Imported from majiayu000/claude-skill-registry (
skills/analysis/corpus-discovery-dialogue/SKILL.md). Install upstream withnpx skills add majiayu000/claude-skill-registry --skill corpus-discovery-dialogue. Copyright stays with the author.
Corpus Discovery Dialogue
Purpose
Transform vague interest in a text corpus into concrete, answerable research questions with clear analytical roadmaps. Prevents aimless exploration and "fishing expedition" research.
Core innovation: Uses Socratic questioning to help researchers articulate what they don't yet consciously know they want to discover.
Core principle: Discovery before execution. Research questions emerge through dialogue, not pre-formed declarations. The researcher makes all interpretive decisions; this skill guides the process.
When to Use
Invocation Triggers:
- "Help me explore this corpus"
- "What questions should I ask of these [texts/reviews/documents]?"
- "Guide me through corpus analysis"
- "I have text data but don't know where to start"
- "Design a research plan for this corpus"
Use This When:
- Starting corpus analysis with unclear research questions
- Have data but uncertain what questions it can answer
- Need structure for exploratory research
- Want to formulate hypothesis before diving into analysis
Skip This When:
- Research questions already clearly defined
- Conducting hypothesis testing (use hypothesis-testing-dialogue instead)
- Interpreting existing analysis results (use analysis-interpretation-dialogue instead)
Input Requirements
Required:
- Corpus description (domain, approximate size, format)
- Access to sample texts (at least 3-5 examples for grounding)
Optional:
corpus_file_path- If corpus is in accessible format (JSON, CSV, TXT)domain_context- Background information about the corpus domainresearch_constraints- Time, computational resources, publication goals
If corpus not yet loaded: Ask: "Can you share 3-5 example texts from your corpus so I can understand the content?"
Announce at Start
I'm using the Corpus Discovery Dialogue skill to help you formulate research questions
and design an analytical approach for your corpus.
This process takes 20-40 minutes and involves five phases:
1. Understanding your corpus (5-10 min)
2. Exploring your interests (5-10 min)
3. Formulating questions (5-10 min)
4. Mapping methodologies (5-10 min)
5. Creating research roadmap (5-10 min)
We'll move at your pace, with confirmation checkpoints along the way.
Let's begin by understanding what you're working with.
The Process
Phase 0: Context Establishment & Sample Review
Goal: Establish factual baseline about corpus and ground dialogue in actual texts
Actions:
-
Request corpus samples (if not already provided):
To ground our conversation in your actual data, please share 3-5 example texts from your corpus. This helps me: - Understand the domain and content - Suggest appropriate analytical methods - Generate realistic research questions - Ground our dialogue in evidence You can paste the texts directly, provide file paths, or describe the corpus with representative excerpts. -
Review samples and extract initial observations:
- Document length range (word counts)
- Writing style (formal, informal, technical, narrative)
- Content type (descriptive, analytical, evaluative)
- Domain-specific terminology
- Temporal markers (dates, time references)
- Structural patterns
-
Present initial observations for validation:
## Initial Observations from Sample Texts Based on the examples you shared, I notice: **Content Type:** [Theater reviews / Business reports / etc.] **Writing Style:** [Evaluative, analytical, narrative-driven] **Length Range:** [~300-700 words per document] **Domain Terminology:** [Performance, staging, direction, etc.] **Time References:** [Dates present, suggest temporal corpus] Does this match your understanding of the full corpus? - Yes → These samples are representative - No → What's different about the full corpus?
Wait for user confirmation before proceeding to Phase 1.
Phase 1: Corpus Understanding (5-10 minutes)
Goal: Establish factual baseline about the corpus through structured inquiry
Method: Ask questions sequentially, one at a time, with structured choices
1A. Basic Characteristics
What kind of texts make up your corpus?
A) Literary texts (novels, poetry, plays, reviews)
B) Professional writing (business documents, technical reports)
C) Social media / conversational data (tweets, chat logs, forum posts)
D) Academic writing (papers, dissertations, grant proposals)
E) Historical documents (letters, diaries, archival materials)
F) Other: [let me describe]
This helps me understand what analytical approaches are appropriate.
Wait for response. Based on answer, follow up with domain-specific clarification if needed.
1B. Corpus Size
Approximately how large is your corpus?
A) Small (1-50 documents, <50K words)
- Suitable for: Close reading + computational validation
- Methods: Manual review with computational support
B) Medium (50-500 documents, 50K-500K words)
- Suitable for: Mixed methods (computational + selective manual review)
- Methods: Topic modeling, sentiment analysis, entity extraction
C) Large (500-5000 documents, 500K-5M words)
- Suitable for: Computational methods with manual validation
- Methods: Full NLP pipeline, network analysis, temporal tracking
D) Very Large (5000+ documents, 5M+ words)
- Suitable for: Fully automated computational approaches
- Methods: Large-scale topic modeling, trend analysis, statistical patterns
E) I'm not sure yet
Why this matters: Corpus size determines which analytical methods are appropriate
and how much manual validation is feasible.
Wait for response.
1C. Temporal Span
Do your texts span a time period?
A) Yes, specific date range: [YYYY-MM-DD to YYYY-MM-DD]
B) Yes, approximate span: [e.g., "1990s to 2020s"]
C) No, all from same time period
D) Unknown / mixed temporal markers
If yes: Are you interested in how patterns change over time?
- Very interested (temporal evolution is central question)
- Somewhat interested (might explore if patterns emerge)
- Not interested (time is just a descriptor, not analytic focus)
Wait for response. If temporal interest indicated, note for Phase 3 question generation.
1D. Authorship
Who created these texts?
A) Single author (one person's writing over time)
- Enables: Voice evolution, style development, thematic shifts
B) Small group (2-10 identified authors)
- Enables: Comparative analysis, author attribution, collaborative patterns
C) Large group (institutional/collective authorship)
- Enables: Consensus analysis, organizational voice, policy evolution
D) Anonymous / crowd-sourced (many unknown authors)
- Enables: Cultural patterns, collective discourse, emergence
E) Mixed / varies
Why this matters: Authorship affects whether you're studying individual voice evolution,
group consensus, or broader cultural patterns.
Wait for response.
1E. Structural Format
What format is your corpus in?
A) Structured data (JSON, CSV, database) with metadata
- Metadata available: [dates, categories, author info, etc.]
B) Plain text files (.txt, .md)
- Organization: [by date / by category / by author / other]
C) Rich documents (PDF, DOCX) needing extraction
- Extraction needed: Yes, will require preprocessing
D) Web pages / HTML needing scraping
- Scraping needed: Yes, will require web extraction
E) Mixed formats
F) Not yet digitized (physical documents)
Do you have metadata? (dates, categories, author info, etc.)
- Yes, extensive metadata (multiple fields)
- Yes, basic metadata (dates only)
- No metadata (just text)
Wait for response.
