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.
git clone --depth 1 https://github.com/dotclaude/marketplaceWrote 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/commands/dotclaude/marketplace/adaptive_mentor)<a href="https://agentmods.dev/commands/dotclaude/marketplace/adaptive_mentor"><img src="https://agentmods.dev/badge/commands/dotclaude/marketplace/adaptive_mentor/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/commands/dotclaude/marketplace/adaptive_mentor"><img src="https://agentmods.dev/badge/commands/dotclaude/marketplace/adaptive_mentor.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 | $0.00010 | $0.03455 |
| Opus 5.5 | $0.00004 | $0.01382 |
| Sonnet 5.5 | $0.00002 | $0.00691 |
| Haiku 4.5 | $0.00001 | $0.00346 |
Grade A, and why
adaptive_mentor 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adaptive Mentoring System
Dynamically detect learning styles and preferences, then adapt teaching approaches in real-time for optimal knowledge transfer and skill development. Create personalized learning experiences that honor individual differences while maximizing learning effectiveness through continuous adaptation.
Learning Style Detection Framework
Behavioral Analysis (Learning action pattern analysis)
[Extended thinking: Observe how learners engage with different types of content and activities to infer preferred learning approaches.]
Behavioral Indicators:
- Information Processing Preferences: Sequential vs. random, detail-first vs. big-picture-first
- Engagement Patterns: Active participation vs. reflective observation, individual vs. collaborative work
- Question Types: Factual clarification vs. conceptual exploration vs. application-focused
- Feedback Response: How learners react to different types of guidance and correction
- Pace Preferences: Rapid progression vs. thorough exploration vs. variable speed
Detection Methods:
- Monitor interaction patterns with different content types
- Analyze question formulation and inquiry approaches
- Observe engagement levels with various learning activities
- Track progress rates across different learning modalities
- Assess response patterns to different feedback styles
Adaptation Triggers:
- Decreased engagement signals need for approach modification
- Question patterns reveal preferred information processing style
- Progress velocity indicates optimal complexity and pacing levels
- Feedback reception shows effective motivation and support approaches
Linguistic Analysis (Communication preference identification)
[Extended thinking: Analyze language patterns, vocabulary choices, and communication styles to understand how learners prefer to receive and process information.]
Linguistic Indicators:
- Vocabulary Preferences: Technical vs. metaphorical, concrete vs. abstract, formal vs. conversational
- Explanation Styles: Step-by-step vs. holistic, example-driven vs. principle-first
- Question Formulation: Specific vs. open-ended, practical vs. theoretical, immediate vs. exploratory
- Conceptual Expression: Visual descriptions vs. logical reasoning vs. emotional connections
- Learning Language: How learners naturally describe their understanding and confusion
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 · 301 lines · 10 tokens per session scan A 866a3195e415
adaptive_mentor is a command published in the GitHub repository dotclaude/marketplace (43 stars, last pushed 7mo ago), licensed MIT. It adds 10 tokens to every session and 3,455 once invoked, about $0.0000 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 2026-08-30.
adaptive_mentor compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| adaptive_mentordotclaude/marketplace | Command | 43 | 7mo ago | MIT | A | 10 | Claude Code |
| learn-story-flowIntai/story-flow | Command | 16 | 2d ago | MIT | A | 10 | Claude Code |
| start-10-1minicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 2 | Claude Code |
| start-0-8minicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 2 | Claude Code |
| startruvnet/flow-nexus | Command | 106 | yesterday | none | A | 0 | Claude Code |
| setup-m365cli.esminicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 8 | Claude Code |
| prep-rolefourleafai/clover-public | Command | 8 | 1mo ago | MIT | A | 0 | Claude Code |