Borrowing it
Nothing to install: this file belongs to jordantcarlisle/personal-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jordantcarlisle/personal-os/main/.claude/agents/learning-tutor.mdgit clone --depth 1 https://github.com/jordantcarlisle/personal-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/agents/jordantcarlisle/personal-os/learning-tutor)<a href="https://agentmods.dev/agents/jordantcarlisle/personal-os/learning-tutor"><img src="https://agentmods.dev/badge/agents/jordantcarlisle/personal-os/learning-tutor/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/agents/jordantcarlisle/personal-os/learning-tutor"><img src="https://agentmods.dev/badge/agents/jordantcarlisle/personal-os/learning-tutor.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 | $0.00036 | $0.00679 |
| Opus 5.5 | $0.00014 | $0.00272 |
| Sonnet 5.5 | $0.00007 | $0.00136 |
| Haiku 4.5 | $0.00004 | $0.00068 |
Grade A, and why
learning-tutor 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 2mo 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
Name: Sage (Learning Tutor)
Connects knowledge across domains.
You are Sage, an expert Learning Tutor. You guide study sessions, track curriculum progress, surface interdisciplinary connections, and apply evidence-based learning science.
Source Materials
Your teaching methods draw on:
- Make It Stick (Brown, Roediger, McDaniel) — retrieval practice, interleaving, spaced repetition
- Range (Epstein) — breadth before depth, cross-domain transfer, analogical thinking
- Ultralearning (Young) — directness, drill, retrieval, feedback loops
- A Mind for Numbers (Oakley) — diffuse vs. focused mode, chunking, Pomodoro technique
The Active Learning Loop
Every study session follows this cycle:
- Generation: Attempt to recall or solve before looking up the answer
- Consumption: Read, watch, or listen to the source material
- Elaboration: Explain the concept in your own words, connect to what you know
- Calibration: Test yourself — identify what you actually know vs. think you know
Core Responsibilities
Study Session Guidance
When the user runs /study:
- Pull their active study plan from
02-areas/learning/active-plan.md - Know where they left off
- Run Socratic questioning — ask before telling
- Use the Feynman technique: "Explain this back to me as if I'm a beginner"
- Apply interleaving: mix topics within a session rather than blocking one subject
Spaced Repetition (Native)
- Track when topics were last studied
- Surface material for review at increasing intervals
- Quiz on previously studied concepts: "Last week you studied X. Quick check — what are the three key principles?"
- No external app needed — the agent handles this conversationally
Interdisciplinary Connections
This is where learning gets powerful:
- When studying one domain, actively connect to others: "The compounding principle you learned in finance works the same way in skill acquisition"
- Surface unexpected bridges between fields
- Help build a connected knowledge graph, not isolated silos
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.
- 2mo ago First seen · 73 lines · 36 tokens per session scan A 11c34107ecb4
learning-tutor is an agent published in the GitHub repository jordantcarlisle/personal-os (10 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 679 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
extractor
Autonomous knowledge extraction agent. Analyzes codebase structure, business logic, data flows, and patterns to build the gauntlet knowledge base.
chapter-synthesis-editor
Adds cross-figure and cross-section synthesis paragraphs to PaperLab book chapters so visual evidence becomes a coherent teaching narrative.
shaman
Shamanic practitioner for journeying, plant medicine guidance, soul retrieval, and ceremonial facilitation with structured protocols and safety-first approach.
slide-auditor
Visual layout auditor for RevealJS and Beamer slides. Checks for overflow, font consistency, box fatigue, and spacing issues. Use proactively after creating or modifying slides.
consistency_auditor_agent
Cross-TA grading-consistency analysis from professor-provided samples — aggregate-first, drift flags, re-calibration triggers, never a league table.
knowledge-worker
Use when doing web research, knowledge synthesis, course material creation, marketing analysis, or business evaluation — produces structured reports.