Internal Comms

When to use this skill

To write internal communications, use this skill for:

• 3P updates (Progress, Plans, Problems)
• Company newsletters
• FAQ responses
• Status reports
• Leadership updates
• Project updates
• Incident reports

How to use this skill

To write any internal communication:

1. Identify the communication type from the request
2. Load the appropriate guideline file from the examples/ directory:
   • examples/3p-updates.md - For Progress/Plans/Problems team updates
   • examples/company-newsletter.md - For company-wide newsletters
   • examples/faq-answers.md - For answering frequently asked questions
   • examples/general-comms.md - For anything else that doesn't explicitly match one of the above
3. Follow the specific instructions in that file for formatting, tone, and content gathering

If the communication type doesn't match any existing guideline, ask for clarification or more context about the desired format.

Keywords

3P updates, company newsletter, company comms, weekly update, faqs, common questions, updates, internal comms

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags internal-comms <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns