Openfang researcher-hand-skill
Expert knowledge for AI deep research — methodology, source evaluation, search optimization, cross-referencing, synthesis, and citation formats
git clone https://github.com/RightNow-AI/openfang
T=$(mktemp -d) && git clone --depth=1 https://github.com/RightNow-AI/openfang "$T" && mkdir -p ~/.claude/skills && cp -r "$T/crates/openfang-hands/bundled/researcher" ~/.claude/skills/rightnow-ai-openfang-researcher-hand-skill && rm -rf "$T"
crates/openfang-hands/bundled/researcher/SKILL.mdDeep Research Expert Knowledge
Research Methodology
Research Process (5 phases)
- Define: Clarify the question, identify what's known vs unknown, set scope
- Search: Systematic multi-strategy search across diverse sources
- Evaluate: Assess source quality, extract relevant data, note limitations
- Synthesize: Combine findings into coherent answer, resolve contradictions
- Verify: Cross-check critical claims, identify remaining uncertainties
Question Types & Strategies
| Question Type | Strategy | Example |
|---|---|---|
| Factual | Find authoritative primary source | "What is the population of Tokyo?" |
| Comparative | Multi-source balanced analysis | "React vs Vue for large apps?" |
| Causal | Evidence chain + counterfactuals | "Why did Theranos fail?" |
| Predictive | Trend analysis + expert consensus | "Will quantum computing replace classical?" |
| How-to | Step-by-step from practitioners | "How to set up a Kubernetes cluster?" |
| Survey | Comprehensive landscape mapping | "What are the options for vector databases?" |
| Controversial | Multiple perspectives + primary sources | "Is remote work more productive?" |
Decomposition Technique
Complex questions should be broken into sub-questions:
Main: "Should our startup use microservices?" Sub-questions: 1. What are microservices? (definitional) 2. What are the benefits vs monolith? (comparative) 3. What team size/stage is appropriate? (contextual) 4. What are the operational costs? (factual) 5. What do similar startups use? (case studies) 6. What are the migration paths? (how-to)
CRAAP Source Evaluation Framework
Currency
- When was it published or last updated?
- Is the information still current for the topic?
- Are the links functional?
- For technology topics: anything >2 years old may be outdated
Relevance
- Does it directly address your question?
- Who is the intended audience?
- Is the level of detail appropriate?
- Would you cite this in your report?
Authority
- Who is the author? What are their credentials?
- What institution published this?
- Is there contact information?
- Does the URL domain indicate authority? (.gov, .edu, reputable org)
Accuracy
- Is the information supported by evidence?
- Has it been reviewed or refereed?
- Can you verify the claims from other sources?
- Are there factual errors, typos, or broken logic?
Purpose
- Why does this information exist?
- Is it informational, commercial, persuasive, or entertainment?
- Is the bias clear or hidden?
- Does the author/organization benefit from you believing this?
Scoring
A (Authoritative): Passes all 5 CRAAP criteria B (Reliable): Passes 4/5, minor concern on one C (Useful): Passes 3/5, use with caveats D (Weak): Passes 2/5 or fewer F (Unreliable): Fails most criteria, do not cite
Search Query Optimization
Query Construction Techniques
Exact phrase:
"specific phrase" — use for names, quotes, error messages
Site-specific: site:domain.com query — search within a specific site
Exclude: query -unwanted_term — remove irrelevant results
File type: filetype:pdf query — find specific document types
Recency: query after:2024-01-01 — recent results only
OR operator: query (option1 OR option2) — broaden search
Wildcard: "how to * in python" — fill-in-the-blank
Multi-Strategy Search Pattern
For each research question, use at least 3 search strategies:
- Direct: The question as-is
- Authoritative:
site:gov OR site:edu OR site:org [topic] - Academic:
or[topic] research paper [year]site:arxiv.org [topic] - Practical:
or[topic] guide
or[topic] tutorial[topic] how to - Data:
or[topic] statistics[topic] data [year] - Contrarian:
or[topic] criticism
or[topic] problems[topic] myths
Source Discovery by Domain
| Domain | Best Sources | Search Pattern |
|---|---|---|
| Technology | Official docs, GitHub, Stack Overflow, engineering blogs | , |
| Science | PubMed, arXiv, Nature, Science | , |
| Business | SEC filings, industry reports, HBR | , |
| Medicine | PubMed, WHO, CDC, Cochrane | |
| Legal | Court records, law reviews, statute databases | , |
| Statistics | Census, BLS, World Bank, OECD | |
| Current events | Reuters, AP, BBC, primary sources | , |
Cross-Referencing Techniques
Verification Levels
Level 1: Single source (unverified) → Mark as "reported by [source]" Level 2: Two independent sources agree (corroborated) → Mark as "confirmed by multiple sources" Level 3: Primary source + secondary confirmation (verified) → Mark as "verified — primary source: [X]" Level 4: Expert consensus (well-established) → Mark as "widely accepted" or "scientific consensus"
Contradiction Resolution
When sources disagree:
- Check which source is more authoritative (CRAAP scores)
- Check which is more recent (newer may have updated info)
- Check if they're measuring different things (apples vs oranges)
- Check for known biases or conflicts of interest
- Present both views with evidence for each
- State which view the evidence better supports (if clear)
- If genuinely uncertain, say so — don't force a conclusion
Synthesis Patterns
Narrative Synthesis
The evidence suggests [main finding]. [Source A] found that [finding 1], which is consistent with [Source B]'s observation that [finding 2]. However, [Source C] presents a contrasting view: [finding 3]. The weight of evidence favors [conclusion] because [reasoning]. A key limitation is [gap or uncertainty].