1F. Data Access Confirmation
Do you currently have access to the full corpus, or are you still gathering it?
A) Full corpus ready (can begin analysis anytime)
B) Partial corpus ready (~50-75% collected)
C) Still gathering (in early collection phase)
D) Need help acquiring/formatting the data
If gathering or formatting needed, we can adjust timeline expectations.
Wait for response.
Output: Corpus Profile
After all Phase 1 questions answered, synthesize into structured profile:
## Corpus Profile
**Domain:** [Theater criticism / Business reports / etc.]
**Type:** [Literary reviews / Professional documents / etc.]
**Size:** [Medium corpus: ~464 documents, ~250K words]
**Temporal Span:** [2010-2025 (15 years)]
**Authorship:** [Single author]
**Format:** [JSON with date metadata]
**Metadata Available:** [Dates, titles, some categories]
**Data Status:** [Ready for analysis / Partially ready / Still gathering]
**Analytical Implications:**
- Size supports: [Topic modeling, sentiment analysis, entity extraction, network analysis]
- Temporal span enables: [Evolution tracking, trend analysis, period comparison]
- Single authorship enables: [Voice evolution, style development, personal pattern tracking]
- Metadata supports: [Temporal aggregation, categorical comparison]
Does this accurately describe your corpus?
- Yes → Proceed to Phase 2
- No → What should I correct?Wait for user confirmation before proceeding.
If user indicates corrections:
- Ask clarifying questions
- Update profile
- Re-present for confirmation
- Only proceed when user explicitly confirms
Phase 2: Interest Exploration (5-10 minutes)
Goal: Uncover researcher's tacit interests, assumptions, and motivations
Method: Ask open-ended and structured questions to reveal what researcher hopes to discover
Important: This phase reveals assumptions worth testing. "Surprise" and "disappointment" questions make tacit expectations explicit.
2A. Initial Draw
What drew you to this corpus?
A) Personal connection (my own writing, my field, my community)
B) Historical significance (important cultural/social moment)
C) Theoretical interest (test a hypothesis, apply a framework)
D) Practical need (dissertation, publication requirement)
E) Curiosity (something seemed interesting but not sure what)
F) Other: [describe]
Tell me more about what sparked your interest.
Wait for response. Allow open-ended elaboration.
2B. Puzzles and Intrigues
What puzzles or intrigues you about these texts?
Think about:
- Something that seems inconsistent or contradictory
- A pattern you notice but can't quite articulate
- A change over time you sense but haven't confirmed
- A question others haven't asked but should
- Something that doesn't match your expectations
Share 1-3 puzzles or curiosities. These don't have to be fully formed —
vague hunches are exactly what we want to clarify.
Wait for response. Allow open-ended exploration. Probe for specifics if too vague:
If vague, ask:
That's interesting. Can you give me an example from the texts where you noticed this?
2C. Surprise Exploration
What would surprise you to discover in this corpus?
A) That patterns you expected are absent
B) That unexpected patterns dominate
C) That the corpus contradicts prevailing wisdom in your field
D) That temporal changes are dramatic (or non-existent)
E) That relationships you assumed don't exist (or that unexpected ones do)
F) Something else: [describe]
What would genuinely surprise you?
This reveals assumptions worth testing — surprises often indicate tacit hypotheses.
Wait for response.
Follow-up:
Why would that surprise you? What do you currently assume is true?
This makes assumptions explicit and testable.
2D. Disappointment Exploration
What would disappoint you NOT to find?
A) Evidence for a pattern you suspect exists
B) Temporal evolution (if corpus seems static)
C) Distinctive voice or style markers
D) Connections between elements (entities, themes, language)
E) Something you could publish or present
F) Confirmation of your field's prevailing theories
What discovery would feel like the project "didn't pay off"?
Wait for response.
This reveals researcher's hopes and helps ensure questions address genuine interests.
2E. Stakes Clarification
What are the stakes of this research?
A) Academic (dissertation chapter, publication, tenure case)
- Who's your audience? [Field specialists / interdisciplinary / general public]
B) Professional (improve practice, inform policy, guide decisions)
- Who will use these findings?
C) Personal (understand my own work, document my evolution)
- What will this help you understand?
D) Cultural (preserve/interpret cultural moment, community history)
- What's at risk if this story isn't told?
E) Methodological (test new analytical approaches, advance methods)
- What methodological contribution does this make?
How will you use these findings? Who cares about what you discover?
Wait for response.
Output: Interest Map
After Phase 2 questions, synthesize researcher's interests into structured map:
## Interest Map
**Primary Draw:** [Personal connection - your own theater criticism over 15 years]
**Puzzles & Intrigues:**
1. [Sense that your critical voice has evolved but not sure how]
2. [Notice some plays/venues appear repeatedly - what patterns exist?]
3. [Feel like your language has changed - more/less positive? Different vocabulary?]
**Surprising Discoveries (would reveal assumptions):**
- Surprise if: [No temporal evolution - assumes change occurred]
- Surprise if: [Sentiment trends opposite to memory - assumes accurate self-awareness]
- Current assumption: [You believe you've become more/less positive over time]
**Disappointing Non-Discoveries:**
- Disappointed if: [No distinctive voice markers - wants evidence of unique style]
- Disappointed if: [No entity relationship patterns - suspects network exists]
**Stakes:** [Personal + Professional - understand critical evolution, possibly publish]
**Audience:** [Theater practitioners, other critics, potentially academic audience]
**Implicit Hypotheses Worth Testing:**
1. [Voice has evolved over 15 years - testable with stylistic/sentiment analysis]
2. [Certain entities are central to network - testable with entity extraction + network analysis]
3. [Language/sentiment has shifted - testable with sentiment analysis + lexicon comparison]
Does this capture your interests and assumptions?
- Yes → Proceed to Phase 3
- No → What should I adjust?Wait for user confirmation.
If adjustments needed:
- Clarify misunderstandings
- Update map
- Re-present
- Proceed only when user confirms
Phase 3: Question Formulation (5-10 minutes)
Goal: Transform interests into concrete, answerable research questions
Method: Generate 8-12 candidate questions across categories, then guide selection
Key Principle: Questions must be:
- Answerable - Clear evidence would resolve the question
- Specific - Not "what patterns exist?" but "what thematic patterns exist?"
- Scoped - Appropriate for corpus size and format
- Interesting - Align with Phase 2 interests
Present Candidate Questions
Based on Corpus Profile (Phase 1) and Interest Map (Phase 2), generate 8-12 questions across these categories:
Template:
## Candidate Research Questions
Based on your corpus and interests, here are potential research questions organized by type.
I've tailored these to your [corpus size], [temporal span], [authorship type], and your
interest in [key interests from Phase 2].
---
### Category 1: Temporal Questions (Evolution Over Time)
[**Include ONLY if corpus has temporal span AND user indicated temporal interest**]
These questions examine how patterns change across [time span].
