company-kb

name	company-kb
description	company-kb — Company Knowledge Base Skill. Use when relevant to this domain.
domain	core
tags	["company","infrastructure","memory","self-improvement"]
persona	{"name":"Domain Expert","title":"Master of Company Kb","expertise":["Core Excellence","Best Practices","Professional Standards"],"philosophy":"Excellence is not a skill, it's an attitude.","credentials":["Industry leader","Practiced professional","Thought leader"],"principles":["Quality first","Continuous improvement","Evidence-based","Customer focused"]}

Company Kb

When to Use

Trigger phrases:

• "company kb"

• "User asks about company info, procedures, history, products, team"

• "Agent needs to look up internal documentation"

• "Storing new company knowledge for future recall"

• User asks about company info, procedures, history, products, team

• Agent needs to look up internal documentation

• Storing new company knowledge for future recall

• Paijo asks about anything related to BerkahKarya operations

When NOT to Use

• When the task can be solved with existing standard libraries
• When the infrastructure is already in place and working
• When the added complexity does not provide measurable benefit

Overview

Company Kb is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.

Architecture

• Input layer — Receives and validates incoming requests
• Processing layer — Core logic for system foundation
• Output layer — Formats and delivers results
• State management — Maintains context across invocations

Configuration

• Set up required environment variables and paths
• Configure logging level and output format
• Define resource limits (memory, time, API calls)
• Enable/disable features via configuration flags

Integration

• Exposes standard interfaces for other skills to consume
• Supports event-driven and request-response patterns
• Compatible with the 1ai-skills hook system
• Logs metrics for the skill performance monitor

Anti-Rationalization

Rationalization	Reality
"I will add monitoring later"	Without monitoring, you cannot detect failures. Add it from day one.
"One model is enough"	Different tasks need different models. Route intelligently.
"Premature optimization"	Infrastructure decisions are hard to change later. Design for scale early.

# Example: Model routing
ROUTES = {
    "code": ["claude-sonnet-4-20250514", "gpt-4o"],
    "vision": ["gemini-2.5-pro", "gpt-4o"],
    "fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}

def route_request(task: str, prompt: str):
    models = ROUTES.get(task, ROUTES["fast"])
    for model in models:
        try:
            return call_model(model, prompt)
        except Exception:
            continue
    raise RuntimeError("All models failed")

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run company kb workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings