:
/learning-loop:discovery "spaced repetition" --surf
Other options: --style guided|branch|checkpoint, --tone academic|conversational|persona.
Need depth, not a walk-through? Run /learning-loop:research.
/learning-loop:research "does creatine help cognition in sleep-deprived adults?"
Deep research with the local librarian doing the token-heavy middle: Claude scopes the question into search angles, the local Ollama model (12b+ tier) runs Search, Fetch, and Extract, then Claude adversarially verifies the surviving claims and synthesizes a cited report. Falls back to a Claude-native research path automatically when the librarian is unavailable or the model is below the research tier.
Reading something good? Run /learning-loop:literature.
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.
- 2d ago First seen · 237 lines · 23 tokens per session scan A 3aab011761ab
help is a skill published in the GitHub repository robinslange/learning-loop (11 stars, last pushed 10d ago), licensed Apache-2.0. It adds 23 tokens to every session and 3,372 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 2026-08-30.
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