**Q1a: How has [specific aspect from Phase 2] evolved from [start year] to [end year]?**
**Analytical Approach:** [Method name]
- Primary tool: [Tool name and what it does]
- Procedure: [3-4 step summary]
**What This Reveals:** [What you'd learn]
**Evidence Type:** [What outputs would look like]
- [Output 1]: [Description]
- [Output 2]: [Description]
**Validation Strategy:** [How to verify findings]
- [Validation method 1]
- [Validation method 2]
**Example for your corpus:**
> **Q1a: How has the emotional tenor of your theater criticism evolved 2010-2025?**
>
> **Analytical Approach:** Sentiment analysis with temporal tracking
> - Primary tool: VADER (lexicon-based) + Transformers (distilbert-based)
> - Procedure:
> 1. Score each review with both tools
> 2. Aggregate sentiment by year
> 3. Visualize trends with confidence intervals
> 4. Identify turning points or steady trends
>
> **What This Reveals:** Whether you've become more/less positive, whether enthusiasm
> has changed, if there are distinct periods in your critical stance
>
> **Evidence Type:**
> - Sentiment scores aggregated by year (line graph with trend)
> - Statistical tests for significant changes (before/after comparison)
> - Representative reviews from high/low sentiment periods
>
> **Validation Strategy:**
> - Manual review of 20 highest-sentiment reviews (check for sarcasm, irony)
> - Manual review of 20 lowest-sentiment reviews (check for genuine negativity)
> - Compare with external events (theater trends, personal life events if known)
> - Cross-tool validation (VADER vs Transformer correlation)
>
> **Confidence:** High - sentiment analysis is well-established for this corpus size
**Q1b: How have [thematic element from Phase 2] changed over time?**
[Similar structure as Q1a]
[Present 2-3 temporal questions if applicable]
---
### Category 2: Thematic Questions (Pattern Discovery)
These questions identify dominant themes, concepts, and patterns across the corpus.
**Q2a: What are the dominant [domain-specific themes] in your [corpus type]?**
**Analytical Approach:** Topic modeling
**Example for your corpus:**
> **Q2a: What are the dominant critical frameworks in your theater reviews?**
>
> **Analytical Approach:** Topic modeling with BERTopic
> - Primary tool: BERTopic (semantic topic modeling)
> - Procedure:
> 1. Generate document embeddings (sentence transformers)
> 2. Cluster documents by semantic similarity
> 3. Extract representative terms per topic
> 4. Label topics based on term analysis + manual review
>
> **What This Reveals:** Whether you emphasize performance, direction, writing,
> social context, production values, audience experience, etc. What critical
> lenses dominate your work.
>
> **Evidence Type:**
> - Topic model with 8-15 coherent topics
> - Representative reviews per topic (example documents)
> - Topic prevalence across corpus (distribution)
> - Topic keywords and phrases (c-TF-IDF scores)
>
> **Validation Strategy:**
> - Manual review of 5 reviews per topic (coherence check)
> - Topic distinctiveness assessment (are topics meaningfully different?)
> - Compare with conscious critical approach (does this match your self-understanding?)
> - Check for junk topics (noise clusters, incoherent groupings)
>
> **Confidence:** High - BERTopic performs well on this corpus size
**Q2b: What [specific content type] appears most frequently?**
[Similar structure]
[Present 2-3 thematic questions]
---
### Category 3: Relational Questions (Networks & Connections)
These questions explore relationships between entities (people, places, works, concepts).
**Q3a: What relationships exist between [entity types]?**
**Analytical Approach:** Entity extraction + network analysis
**Example for your corpus:**
> **Q3a: What plays, venues, and artists are central to your critical network?**
>
> **Analytical Approach:** Named entity recognition + co-occurrence network analysis
> - Primary tool: spaCy NER (entity extraction) + NetworkX (network construction)
> - Procedure:
> 1. Extract named entities (people, organizations, works)
> 2. Build co-occurrence network (entities mentioned together)
> 3. Calculate centrality metrics (which entities are most central)
> 4. Identify clusters (which entities appear together frequently)
>
> **What This Reveals:** Which plays/venues/artists you review most frequently,
> which entities cluster together (e.g., small theater ecosystem vs. major venues),
> what the structure of your critical attention looks like
>
> **Evidence Type:**
> - Network visualization (nodes = entities, edges = co-occurrence)
> - Centrality rankings (top 20-50 entities by importance)
> - Cluster analysis (community detection results)
> - Temporal network evolution (if time-based)
>
> **Validation Strategy:**
> - Manual review of top 50 entities (correctly identified?)
> - Check for entity normalization issues ("CTG" vs "Center Theatre Group")
> - Validate co-occurrence patterns (do relationships make sense?)
> - Cross-reference with LA theater ecosystem (external validation)
>
> **Confidence:** Medium-High - NER works well but may need manual cleanup
[Present 1-2 relational questions]
---
### Category 4: Stylistic Questions (Language & Voice)
These questions examine how language is used distinctively across corpus.
**Q4a: What language distinguishes [period A] from [period B]?**
**Analytical Approach:** Comparative keyness analysis + collocation extraction
**Example for your corpus:**
> **Q4a: What language distinguishes your early (2010-2015) from recent (2020-2025) criticism?**
>
> **Analytical Approach:** Scattertext visualization + keyness testing
> - Primary tool: Scattertext (contrastive lexicon analysis)
> - Procedure:
> 1. Split corpus into early vs recent periods
> 2. Calculate keyness (log-likelihood) for all terms
> 3. Identify distinctive vocabulary for each period
> 4. Extract collocations (multi-word phrases) per period
>
> **What This Reveals:** Vocabulary shifts, new terminology adopted, phrases
> abandoned, evolving critical language
>
> **Evidence Type:**
> - Scattertext visualization (interactive chart showing distinctive terms)
> - Keyness-ranked term lists (statistically distinctive vocabulary)
> - Collocation frequency tables (signature multi-word phrases)
> - Example sentences showing terms in context
>
> **Validation Strategy:**
> - Manual review of distinctive terms (do they feel authentic?)
> - Check for stopword contamination (are distinctive terms meaningful?)
> - Verify collocation context (are phrases used consistently?)
> - Compare with self-reported language evolution
>
> **Confidence:** High - keyness testing is statistically robust
**Q4b: What are the signature phrases or linguistic markers of [corpus type]?**
[Similar structure]
[Present 2-3 stylistic questions]
---
### Category 5: Comparative Questions (Contrast & Difference)
[**Include ONLY if corpus has clear subcategories or comparison groups**]
These questions compare different subsets of the corpus.
**Q5a: How does [subset A] differ from [subset B]?**
[Tailor based on corpus structure - e.g., early vs late, category A vs B, positive vs negative]
[Present 1-2 comparative questions if applicable]
---
## Question Selection
Now that you've seen 8-12 possible research questions, let's select the ones
that align best with your interests and corpus capabilities.
