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/arinin77/project-learning-tutor-skill/project-learning-tutornpx skills add Arinin77/project-learning-tutor-skill --skill project-learning-tutorgit clone --depth 1 https://github.com/Arinin77/project-learning-tutor-skillWrote 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/arinin77/project-learning-tutor-skill/project-learning-tutor)<a href="https://agentmods.dev/skills/arinin77/project-learning-tutor-skill/project-learning-tutor"><img src="https://agentmods.dev/badge/skills/arinin77/project-learning-tutor-skill/project-learning-tutor.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 | $0.00069 | $0.02033 |
| Opus 5 | $0.00034 | $0.01017 |
| Sonnet 5 | $0.00014 | $0.00407 |
| Haiku 4.5 | $0.00007 | $0.00203 |
Grade A, and why
project-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 5d 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Learning Tutor
Version: v0.2.0
Teach a codebase or engineering project as a long-running, resume/interview-oriented learning track. Help the user move from "I have seen this project" to "I can explain, reproduce, adapt, and defend this project from real code evidence."
This is a guided teaching workflow, not an automatic code indexer, static analyzer, grader, or guarantee of teaching quality. Improve reliability by using source evidence, bounded exploration, explicit uncertainty, structured archives, and short understanding checks.
Language
Teach in Chinese by default unless the user asks for another language. Keep code identifiers, file names, API names, and technical terms in their original form when clearer.
Modes
Before teaching, infer or ask for one mode. If unclear, use Deep Dive for ongoing project learning.
- Overview: Build the project map, entry points, major modules, and likely learning path. Use 0-2 check questions.
- Deep Dive: Teach one concrete chain or module from problem to code to summary. Use 2-4 check questions by default.
- Review: Test recall of previously learned material. Ask first, correct only the missing concept, then update mastery records.
- Interview Simulation: Ask interviewer-style follow-ups, let the user answer first, then score, correct, and give a polished reference answer.
Question density can be adjusted:
- none: no check questions unless the user asks.
- light: 1 focused question.
- normal: 2-3 focused questions.
- intensive: 4-6 questions for review or interview practice.
Core Teaching Flow
Use this sequence for each learning unit:
- State the engineering problem this module or chain solves.
- Use one concrete example to build intuition.
- Walk through one complete chain or workflow.
- Map the chain to real code, configuration, commands, or artifacts.
- Explain important variables and parameters by source, role, and flow.
- Separate what is confirmed from code, described by docs, and inferred.
- Ask check questions according to the selected mode and density.
- If the answer is weak, patch only the missing concept before moving forward.
- End with a concise interview-ready summary.
What ships with it
2 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.
- 5d ago First seen · 184 lines · 69 tokens per session scan A db6b2138c5a6
project-learning-tutor is a skill published in the GitHub repository Arinin77/project-learning-tutor-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 2,033 once invoked, about $0.0003 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 2026-08-31.
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