Confidence level management and hypothesis lifecycle
Scanned 9/2/2026
Install to Claude Code
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---
name: hypothesis-tracking
version: "2.0.0"
description: "Confidence level management and hypothesis lifecycle"
metadata:
openclaw:
requires:
bins:
- primr-mcp
env:
- GEMINI_API_KEY
mcp_server: primr
tools:
- get_hypotheses
- save_hypothesis
resources:
- primr://memory/{company}
---
# 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:
| Level | Meaning | Evidence Required |
|-------|---------|-------------------|
| `UNTESTED` | Claim extracted, not yet verified | None (initial state) |
| `VALIDATED` | Supporting evidence found | At least one corroborating source |
| `INVALIDATED` | Contradicting evidence found | At least one contradicting source |
| `CONFIRMED` | High confidence, multiple sources | Multiple independent sources |
## Hypothesis Lifecycle
```
┌─────────────┐
│ UNTESTED │
└──────┬──────┘
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ VALIDATED │ │ INVALIDATED │ │ (remains) │
└──────┬──────┘ └─────────────┘ └─────────────┘
│
▼
┌─────────────┐
│ CONFIRMED │
└─────────────┘
```
## Hypothesis Structure
```yaml
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
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