**Selection Criteria:**
1. **Interest Alignment:** Which questions address your puzzles from Phase 2?
2. **Corpus Fit:** Which are answerable with your corpus size/format?
3. **Method Clarity:** Which have clear analytical approaches?
4. **Meaningful Findings:** Which would produce results you care about?
5. **Feasibility:** Which can you realistically complete given constraints?
**Select your top 3-5 research questions** by considering these criteria.
You can:
- Select questions as written
- Modify questions to better fit your interests
- Combine elements from multiple questions
- Propose entirely new questions inspired by these examples
Which 3-5 questions would you like to pursue?
Please indicate by question number (Q1a, Q2b, etc.) or describe modifications.Wait for user to select/modify questions.
If user proposes modifications:
- Rewrite questions incorporating their changes
- Check if analytical approach still works
- Present modified questions for confirmation
If user wants to combine questions:
- Synthesize into coherent combined question
- Ensure combined question is still answerable
- Adjust analytical approach if needed
Output: Research Question Framework (Preliminary)
After user selects questions, create structured framework:
## Selected Research Questions
### Primary Questions (User-Selected)
**RQ1:** [Question text]
- **Priority:** High
- **Rationale:** [Why this question matters to you - drawn from Phase 2]
- **Addresses Interest:** [Links to specific puzzle/intrigue from Interest Map]
- **Expected Contribution:** [What this adds to knowledge/understanding]
**RQ2:** [Question text]
- **Priority:** High
- **Rationale:** [Why this question matters]
- **Addresses Interest:** [Links to Interest Map]
- **Expected Contribution:** [What this adds]
**RQ3:** [Question text]
- **Priority:** Medium/High
- **Rationale:** [Why this question matters]
- **Addresses Interest:** [Links to Interest Map]
- **Expected Contribution:** [What this adds]
[Continue for all selected questions]
### Deferred Questions (Interesting but Not Priority)
[Questions user found interesting but not immediate priority - keep for potential future exploration]
**Dependency Analysis:**
- **Independent questions** (can pursue in any order): [List]
- **Sequential questions** (one informs another): [List with order]
- **Complementary questions** (strengthen each other if pursued together): [List]
Does this prioritization feel right?
- Yes → Proceed to Phase 4
- No → Let's reorder or reconsiderWait for confirmation.
Phase 4: Methodological Mapping (5-10 minutes)
Goal: Map each selected research question to specific analytical approaches with clear implementation paths
Method: For each question, specify tools, procedures, evidence, and validation
Important: Methods must match corpus size, user expertise, and available resources.
Map Each Research Question
Template (repeat for each RQ):
## Methodological Roadmap
For each research question, I'll propose a specific analytical approach with tools,
procedures, evidence types, and validation strategies.
This roadmap is designed for your corpus: [size], [format], [temporal properties].
---
### RQ1: [Question text]
**Analytical Approach:** [Method name - e.g., Sentiment Analysis with Temporal Tracking]
#### Tools Required
**Primary Tool:**
- **Name:** [e.g., VADER]
- **What it does:** [Lexicon-based sentiment scoring from -1 to +1]
- **Why this tool:** [Fast, interpretable, works well on evaluative text]
- **Installation:** `pip install vaderSentiment`
- **Estimated cost:** Free (open source)
**Secondary Tool:**
- **Name:** [e.g., Transformers (distilbert)]
- **What it does:** [Neural sentiment classification]
- **Why this tool:** [Captures nuance, handles sarcasm better]
- **Installation:** `pip install transformers`
- **Estimated cost:** Free (open source, runs locally)
**Validation Tool:**
- **Name:** [Manual review spreadsheet]
- **What it does:** [Human verification of computational results]
- **Why this tool:** [Ensures accuracy, catches model errors]
#### Procedure (Step-by-Step)
1. **Data Preparation** [Estimated time: 1-2 hours]
- Load corpus into analysis environment (Python/R)
- Clean text (minimal - preserve punctuation for sentiment)
- Extract temporal metadata (dates)
- Validate data quality (no missing values, encoding issues)
2. **Sentiment Scoring** [Estimated time: 2-3 hours]
- Score each document with VADER (compound score)
- Score each document with Transformer (positive probability)
- Store scores with document IDs and dates
3. **Temporal Aggregation** [Estimated time: 1-2 hours]
- Group scores by year (or month if fine-grained analysis needed)
- Calculate mean sentiment per period
- Calculate confidence intervals (standard deviation, standard error)
- Identify outliers (documents with extreme scores)
4. **Trend Analysis** [Estimated time: 1-2 hours]
- Visualize sentiment over time (line graph with error bars)
- Test for significant trends (linear regression, Mann-Kendall test)
- Identify change points (periods where trend shifts)
- Compare VADER vs Transformer results (correlation check)
5. **Validation** [Estimated time: 2-3 hours]
- Manual review of 20 highest-sentiment documents
- Manual review of 20 lowest-sentiment documents
- Check for sarcasm, irony, or misclassification
- Document validation findings
6. **Interpretation** [Estimated time: 1-2 hours]
- Write interpretation of findings
- Connect to research question
- Note limitations and caveats
#### Evidence This Produces
**Quantitative Evidence:**
- Sentiment score dataset (CSV: document_id, date, vader_score, transformer_score)
- Temporal aggregation table (year, mean_sentiment, std_dev, n_documents)
- Trend statistics (slope, p-value, R-squared)
- Correlation metrics (VADER vs Transformer agreement)
**Visual Evidence:**
- Sentiment-over-time line graph with confidence intervals
- Distribution plots (sentiment score histograms per period)
- Scatter plot (VADER vs Transformer comparison)
- Change point visualization (if applicable)
**Qualitative Evidence:**
- Example documents from high/low sentiment periods (with scores)
- Validation notes from manual review
- Interpretation connecting scores to content
**What This Shows:**
- Whether sentiment has increased/decreased over time
- How stable or volatile sentiment is
- Periods of particularly high/low sentiment
- Reliability of computational sentiment analysis for this corpus
#### Validation Strategy
**Computational Validation:**
- Cross-tool agreement (VADER vs Transformer correlation > 0.7 indicates reliability)
- Outlier analysis (investigate documents with divergent scores)
- Baseline comparison (is sentiment range plausible for this domain?)
**Manual Validation:**
- Review 40 documents (20 high, 20 low sentiment)
- Code each as: Correctly classified / Misclassified / Ambiguous
- Acceptable error rate: <20% misclassified
- If error rate high, adjust method or caveat findings
**External Validation:**
- Compare sentiment trends with known events (e.g., theater closures, cultural moments)
- Cross-reference with qualitative self-reflection (does trend match memory?)
- Check against field knowledge (do findings align with domain expertise?)
**Alternative Explanation Check:**
- Could sentiment change reflect selection bias (reviewing different types of shows)?
- Could it reflect linguistic drift (changing language norms, not genuine sentiment)?
