Analyst Relations

Skill Profile

(Select at least one profile to enable specific modules)

[ ] DevOps
[x] Backend
[ ] Frontend
[ ] AI-RAG
[ ] Security Critical

Overview

Analyst Relations enables managing relationships with industry analysts, briefings, and research reports. This capability is essential for building market credibility, influencing analyst reports, supporting sales cycles, and driving market awareness.

Why This Matters

Strategic Necessity:

Market Credibility: Build credibility with analysts through consistent engagement
Influence: Influence analyst reports and coverage to benefit your product
Sales Support: Support sales with analyst interactions and insights
Market Awareness: Increase market visibility through analyst relationships
Competitive Intelligence: Gather competitive insights from analyst briefings

Core Concepts & Rules

1. Core Principles

Follow established patterns and conventions
Maintain consistency across codebase
Document decisions and trade-offs

2. Implementation Guidelines

Start with the simplest viable solution
Iterate based on feedback and requirements
Test thoroughly before deployment

Inputs / Outputs / Contracts

Inputs:
- <e.g., env vars, request payload, file paths, schema>
Entry Conditions:
- <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
Outputs:
- <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
Artifacts Required (Deliverables):
- <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
Acceptance Evidence:
- <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
Success Criteria:
- <e.g., p95 < 300ms, coverage ≥ 80%>

Skill Composition

Depends on: None
Compatible with: None
Conflicts with: None
Related Skills: None

Quick Start / Implementation Example

Review requirements and constraints
Set up development environment
Implement core functionality following patterns
Write tests for critical paths
Run tests and fix issues
Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions

Analysts are relevant to your market
Product has compelling story and differentiation
Sales team is available for analyst interactions
Sufficient data and insights available
Budget for analyst engagement exists

Compatibility & Prerequisites

Supported Versions:
- Python 3.8+
- Node.js 16+
- Modern browsers (Chrome, Firefox, Safari, Edge)
Required AI Tools:
- Code editor (VS Code recommended)
- Testing framework appropriate for language
- Version control (Git)
Dependencies:
- Language-specific package manager
- Build tools
- Testing libraries
Environment Setup:
- .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix

| Scenario | Description | Expected Outcome | |----------|-------------|------------------| | Analyst Identification | Identify relevant analysts | Complete analyst list with scores | | Briefing Scheduling | Schedule analyst briefings | Scheduled briefings with calendar invitations | | Material Preparation | Prepare briefing materials | Complete materials package | | Research Support | Provide research data | Data and insights provided | | Feedback Collection | Collect analyst feedback | Feedback collected and analyzed |

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

Top Threats: Injection attacks, authentication bypass, data exposure

[ ] Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
[ ] Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
[ ] Authorization: Validate user permissions before state changes

2. Performance & Resources

[ ] Execution Efficiency: Consider time complexity for algorithms
[ ] Memory Management: Use streams/pagination for large data
[ ] Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

[ ] Design Pattern: Follow SOLID principles, use Dependency Injection
[ ] Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

[ ] Logging Standards: Structured JSON, include trace IDs request_id
[ ] Metrics: Track error_rate, latency, queue_depth
[ ] Error Handling: Standardized error codes, no bare except
[ ] Observability Artifacts:
- Log Fields: timestamp, level, message, request_id
- Metrics: request_count, error_count, response_time
- Dashboards/Alerts: High Error Rate > 5%

Agent Directives

Identification Phase: Always research analysts before outreach
Briefing Phase: Always prepare thoroughly for briefings
Research Phase: Always provide quality data and insights
Engagement Phase: Always maintain consistent, professional engagement
Feedback Phase: Always collect and act on feedback

Definition of Done (DoD) Checklist

[ ] Tests passed + coverage met
[ ] Lint/Typecheck passed
[ ] Logging/Metrics/Trace implemented
[ ] Security checks passed
[ ] Documentation/Changelog updated
[ ] Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

⛔ Don't: Log PII, catch-all exception, N+1 queries
⚠️ Watch out for: Common symptoms and quick fixes
💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

Internal documentation and examples
Official documentation and best practices
Community resources and discussions

Versioning & Changelog

Version: 1.0.0
Changelog:
- 2026-02-22: Initial version with complete template structure