majiayu000 Dossier Templates
Admiralty Code Rating System
Rate every data point on two dimensions:
Source Reliability (who provided it):
- A = Completely reliable (official government registry, SEC filing, company's own website)
- B = Usually reliable (established news outlet, Crunchbase, official API)
- C = Fairly reliable (industry blog, review site, community database)
- D = Not usually reliable (unverified social media, anonymous source)
- E = Unreliable (known-inaccurate source)
- F = Cannot be judged (new/unknown source)
Information Credibility (how trustworthy is the specific claim):
- 1 = Confirmed (corroborated by 3+ independent sources)
- 2 = Probably true (corroborated by 2 independent sources)
- 3 = Possibly true (single credible source, consistent with other data)
- 4 = Doubtful (single source, not corroborated, some inconsistency)
- 5 = Improbable (contradicted by other evidence)
- 6 = Cannot be judged (insufficient basis for assessment)
Example ratings: A1 = government filing confirmed by multiple sources | B2 = Crunchbase data corroborated by news | C3 = single blog post, seems plausible | D4 = unverified LinkedIn claim
Person Dossier Schema (JSON)
{
"meta": {
"schema_version": "1.0",
"generated_at": "ISO-8601 datetime",
"generated_by": "nw-business-osint",
"target_type": "person",
"overall_confidence": "high|medium|low",
"data_sources_count": 0,
"knowledge_gaps_count": 0
},
"subject": {
"name": "string",
"current_role": "string",
"current_company": "string",
"location": "string|null",
"email": "string|null",
"rating": "A1-F6"
},
"career_timeline": [
{
"company": "string",
"role": "string",
"start": "YYYY-MM|null",
"end": "YYYY-MM|null|current",
"source": "string",
"rating": "A1-F6"
}
],
"education": [
{
"institution": "string",
"degree": "string|null",
"field": "string|null",
"year": "YYYY|null",
"source": "string",
"rating": "A1-F6"
}
],
"publications": [
{
"title": "string",
"venue": "string",
"year": "YYYY",
"co_authors": ["string"],
"doi": "string|null",
"source": "string",
"rating": "A1-F6"
}
],
"patents": [
{
"title": "string",
"patent_number": "string",
"filing_date": "YYYY-MM-DD",
"co_inventors": ["string"],
"source": "string",
"rating": "A1-F6"
}
],
"speaking_engagements": [
{
"event": "string",
"title": "string|null",
"date": "YYYY-MM|null",
"url": "string|null",
"source": "string",
"rating": "A1-F6"
}
],
"social_presence": {
"linkedin_url": "string|null",
"github": {
"handle": "string|null",
"public_repos": 0,
"top_languages": ["string"],
"organizations": ["string"],
"rating": "A1-F6"
},
"twitter_handle": "string|null",
"personal_website": "string|null"
},
"key_connections": [
{
"name": "string",
"relationship": "string",
"context": "string",
"source": "string",
"rating": "A1-F6"
}
],
"signals": [
{
"type": "string",
"description": "string",
"detected_date": "ISO-8601",
"relevance_score": 0.0,
"source": "string",
"rating": "A1-F6"
}
],
"knowledge_gaps": [
{
"field": "string",
"sources_tried": ["string"],
"result": "string",
"recommendation": "string"
}
],
"compliance": {
"eu_data_subject": true,
"gdpr_lia_applicable": true,
"data_retention_recommendation": "6 months",
"collection_date": "ISO-8601"
}
}
Company Dossier Schema (JSON)
{
"meta": {
"schema_version": "1.0",
"generated_at": "ISO-8601 datetime",
"generated_by": "nw-business-osint",
"target_type": "company",
"overall_confidence": "high|medium|low",
"data_sources_count": 0,
"knowledge_gaps_count": 0
},
"company": {
"name": "string",
"legal_name": "string|null",
"domain": "string|null",
"industry": "string",
"sub_industry": "string|null",
"founded": "YYYY|null",
"hq_location": "string",
"employee_count": "string|null",
"type": "public|private|startup|nonprofit",
"rating": "A1-F6"
},
"financials": {
"revenue": "string|null",
"revenue_source": "string|null",
"funding_total": "number|null",
"last_funding_round": {
"type": "string",
"amount": "number|null",
"date": "YYYY-MM|null",
"lead_investor": "string|null",
"source": "string",
"rating": "A1-F6"
},
"public_filings": [
{
"type": "10-K|10-Q|8-K|DEF14A",
"date": "YYYY-MM-DD",
"url": "string",
"key_findings": "string"
}
]
},
"technology_stack": [
{
"technology": "string",
"category": "string",
"source": "string",
"rating": "A1-F6"
}
],
"leadership": [
{
"name": "string",
"role": "string",
"since": "YYYY|null",
"source": "string",
"rating": "A1-F6"
}
],
"recent_news": [
{
"headline": "string",
"source": "string",
"date": "YYYY-MM-DD",
"url": "string",
"relevance": "string",
"rating": "A1-F6"
}
],
"job_postings_analysis": {
"total_openings_estimate": "number|null",
"top_departments": ["string"],
"tech_mentions": ["string"],
"growth_signals": ["string"],
"source": "string",
"rating": "A1-F6"
},
"competitive_landscape": [
{
"competitor": "string",
"basis": "string",
"source": "string"
}
],
"signals": [],
"risk_factors": ["string"],
"opportunity_indicators": ["string"],
"knowledge_gaps": [],
"compliance": {
"eu_company": true,
"gdpr_applicable": true,
"data_retention_recommendation": "12 months",
"collection_date": "ISO-8601"
}
}
Markdown Dossier Format
# {Subject Name} -- Intelligence Dossier
**Generated**: {date} | **Confidence**: {high/medium/low} | **Sources**: {count}
## Executive Summary
- {Key finding 1 -- most important for the upcoming meeting}
- {Key finding 2}
- {Key finding 3}
- {Signal or recent change worth noting}
- {Knowledge gap or caveat}
## {Sections vary by target type -- Person or Company}
### Current Position
...
### Career Timeline
| Period | Company | Role | Source | Rating |
|--------|---------|------|--------|--------|
### Key Connections
...
### Recent Signals
...
## Knowledge Gaps
| Area | Sources Tried | Result | Recommendation |
|------|--------------|--------|----------------|
## Data Sources
| Source | Data Points | Reliability | Last Accessed |
|--------|------------|-------------|---------------|
## Compliance Note
{EU data subject flag, LIA reference, retention recommendation}
Executive Summary Guidelines
The executive summary is the most-read section. Write 3-5 bullets covering:
- The single most important finding for the meeting context
- Current role and company status (growth, funding, challenges)
- Relevant professional background or expertise
- Recent signals (job change, funding, M&A, hiring surge)
- Biggest knowledge gap or caveat about the data
Keep each bullet to one sentence. Lead with the most actionable insight.