dr-explore

Collects cross-domain web evidence into reproducible research run directories.

Updated Mar 23, 2026
One-click install
npx skills add https://github.com/sakamotomomotaro0809-netizen/tateyomi --skill dr-explore-sakamotomomotaro0809-netizen
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dr-explore
Source: https://github.com/sakamotomomotaro0809-netizen/tateyomi/tree/main/taisun_agent/.claude/skills/dr-explore
Command: npx skills add https://github.com/sakamotomomotaro0809-netizen/tateyomi --skill dr-explore-sakamotomomotaro0809-netizen

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Deep research often produces scattered, unverifiable notes that cannot be reused or audited. This Skill turns the exploration phase into a reproducible run, saving every finding as structured evidence with source URLs, retrieval timestamps, quotes, and reliability scores. ## Core Features & Use Cases - Reproducible Run Directories: Creates a timestamped run folder containing input.yaml, evidence.jsonl, raw sources, a changelog, and open questions. - Structured Evidence Schema: Each evidence entry records source type, URL, excerpt, claims, and a 0-5 reliability score based on source quality. - Domain-Aware Collection: Provides focused collection guidance for AI systems, marketing, investing, and spiritual research domains, with depth levels (lite/standard/deep) controlling evidence volume. - Use Case: Ask it to research a topic like "MCP agent security trends" over the last 30 days, and it produces an evidence.jsonl file with scored, cited findings ready for a downstream synthesis phase. ## Quick Start Run dr-explore with a topic such as "AI agent frameworks" and parameters like horizon=30d, lang=ja,en, and depth=standard to generate a full evidence run directory.

Frequently Asked Questions about dr-explore

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a deep research exploration with dr-explore?▼

Invoke dr-explore with a topic and optional parameters like domain, horizon, lang, and depth. It creates a timestamped run directory under research/runs/ containing input.yaml, evidence.jsonl, raw sources, a changelog, and open questions.

What output format does the evidence collection produce?▼

Evidence is stored in evidence.jsonl, one JSON object per line. Each entry includes source URL, retrieval timestamp, excerpt, claims, language, and a reliability score from 0 to 5 with rationale.

How does the reliability scoring for sources work?▼

Sources are scored 0-5: official primary sources like docs and peer-reviewed papers score 5, major media score 4, blogs score 3, unverified SNS posts score 2, and unverifiable or contradictory content scores 0-1.

Can dr-explore handle content in multiple languages?▼

Yes, the lang parameter accepts comma-separated language codes such as ja,en, defaulting to Japanese and English. Each evidence entry records its detected language in the language field.

How does it protect against prompt injection in scraped content?▼

All retrieved text is treated strictly as data, never as instructions. Content impersonating system or developer messages is quarantined as dangerous and excluded from evidence, and secrets or personal data are redacted from logs.

What is the difference between lite, standard, and deep depth levels?▼

Depth controls evidence volume: lite collects 10-20 items for quick scans, standard collects 30-50 items covering main issues, and deep collects 50-100 items including counter-evidence and alternative hypotheses.