- Could it reflect method artifacts (tool limitations, not real pattern)?
#### Example Output
**Finding:** Sentiment increased 12% from 2010-2025 (VADER: 0.34→0.41, p<0.01)
**Example High-Sentiment Review (2024):**
> "This production is a triumph — intimate, visceral, and deeply moving. The
> performances are revelatory."
> [VADER: 0.89, Transformer: 0.95]
**Example Low-Sentiment Review (2012):**
> "Despite strong performances, the production feels uneven and lacks a clear
> directorial vision."
> [VADER: 0.12, Transformer: 0.18]
**Interpretation:** Critical language became more enthusiastic over 15 years, though
whether this reflects genuine appreciation shifts or evolving linguistic norms
requires further investigation.
#### Confidence in Answerability
**Confidence Level:** High
**Why High Confidence:**
- Sentiment analysis is well-established for this corpus size
- Tools are validated and reliable
- Evidence type is clear and measurable
- Validation strategy is robust
- Corpus format supports this analysis (text + dates)
**Potential Challenges:**
- Sarcasm/irony may confuse sentiment tools (mitigated by manual review)
- Domain-specific language may not align with general sentiment lexicons (mitigated by dual tools)
- Selection bias (what gets reviewed) may confound trends (can be investigated but not fully resolved)
#### Estimated Effort
**Time:** 8-12 hours total
- Setup & data prep: 1-2 hours
- Analysis execution: 4-6 hours
- Validation: 2-3 hours
- Interpretation & write-up: 1-2 hours
**Expertise Required:**
- Basic Python or R (can run with provided code snippets)
- Understanding of sentiment analysis (can be learned quickly)
- Domain knowledge for validation (you have this)
**Computational Resources:**
- Standard laptop sufficient
- No GPU required
- Local execution (no API costs)
**Dependencies:**
- Completion of RQ1 doesn't block other questions (can run in parallel)
- Results inform interpretation of other analyses (sentiment context for themes)
---
**After completing RQ1 mapping, ask:**
Does this approach make sense for RQ1?
- Yes → Continue to RQ2
- No → What concerns do you have? Let's revise.
**Wait for confirmation before proceeding to next RQ.**
[Repeat template for RQ2, RQ3, etc.]
---
### After Mapping All Questions
**Output: Methodological Summary**
```markdown
## Methodological Summary
| Research Question | Method | Primary Tools | Evidence Type | Confidence | Time Estimate |
|-------------------|--------|---------------|---------------|------------|---------------|
| RQ1: [short form] | Sentiment analysis | VADER, Transformers | Sentiment scores by year | High | 8-12 hours |
| RQ2: [short form] | Topic modeling | BERTopic | Topic evolution heatmaps | High | 10-15 hours |
| RQ3: [short form] | Entity extraction | spaCy, NetworkX | Co-occurrence network | Medium-High | 12-18 hours |
| RQ4: [short form] | Keyness analysis | Scattertext | Distinctive term lists | High | 6-10 hours |
[Continue for all RQs]
**Total Estimated Effort:** [Sum of estimates] hours = [X-Y weeks at Z hours/week]
**Estimated Timeline:**
- At 5 hours/week: [X weeks]
- At 10 hours/week: [Y weeks]
- At 20 hours/week: [Z weeks]
**Dependency Chain (Recommended Execution Order):**
1. **Phase A: Data Preparation** (Required first)
- Load corpus, validate format, create metadata structures
- Estimated time: 2-4 hours
- Blocks: All analyses
2. **Phase B: Independent Analyses** (Can run in parallel)
- RQ1: [Method] - [Time estimate]
- RQ2: [Method] - [Time estimate]
- [Continue for independent questions]
3. **Phase C: Dependent Analyses** (Require Phase B completion)
- RQ[n]: [Method] - Requires RQ[m] results
- [Continue for dependent questions]
**Critical Decision Points:**
After Data Preparation (Phase A):
- Validate data quality before proceeding
- If data issues found, resolve before continuing
- Checkpoint: "Is the corpus ready for analysis?"
After RQ1 Analysis:
- Validate results with manual review
- If validation reveals issues, tune method before proceeding
- Checkpoint: "Are computational results reliable?"
After RQ2 Analysis:
- Check if topics align with intuitions
- If topics seem incoherent, adjust parameters (min_cluster_size, diversity)
- Checkpoint: "Do topics make sense for the domain?"
[Continue for other critical checkpoints]
**Resource Requirements:**
**Software:**
- Python 3.8+ or R 4.0+
- Required packages: [List all unique packages across RQs]
- Total installation size: ~2-3 GB
**Hardware:**
- RAM: 8 GB minimum, 16 GB recommended
- Storage: [Corpus size + 2x for outputs] = [X GB]
- CPU: Standard laptop sufficient (no GPU required for this corpus size)
**External Resources:**
- None required (all tools are open source and free)
- Optional: External validation sources (theater databases, event timelines)
**Expertise:**
- Programming: Basic to intermediate (code examples provided)
- Statistics: Basic understanding of p-values, correlations
- Domain knowledge: You have this (critical for validation)
Does this roadmap feel achievable and valuable?
- Yes → Proceed to Phase 5 (Output Generation)
- No → What should we revise? (scope, methods, effort estimates, timeline)
Wait for confirmation.
If user has concerns:
- Too time-intensive: Suggest reducing scope (fewer RQs, simpler methods)
- Too technically complex: Suggest simpler tools or collaboration options
- Unclear value: Revisit Phase 2 (Interest Map) - are these the right questions?
- Resource constraints: Adjust methods to fit constraints
Only proceed to Phase 5 when user confirms roadmap is achievable.
Phase 5: Research Roadmap Output (5-10 minutes)
Goal: Generate comprehensive research framework document that serves as execution guide
Method: Compile all Phase 1-4 outputs into structured markdown document with execution timeline
Announce Output Generation
I'll now create your Research Questions Framework document. This will include:
- Corpus profile (from Phase 1)
- Interest map and assumptions (from Phase 2)
- Selected research questions with rationales (from Phase 3)
- Complete methodological roadmap (from Phase 4)
- Execution timeline with validation checklists
- Success criteria for the research
This document will serve as your guide throughout the analysis process.
Where should I save this?
Recommended path: ~/[project-name]/00-research-questions-framework.md
Or provide custom path: _____________
Wait for path confirmation.
