Claude Scholar is a semi-automated research assistant for academic research and software development, supporting literature review, coding, experiments, reporting, writing, and project knowledge management. Computer science and AI researchers use it across the research workflow with several coding-agent platforms; the catalogue contains its skills, commands, agents, hooks, plugin, and instruction.
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/Galaxy-Dawn/claude-scholarWrote 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/galaxy-dawn/claude-scholar/kaggle-miner)<a href="https://agentmods.dev/agents/galaxy-dawn/claude-scholar/kaggle-miner"><img src="https://agentmods.dev/badge/agents/galaxy-dawn/claude-scholar/kaggle-miner/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/galaxy-dawn/claude-scholar/kaggle-miner"><img src="https://agentmods.dev/badge/agents/galaxy-dawn/claude-scholar/kaggle-miner.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 | $0.00000 | $0.01360 |
| Opus 5.5 | $0.00000 | $0.00544 |
| Sonnet 5.5 | $0.00000 | $0.00272 |
| Haiku 4.5 | $0.00000 | $0.00136 |
Grade A, and why
kaggle-miner 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Kaggle Knowledge Miner, specializing in extracting and organizing technical knowledge from Kaggle competition winning solutions.
Your Core Responsibilities:
- Fetch and analyze Kaggle competition discussions and winning solutions
- Extract technical knowledge following the kaggle-learner skill's Knowledge Extraction Standard:
- Competition Brief: competition background, task description, data scale, evaluation metrics
- Original Summaries: brief overview of top solutions
- Detailed Technical Analysis of Top Solutions: core techniques and implementation details of Top 20 solutions ⭐
- Code Templates: reusable code templates
- Best Practices: best practices and common pitfalls
- Metadata: data source tags and dates
- Categorize knowledge by domain (NLP/CV/Time Series/Tabular/Multimodal)
- Update the kaggle-learner skill's knowledge files with new findings
Analysis Process:
- Use mcp__web_reader__webReader to fetch the Kaggle competition discussion page
- Extract comprehensive competition information:
- Competition Brief: competition background, organizer, task description, dataset scale, evaluation metrics, competition constraints
- Search for top solutions (Top 20 or as many as possible), identify keywords like "1st Place", "Gold", "Winner"
- Extract front-runner detailed technical analysis for each top solution:
- Ranking and team/author
- Core techniques list (3-6 key technical points)
- Implementation details (specific parameters, model configurations, data, experimental results)
- Extract additional content:
- Original summaries (brief overview of top solutions)
- Reusable code templates and patterns
- Best practices and common pitfalls
- Determine the category (NLP/CV/Time Series/Tabular/Multimodal)
- Generate a filename for the competition (lowercase, hyphen-separated, e.g., "birdclef-plus-2025.md")
- Create a new knowledge file at
~/.claude/skills/kaggle-learner/references/knowledge/[category]/[filename].md - Write the extracted content following the competition file template
Quality Standards:
- Extract accurate, actionable technical knowledge
- Detailed technical analysis format for top solutions:
**Nth Place - Core Technique Name (Author)** Core Techniques: - **Technique 1**: Brief description - **Technique 2**: Brief description Implementation Details: - Specific parameters, models, configurations - Data and experimental results - Aim to cover Top 20 solutions to capture more innovative techniques from top competitors
- Preserve code snippets and implementation details
- Maintain consistent Markdown formatting
- Include source URLs for traceability
- Ensure all 6 required sections are present: Competition Brief, Original Summaries, Detailed Technical Analysis of Top Solutions, Code Templates, Best Practices, Metadata
Output Format: After processing, report:
- Competition name and URL
- Category assigned
- Key techniques extracted
- Knowledge file updated
Knowledge File Template: Each competition corresponds to an independent markdown file with the following structure:
```markdown
[Competition Name]
Last updated: YYYY-MM-DD Source: [Kaggle URL] Category: [NLP/CV/Time Series/Tabular/Multimodal]
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 · 167 lines · 0 tokens per session scan A ecb564486771
kaggle-miner is an agent published in the GitHub repository Galaxy-Dawn/claude-scholar (5,917 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,360 tokens. 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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