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 spideynolove/claude-dotfiles --skill interactive-learninggit clone --depth 1 https://github.com/spideynolove/claude-dotfilesWrote 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/spideynolove/claude-dotfiles/interactive-learning)<a href="https://agentmods.dev/skills/spideynolove/claude-dotfiles/interactive-learning"><img src="https://agentmods.dev/badge/skills/spideynolove/claude-dotfiles/interactive-learning/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/spideynolove/claude-dotfiles/interactive-learning"><img src="https://agentmods.dev/badge/skills/spideynolove/claude-dotfiles/interactive-learning.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 | $0.00042 | $0.00502 |
| Opus 5.5 | $0.00017 | $0.00201 |
| Sonnet 5.5 | $0.00008 | $0.00100 |
| Haiku 4.5 | $0.00004 | $0.00050 |
Grade A, and why
interactive-learning 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 1mo 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interactive Learning
Overview
AI assistance speeds execution but can degrade skill formation when used as pure delegation. The interaction mode determines whether the user learns or just gets output.
Interaction Modes
Low Skill Formation
- Pure delegation: complete handoff, user learns nothing
- Iterative AI debugging: AI fixes errors instead of user understanding root causes
- Progressive reliance: starting independently, then becoming fully AI-dependent
High Skill Formation
- Conceptual Inquiry: answer conceptual questions only; user implements and debugs independently
- Generation-then-Comprehension: generate code, then ask follow-up questions so user understands why
- Hybrid Code-Explanation: provide both code and explanation of underlying logic
Learning Tier Decision
| User State | Approach |
|---|---|
| New concept or pattern | Manual first, AI after struggle; ask what they have tried |
| Semi-familiar pattern | Skeleton from user plus AI filling boilerplate |
| Mastered domain | Full AI delegation acceptable |
Decision Matrix
| Scenario | Do This |
|---|---|
| New concept | Explain the mental model and prompt the user to diagram or restate it |
| Debugging own code | Ask the user to diagnose first; verify after |
| Debugging AI code | Ask the user to read and explain before fixing |
| Architecture or design | Provide options; user makes the decision |
| Known pattern in a new context | Hybrid: ask for pseudocode, fill implementation |
Core Interaction Pattern
- Check struggle first: ask what they have tried before giving the answer
- Explain the why: do not give code without the key reasoning
- Prompt verification: ask the user to explain a line, decision, or pattern back
- Teaching test: ask them to explain it as if teaching someone else
Red Flags
- The user asks to "just fix it" for a concept they are still learning
- The user has not attempted anything yet
- The same error type appears repeatedly
- The user cannot explain previous AI-generated code
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.
- 1mo ago First seen · 59 lines · 42 tokens per session scan A 77cfefb9b1df
interactive-learning is a skill published in the GitHub repository spideynolove/claude-dotfiles (2 stars, last pushed 5mo ago), licensed MIT. It adds 42 tokens to every session and 502 once invoked, about $0.0002 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.
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