If user provides directory without filename:
- Use default filename:
00-research-questions-framework.md
If path doesn't exist:
- Offer to create directory
- Confirm before creating
Generate Framework Document
Template:
---
title: Research Questions Framework
project: [Project Name - e.g., Theater Review Corpus Analysis]
created: [YYYY-MM-DD]
researcher: [User name if provided, otherwise "Researcher"]
corpus: [Brief corpus description]
created_with: corpus-discovery-dialogue skill v1.0
status: Initial Framework
version: 1.0
---
# Research Questions Framework: [Project Name]
**Created:** [Date]
**Researcher:** [Name]
**Corpus:** [Description]
**Framework Status:** ✅ Complete - Ready for execution
---
## I. Corpus Profile
**Domain:** [Domain]
**Type:** [Text type]
**Size:** [N documents, ~X words]
**Temporal Span:** [Date range if applicable]
**Authorship:** [Single/group/anonymous]
**Format:** [JSON/CSV/TXT/etc. with metadata description]
**Data Status:** [Ready/partial/gathering]
**Analytical Implications:**
- **Size supports:** [List appropriate methods for this corpus size]
- **Temporal span enables:** [List temporal analyses if applicable]
- **Authorship enables:** [List authorship-specific analyses]
- **Metadata supports:** [List metadata-dependent analyses]
**Sample Texts Reviewed:** [Number of samples examined]
**Representative Features:**
- Length range: [X-Y words per document]
- Writing style: [Description]
- Content focus: [Description]
- Domain terminology: [Examples]
---
## II. Research Interests & Assumptions
### Primary Motivation
[Why you're studying this corpus - from Phase 2A]
### Puzzles & Intrigues
1. [Puzzle 1 from Phase 2B]
2. [Puzzle 2 from Phase 2B]
3. [Puzzle 3 from Phase 2B]
### Explicit Assumptions (Worth Testing)
**Surprise Indicators (what would contradict your expectations):**
- Surprise if: [Assumption 1]
- Surprise if: [Assumption 2]
- Current assumption: [Explicit statement]
**Disappointment Indicators (what you hope to find):**
- Disappointed if: [Hope 1]
- Disappointed if: [Hope 2]
- Seeking: [Explicit statement]
### Stakes & Audience
**Primary Stakes:** [Academic/Professional/Personal/Cultural/Methodological]
**Intended Audience:** [Who will read/use this research]
**Expected Contribution:** [What this research adds]
**Success Metric:** [How you'll know if this research "worked"]
**Implicit Hypotheses Worth Testing:**
1. [Hypothesis 1 - derived from surprises/disappointments]
2. [Hypothesis 2]
3. [Hypothesis 3]
---
## III. Research Questions
### Primary Questions
**RQ1:** [Full question text]
- **Priority:** High
- **Rationale:** [Why this matters - from Phase 3]
- **Addresses Interest:** [Links to specific puzzle from Section II]
- **Expected Contribution:** [What answering this adds to understanding]
- **Analytical Approach:** [Method name]
- **Estimated Time:** [Hours]
**RQ2:** [Full question text]
[Same structure as RQ1]
**RQ3:** [Full question text]
[Same structure as RQ1]
[Continue for all selected questions]
### Deferred Questions (Future Exploration)
**Interesting but not prioritized for initial analysis:**
1. [Question 1 - with brief note on why deferred]
2. [Question 2 - with brief note on why deferred]
3. [Question 3 - with brief note on why deferred]
**Note:** These may become relevant after initial analyses reveal patterns.
### Question Dependencies
**Independent Questions (any order):**
- [List questions that don't depend on each other]
**Sequential Questions (must follow order):**
- [Question A] → [Question B] (because B requires A's results)
**Complementary Questions (stronger together):**
- [Question X] + [Question Y] (because they illuminate each other)
---
## IV. Methodological Roadmap
[For each RQ, include complete mapping from Phase 4]
### RQ1: [Question]
**Analytical Approach:** [Method]
#### Tools & Installation
**Primary:** [Tool name]
- Purpose: [What it does]
- Installation: `[command]`
- Documentation: [URL if helpful]
**Secondary:** [Tool name]
- Purpose: [What it does]
- Installation: `[command]`
#### Procedure
1. **[Step 1 name]** [Estimated time: X hours]
- [Action description]
- [Expected output]
2. **[Step 2 name]** [Estimated time: X hours]
- [Action description]
- [Expected output]
[Continue for all steps]
#### Expected Evidence
**Quantitative:**
- [Output type 1]: [Description]
- [Output type 2]: [Description]
**Visual:**
- [Visualization 1]: [Description]
- [Visualization 2]: [Description]
**Qualitative:**
- [Output type 1]: [Description]
#### Validation Strategy
**Computational Validation:**
- [Method 1]
- [Method 2]
**Manual Validation:**
- [Method 1]
- [Method 2]
**External Validation:**
- [Method 1]
- [Method 2]
**Alternative Explanation Checks:**
- Could this be [alternative explanation]? Test by [method]
- Could this be [alternative explanation]? Test by [method]
#### Confidence Assessment
**Level:** [High / Medium-High / Medium / Low-Medium / Low]
**Rationale:** [Why this confidence level]
**Potential Challenges:**
- [Challenge 1]: Mitigated by [strategy]
- [Challenge 2]: Mitigated by [strategy]
#### Estimated Effort
- **Total Time:** [X-Y hours]
- **Technical Expertise:** [Level required]
- **Computational Resources:** [Requirements]
- **Dependencies:** [What must be completed first]
---
[Repeat for RQ2, RQ3, etc.]
---
## V. Execution Plan
### Phase A: Data Preparation
**Goal:** Load corpus, validate quality, create analysis-ready dataset
**Tasks:**
- [ ] Load corpus into analysis environment (Python/R/Jupyter)
- [ ] Validate data quality
- [ ] No missing values in critical fields
- [ ] Text encoding correct (no garbled characters)
- [ ] Dates parseable and within expected range
- [ ] Document IDs unique and consistent
- [ ] Create temporal metadata (if time-based analysis)
- [ ] Parse dates into standard format
- [ ] Extract year, month, period fields
- [ ] Validate temporal distribution
- [ ] Run basic descriptive statistics
- [ ] Total document count
- [ ] Word count distribution (min, max, mean, median)
- [ ] Temporal distribution (documents per period)
- [ ] Metadata completeness check
**Estimated Time:** [X hours]
**Completion Checkpoint:**
✓ Data loaded successfully
✓ Quality validation passed
✓ Metadata structured correctly
✓ Descriptive statistics documented
**Critical Decision Point:** Do NOT proceed to analyses until data quality confirmed.
---
### Phase B: RQ1 Analysis - [Question]
[Include step-by-step checklist from Phase 4 procedure]
**Tasks:**
- [ ] [Step 1 from procedure]
- [ ] [Step 2 from procedure]
- [ ] [Step 3 from procedure]
- [ ] Validate results (manual review, sanity checks)
- [ ] Document findings
- [ ] Save outputs: [file paths/formats]
**Estimated Time:** [X hours]
**Completion Checkpoint:**
✓ Analysis executed successfully
✓ Outputs generated (quantitative, visual, qualitative)
✓ Validation completed (< X% error rate)
✓ Findings documented
**Critical Decision Point:** Validate results before proceeding to RQ2.
If validation reveals issues, revise method and re-run.
---
### Phase C: RQ2 Analysis - [Question]
[Same structure as Phase B]
---
### Phase D: RQ3 Analysis - [Question]
[Same structure as Phase B]
---
[Continue for all RQs]
---
### Phase X: Synthesis & Documentation
**Goal:** Integrate findings across RQs, write synthesis, document methodology
**Tasks:**
- [ ] Review all RQ findings
- [ ] Identify connections between findings
- [ ] Write integrated synthesis (use research-synthesis-dialogue skill)
- [ ] Document methodology (use methodology-documentation-dialogue skill)
- [ ] Create visualizations summarizing key findings
- [ ] Prepare presentation/publication materials (if applicable)
**Estimated Time:** [X hours]
**Tools to Use:**
- research-synthesis-dialogue skill (for narrative integration)
- methodology-documentation-dialogue skill (for reproducibility documentation)
---
## VI. Validation Checklists
### For Sentiment Analysis (RQ[n] if applicable)
**Computational Checks:**
- [ ] VADER and Transformer correlation > 0.7 (tool agreement)
- [ ] Sentiment score distribution is plausible (not all extreme scores)
- [ ] No systematic biases (e.g., all negative at corpus start)
**Manual Checks:**
- [ ] Review 20 highest-sentiment texts (check for sarcasm, irony, false positives)
- [ ] Review 20 lowest-sentiment texts (check for genuine negativity, false negatives)
- [ ] Check 10 mid-range texts (check for ambiguous/neutral classification)
- [ ] Error rate < 20% acceptable
**External Checks:**
- [ ] Compare sentiment trends to external events (do trends align with known context?)
- [ ] Cross-reference with qualitative self-reflection (matches or contradicts memory?)
- [ ] Consult domain expertise (do findings make sense given field knowledge?)
**Alternative Explanations:**
- [ ] Selection bias investigated (is sentiment change due to what gets analyzed?)
- [ ] Linguistic drift considered (is change in language norms, not sentiment?)
- [ ] Method artifacts ruled out (is this tool limitation, not real pattern?)
---
### For Topic Modeling (RQ[n] if applicable)
**Computational Checks:**
- [ ] Number of topics is reasonable (8-15 topics for medium corpus)
- [ ] Topics are distinct (low overlap in top terms)
- [ ] Topic coherence scores acceptable (if using coherence metrics)
**Manual Checks:**
- [ ] Review 5 representative texts per topic (do they belong together?)
- [ ] Topic labels make sense (can you describe each topic coherently?)
- [ ] Check for junk topics (noise clusters, incoherent word lists)
- [ ] Topics align with domain knowledge (do they reflect actual content themes?)
**Temporal Checks (if applicable):**
- [ ] Topic shifts align with known events (do changes make historical sense?)
- [ ] Topic evolution is plausible (gradual shifts vs. sudden jumps)
---
### For Entity Extraction (RQ[n] if applicable)
**Computational Checks:**
- [ ] Entity recognition accuracy spot-checked (sample 100 entities)
- [ ] Entity types correctly classified (PERSON vs ORG vs WORK_OF_ART)
**Manual Checks:**
- [ ] Review top 50 entities (correctly identified and classified?)
- [ ] Check for entity normalization issues (aliases, misspellings, variations)
- [ ] Validate co-occurrence patterns (do relationships make domain sense?)
**External Checks:**
- [ ] Cross-reference with external knowledge base (Wikipedia, domain databases)
- [ ] Network structure matches domain reality (central entities are genuinely central?)
---
### For Lexicon/Keyness Analysis (RQ[n] if applicable)
**Computational Checks:**
- [ ] Keyness scores are statistically significant (p < 0.05 or log-likelihood > 3.84)
- [ ] Stopwords filtered appropriately (no "the", "and", "of" as distinctive terms)
**Manual Checks:**
- [ ] Review top 50 distinctive terms (are they meaningfully distinctive?)
- [ ] Check example sentences for key terms (used consistently in claimed context?)
- [ ] Validate collocations (multi-word phrases coherent and frequent?)
**Interpretation Checks:**
- [ ] Distinctive terms align with intuitions (do they feel authentic?)
- [ ] Language shifts make domain sense (can you explain why vocabulary changed?)
---
## VII. Success Criteria
### Research Framework Success (This Document)
**Clarity:**
- [ ] Each research question is specific and answerable
- [ ] Evidence types are clearly defined for each RQ
- [ ] Validation strategies are specified and feasible
- [ ] Procedures are step-by-step and executable
**Feasibility:**
- [ ] Methods align with available tools and expertise
- [ ] Estimated effort is realistic given constraints
- [ ] Corpus size and quality support proposed analyses
- [ ] Timeline is achievable given available time
**Meaningfulness:**
- [ ] Questions align with research interests (Section II)
- [ ] Findings would have value to intended audience
- [ ] Results would be publishable/presentable/actionable (depending on stakes)
- [ ] Research addresses genuine puzzles, not trivial questions
**Rigor:**
- [ ] Multiple validation strategies for each finding
- [ ] Alternative explanations considered and testable
- [ ] Limitations explicitly acknowledged
- [ ] Methods are reproducible (documented sufficiently for replication)
### Analysis Success (After Execution)
**For Each RQ:**
- [ ] Analysis executed successfully (no technical failures)
- [ ] Evidence generated (quantitative + visual + qualitative outputs)
- [ ] Validation completed (error rate acceptable, sanity checks passed)
- [ ] Findings documented (interpretation written, limitations noted)
- [ ] RQ answered (clear response to original question, even if answer is "no pattern found")
**Overall Project Success:**
- [ ] All priority RQs addressed (even if some yield null results)
- [ ] Findings integrated into coherent narrative (synthesis complete)
- [ ] Methodology documented for reproducibility
- [ ] Research contributes to intended goal (academic/professional/personal/cultural)
- [ ] Audience needs met (findings useful to intended readers)
---
## VIII. Next Steps
### Immediate (Before Starting Analysis)
1. **Review this framework**
- Does it capture your research goals accurately?
- Are the questions still the ones you want to answer?
- Is the timeline realistic given your schedule?