Structured Synthesis
FINDING 1: [Claim] Evidence for: [Source A], [Source B] — [details] Evidence against: [Source C] — [details] Confidence: [high/medium/low] Reasoning: [why the evidence supports this finding] FINDING 2: [Claim] ...
Gap Analysis
After synthesis, explicitly note:
- What questions remain unanswered?
- What data would strengthen the conclusions?
- What are the limitations of the available sources?
- What follow-up research would be valuable?
Citation Formats
Inline URL
According to a 2024 study (https://example.com/study), the effect was significant.
Footnotes
According to a 2024 study[1], the effect was significant. --- [1] https://example.com/study — "Title of Study" by Author, Published Date
Academic (APA)
In-text: (Smith, 2024) Reference: Smith, J. (2024). Title of the article. *Journal Name*, 42(3), 123-145. https://doi.org/10.xxxx
For web sources (APA):
Author, A. A. (Year, Month Day). Title of page. Site Name. https://url
Numbered References
According to recent research [1], the finding was confirmed by independent analysis [2]. ## References 1. Author (Year). Title. URL 2. Author (Year). Title. URL
Output Templates
Brief Report
# [Question] **Date**: YYYY-MM-DD | **Sources**: N | **Confidence**: high/medium/low ## Answer [2-3 paragraph direct answer] ## Key Evidence - [Finding 1] — [source] - [Finding 2] — [source] - [Finding 3] — [source] ## Caveats - [Limitation or uncertainty] ## Sources 1. [Source](url) 2. [Source](url)
Detailed Report
# Research Report: [Question] **Date**: YYYY-MM-DD | **Depth**: thorough | **Sources Consulted**: N ## Executive Summary [1 paragraph synthesis] ## Background [Context needed to understand the findings] ## Methodology [How the research was conducted, what was searched, how sources were evaluated] ## Findings ### [Sub-question 1] [Detailed findings with inline citations] ### [Sub-question 2] [Detailed findings with inline citations] ## Analysis [Synthesis across findings, patterns identified, implications] ## Contradictions & Open Questions [Areas of disagreement, gaps in knowledge] ## Confidence Assessment [Overall confidence level with reasoning] ## Sources [Full bibliography in chosen citation format]
Cognitive Bias in Research
Be aware of these biases during research:
-
Confirmation bias: Favoring information that confirms your initial hypothesis
- Mitigation: Explicitly search for disconfirming evidence
-
Authority bias: Over-trusting sources from prestigious institutions
- Mitigation: Evaluate evidence quality, not just source prestige
-
Anchoring: Fixating on the first piece of information found
- Mitigation: Gather multiple sources before forming conclusions
-
Selection bias: Only finding sources that are easy to access
- Mitigation: Vary search strategies, check non-English sources
-
Recency bias: Over-weighting recent publications
- Mitigation: Include foundational/historical sources when relevant
-
Framing effect: Being influenced by how information is presented
- Mitigation: Look at raw data, not just interpretations
Domain-Specific Research Tips
Technology Research
- Always check the official documentation first
- Compare documentation version with the latest release
- Stack Overflow answers may be outdated — check the date
- GitHub issues/discussions often have the most current information
- Benchmarks without methodology descriptions are unreliable
Business Research
- SEC filings (10-K, 10-Q) are the most reliable public company data
- Press releases are marketing — verify claims independently
- Analyst reports may have conflicts of interest — check disclaimers
- Employee reviews (Glassdoor) provide internal perspective but are biased
Scientific Research
- Systematic reviews and meta-analyses are strongest evidence
- Single studies should not be treated as definitive
- Check if findings have been replicated
- Preprints have not been peer-reviewed — note this caveat
- p-values and effect sizes both matter — not just "statistically significant"