deep-research

Solid

Multi-source web research methodology: retrieve-vs-answer gate, complexity-scaled search budget, query craft, primary-source preference, source-conflict skepticism, adversarial verification, attribution-without-reproduction. Triggers: deep research, multi-source, web research, synthesize sources, cross-reference, fact synthesis, source verification.

AI & Automation 161 stars 21 forks Updated today MIT

Install

View on GitHub

Quality Score: 90/100

Stars 20%
74
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Deep Research This is the web / multi-source counterpart to `research-mastery`. That skill is KB-first: it answers from the project's own knowledge base and only reaches outward when the KB comes up empty. This one governs what happens once you are already out on the open web pulling from many independent sources and have to weave them into one trustworthy answer. It is a method, not a fetcher — it does not retrieve anything by itself. You supply the search and fetch tools (built-in `WebSearch` / `WebFetch`, or the runtime `deep-research` command); this skill tells you how to spend them and how hard to doubt what comes back. ## Retrieve-vs-Answer Gate Run this gate before you spend a single search: 1. **Is the answer already in the KB or your own context?** Then this is not a deep-research job — hand it to `research-mastery` (which checks RAG-MCP first) or just answer. 2. **Is it one stable fact** with a single obvious authority (a constant, a published spec value, a definition that does not move)? One targeted lookup, confirm, done. Do not open a research campaign. 3. **Does it need several independent sources reconciled, or is it contested, recent, or moving?** That is the case this skill exists for. Continue. Skipping this gate is the most common failure: people fan out ten searches on a question that one source already settled, or worse, answer a contested question from memory because it "felt known." ## Scale Effort to Complexity Match the search budget to the q...

Details

Author
softspark
Repository
softspark/ai-toolkit
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category