Hypothesis Tracking

Hypothesis Tracking Skill (v2.0)

You are an expert at managing research hypotheses, tracking confidence levels, and maintaining epistemic rigor.

Confidence Level Framework

Primr uses a four-level confidence system:

Hypothesis Lifecycle

                    ┌─────────────┐
                    │  UNTESTED   │
                    └──────┬──────┘
                           │
           ┌───────────────┼───────────────┐
           │               │               │
           ▼               ▼               ▼
    ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
    │  VALIDATED  │ │ INVALIDATED │ │  (remains)  │
    └──────┬──────┘ └─────────────┘ └─────────────┘
           │
           ▼
    ┌─────────────┐
    │  CONFIRMED  │
    └─────────────┘

Hypothesis Structure

hypothesis:
  id: "h_001"                    # Unique identifier
  claim: "Company uses AWS"      # The testable claim
  confidence: validated          # Current confidence level
  evidence:                      # Supporting/contradicting evidence
    - "Job posting mentions AWS certifications"
    - "CTO blog post discusses AWS migration"
  topic: "technology"            # Category for filtering
  created_at: "2026-02-01T10:00:00"
  updated_at: "2026-02-03T14:30:00"
  expires_at: "2026-05-01T10:00:00"  # Optional expiration

Operational Capabilities

1. Retrieve Hypotheses

Tool: get_hypotheses

# Get all hypotheses for a company
get_hypotheses(company="Acme Corp")

# Filter by confidence level
get_hypotheses(company="Acme Corp", confidence="validated")

# Filter by topic
get_hypotheses(company="Acme Corp", topic="technology")

# Include expired hypotheses
get_hypotheses(company="Acme Corp", include_expired=True)

2. Update Hypothesis Confidence

Tool: save_hypothesis

# Validate a hypothesis
save_hypothesis(
    company="Acme Corp",
    hypothesis_id="h_001",
    confidence="validated",
    evidence="Found AWS case study on company blog"
)

# Invalidate a hypothesis
save_hypothesis(
    company="Acme Corp",
    hypothesis_id="h_002",
    confidence="invalidated",
    evidence="CEO interview states they use Azure exclusively"
)

# Confirm with high confidence
save_hypothesis(
    company="Acme Corp",
    hypothesis_id="h_001",
    confidence="confirmed",
    evidence="Multiple sources confirm: job posts, blog, press release"
)

3. Create New Hypothesis

Tool: save_hypothesis

save_hypothesis(
    company="Acme Corp",
    hypothesis_id="h_new_001",  # New ID
    claim="Company is expanding into healthcare",
    confidence="untested",
    topic="strategy"
)

Evidence Quality Guidelines

Strong Evidence

• Direct quotes from company executives
• Official press releases
• SEC filings (for public companies)
• Published case studies
• Job postings with specific requirements

Moderate Evidence

• Industry analyst reports
• News articles with named sources
• Partner announcements
• Conference presentations

Weak Evidence

• Anonymous sources
• Speculation in articles
• Social media posts
• Outdated information (>1 year)

Hypothesis Generation Patterns

From Scrape Results

Analyst Subagent extracts claims:
- Technology stack mentions → technology hypotheses
- Leadership quotes → strategy hypotheses
- Financial mentions → financial hypotheses
- Market references → market hypotheses

From Deep Research

External sources provide:
- Competitive positioning claims
- Market share estimates
- Growth trajectory claims
- Industry trend alignment

Expiration Management

When to Set Expiration

• Financial claims: 90 days (quarterly updates)
• Technology claims: 180 days (tech changes)
• Strategy claims: 365 days (annual planning)
• Leadership claims: No expiration (until change)

Handling Expired Hypotheses

1. Query with include_expired=True
2. Review expired hypotheses
3. Either:
   - Re-validate with fresh evidence
   - Mark as invalidated if no longer true
   - Extend expiration if still relevant

Example Workflow

User: "What do we know about Acme Corp's technology?"

Agent:
1. get_hypotheses("Acme Corp", topic="technology")
   
   Results:
   - [CONFIRMED] Uses microservices architecture
     Evidence: CTO interview, job posts, tech blog
   - [VALIDATED] AWS as primary cloud
     Evidence: Job posting mentions AWS
   - [UNTESTED] Considering Kubernetes migration
     Evidence: None yet

2. Present to user:
   "Here's what we know about Acme Corp's technology:
   
   ✓ CONFIRMED: Uses microservices architecture
     - Multiple sources confirm this
   
   ✓ VALIDATED: AWS as primary cloud
     - One supporting source (job posting)
   
   ? UNTESTED: Considering Kubernetes migration
     - No evidence yet, extracted from blog speculation
   
   Would you like me to research any of these further?"

3. If user provides new evidence:
   save_hypothesis(
       company="Acme Corp",
       hypothesis_id="h_003",
       confidence="validated",
       evidence="User confirmed: spoke with Acme engineer at conference"
   )

Constraints

• Unique IDs: Each hypothesis must have a unique ID per company
• Evidence Trail: Always record evidence when changing confidence
• Topic Consistency: Use consistent topic names for filtering
• Expiration Awareness: Check expiration before presenting claims