2. **Gather tools**
- Install required software (Python/R + packages)
- Download any required models (spaCy, Transformers)
- Set up analysis environment (Jupyter notebooks recommended)
3. **Prepare workspace**
- Create project directory structure:
```
~/[project-name]/
├── 00-research-questions-framework.md (this document)
├── data/ (corpus files)
├── analysis/ (scripts and notebooks)
├── outputs/ (results, visualizations)
├── interpretations/ (findings documentation)
└── methodology/ (reproducibility documentation)
```
### Phase A: Data Preparation (Start Here)
1. **Load and validate corpus** (see Phase A checklist in Section V)
2. **Run descriptive statistics**
3. **Document data quality**
4. **Checkpoint:** Confirm data ready before proceeding
### Iterative Workflow
After completing each RQ analysis:
1. **Use analysis-interpretation-dialogue skill** to interpret computational outputs
- Invocation: "Help me interpret these [sentiment/topic/entity] results"
- Input: Analysis outputs + original research question
- Output: Interpreted findings with confidence levels
2. **Use pattern-verification-dialogue skill** (if patterns claimed)
- Invocation: "Verify this pattern I found"
- Input: Claimed pattern + supporting evidence
- Output: Verification report with robustness assessment
3. **Use hypothesis-testing-dialogue skill** (if findings suggest hypotheses)
- Invocation: "Design an experiment for this hypothesis"
- Input: Hypothesis from findings + corpus data
- Output: Experimental protocol
### After Completing All RQ Analyses
1. **Use research-synthesis-dialogue skill**
- Invocation: "Help me synthesize my findings"
- Input: All RQ interpretations + original Interest Map
- Output: Integrated narrative synthesis
2. **Use methodology-documentation-dialogue skill**
- Invocation: "Document my analysis methodology"
- Input: Analysis code/notebooks + decisions made
- Output: Reproducibility documentation
### Publication/Presentation (If Applicable)
1. Draft findings document (use collaborative-writing skill if needed)
2. Create visualizations for presentation
3. Prepare supplementary materials (code, data)
4. Submit/present/share with intended audience
---
## IX. Revision History
This framework is a living document. Update as:
- Questions evolve based on preliminary findings
- Methods are refined through iteration
- New puzzles emerge from analysis
- Scope adjusts due to time/resource constraints
**Version 1.0** - [Date] - Initial framework created via corpus-discovery-dialogue
- Corpus: [Description]
- Research questions: [Count]
- Status: Ready for execution
**Future Versions:**
[Document revisions here as framework evolves]
---
## X. Recommended Skills for Each Phase
**Research Question Formulation (Current Phase):**
- ✅ corpus-discovery-dialogue (this skill - completed)
**Analysis Execution:**
- Python/R programming (execute analytical methods)
- Jupyter notebooks (recommended environment)
**Interpretation & Validation:**
- analysis-interpretation-dialogue (interpret computational outputs)
- pattern-verification-dialogue (verify claimed patterns)
- hypothesis-testing-dialogue (test emergent hypotheses)
**Synthesis & Documentation:**
- research-synthesis-dialogue (synthesize findings into narrative)
- methodology-documentation-dialogue (document for reproducibility)
- collaborative-writing (if writing publication/presentation)
---
## XI. Contact & Collaboration
**Primary Researcher:** [Name/email if provided]
**Potential Collaborators:** [If mentioned]
**Consultation Needs:** [If user indicated areas where expertise needed]
---
**Framework Status:** ✅ Complete - Ready for execution
**Next Recommended Action:** Begin Phase A (Data Preparation)
---
*Created with corpus-discovery-dialogue skill v1.0*
*For skill documentation: [link to skill if applicable]*Save and Confirm
Actions:
- Save document to user-specified path
- Generate summary of what was created
- Confirm completion and suggest next steps
Confirmation Message:
✅ Research Questions Framework Complete!
**Saved to:** [file path]
**Framework Summary:**
- Corpus: [Type, size, span]
- Research questions: [Count]
- Analytical approaches: [List methods]
- Estimated total effort: [X-Y hours = W-Z weeks]
- Status: Ready for execution
**What's in This Framework:**
- Section I: Corpus Profile (your data characteristics)
- Section II: Interest Map (your puzzles and assumptions)
- Section III: Research Questions (3-5 prioritized questions)
- Section IV: Methodological Roadmap (complete analysis procedures)
- Section V: Execution Plan (step-by-step checklists)
- Section VI: Validation Checklists (quality assurance)
- Section VII: Success Criteria (how to know if research succeeds)
- Section VIII: Next Steps (what to do now)
**Your Corpus Discovery Dialogue is complete!**
**Recommended Next Steps:**
1. **Review the framework**
- Read through Sections I-IV to ensure alignment with your goals
- Check timeline feasibility (Section V)
- Confirm questions still feel right (Section III)
2. **Prepare your environment**
- Install required software (listed in Section IV)
- Set up project directory structure (Section VIII)
- Gather any external resources needed
3. **Begin data preparation**
- Follow Phase A checklist (Section V)
- Validate corpus quality
- Run descriptive statistics
4. **Use complementary skills as you proceed**
- analysis-interpretation-dialogue (after running each analysis)
- pattern-verification-dialogue (when patterns emerge)
- research-synthesis-dialogue (after completing all analyses)
**Questions or Concerns?**
- Need help installing tools? (I can guide you)
- Questions about specific methods? (I can explain in detail)
- Want to revise the framework? (We can modify anytime)
- Ready to start analysis? (I can help with Python/R code)
Would you like guidance on any specific aspect, or are you ready to proceed independently?
Success Criteria for This Skill
This corpus-discovery-dialogue skill succeeds when:
1. Questions are Answerable
- Each RQ has clear evidence that would resolve it (not "fishing expedition")
- Methods proposed are appropriate for corpus size and type
- Evidence types are specific and measurable
- Validation strategies are feasible
- No vague questions like "what patterns exist?" without specification
2. User Clarity Increased
- User can articulate what they want to discover (vs. vague curiosity at start)
- Assumptions made explicit (surprise/disappointment questions revealed them)
- Priorities clear (primary vs. deferred questions distinguished)
- User understands why each question matters (rationales documented)
- User confident in research direction (not overwhelmed or uncertain)
3. Execution Path Clear
- Step-by-step procedures specified for each RQ
- Tools identified with installation instructions
- Time estimates realistic and bounded
- Dependencies and execution order explicit
- Critical decision points identified (validation checkpoints)
4. Validation Planned
- Each finding has verification strategy (computational + manual + external)
- Alternative explanations considered (not just confirmation bias)
- Manual checks specified with acceptable error rates
- External validation sources identified
- Confidence levels explicit for each method
5. Framework is Actionable
- User can begin analysis immediately after this dialogue
- Next steps are concrete and specific
- Success criteria are measurable
- Framework document is comprehensive (stands alone)
- Complementary skills identified for later phases
6. Alignment with User Interests
- Research questions directly address puzzles from Phase 2
- Methods match user's technical expertise level
- Timeline fits user's available time
- Stakes and audience are honored (research serves intended purpose)
- Hypotheses worth testing are made explicit
7. Methodological Rigor
- Multiple validation strategies per finding
- Alternative explanations testable
- Limitations acknowledged upfront
- Reproducibility considered (documentation planned)
- Quality checks built into execution plan
Related Skills
Use These Skills in Your Research Workflow:
After Corpus Discovery (This Skill)
- analysis-interpretation-dialogue - Interpret computational outputs from RQ analyses
- When: After running sentiment analysis,
Truncated - read the full file at https://github.com/majiayu000/claude-skill-registry/blob/a16e8e7bdddc9a7406a56f51e520cef7a8646208/skills/analysis/corpus-discovery-dialogue/SKILL.md.