The Daem0n's Protocol

Overview

When Daem0nMCP memory tools are available, you MUST follow this protocol. Memory without discipline is noise.

Core principle: Check before you change, record what you decide, track whether it worked.

Tool Detection

First, verify Daem0nMCP tools are available:

Look for these tools in your available tools:
- mcp__daem0nmcp__get_briefing
- mcp__daem0nmcp__context_check
- mcp__daem0nmcp__remember
- mcp__daem0nmcp__record_outcome
- mcp__daem0nmcp__link_memories
- mcp__daem0nmcp__trace_chain
- mcp__daem0nmcp__get_graph

If tools are NOT available: This skill does not apply. Proceed normally.

If tools ARE available: Follow the protocol below. No exceptions.

The Protocol

1. SESSION START (Non-Negotiable)

IMMEDIATELY when you have daem0nmcp tools:

mcp__daem0nmcp__get_briefing()

DO NOT:
- Ask user what they want first
- Skip briefing because "it's a quick task"
- Assume you remember from last session

The briefing loads:

• Past decisions and their outcomes
• Warnings and failed approaches to AVOID
• Patterns to FOLLOW
• Git changes since last session

2. BEFORE ANY CODE CHANGES

BEFORE touching any file:

mcp__daem0nmcp__context_check(description="what you're about to do")

OR for specific files:

mcp__daem0nmcp__recall_for_file(file_path="path/to/file")

If context_check returns:

• WARNING: You MUST acknowledge it to the user
• FAILED APPROACH: Explain how your approach differs
• must_not: These are HARD CONSTRAINTS - do not violate

3. AFTER MAKING DECISIONS

AFTER every significant decision:

memory_result = mcp__daem0nmcp__remember(
    category="decision",  # or "pattern", "warning", "learning"
    content="What you decided",
    rationale="Why you decided it",
    file_path="relevant/file.py",  # optional
    tags=["relevant", "tags"]
)

SAVE THE MEMORY ID - you need it for record_outcome

Category Guide:

4. AFTER IMPLEMENTATION (Critical)

AFTER implementing and testing:

mcp__daem0nmcp__record_outcome(
    memory_id=<id from remember>,
    outcome="What actually happened",
    worked=true  # or false
)

FAILURES ARE VALUABLE. If something doesn't work:

• Record worked=false with explanation
• Failed approaches get 1.5x boost in future searches
• You WILL see past mistakes - that's the point

Red Flags - STOP

• About to edit a file without calling recall_for_file
• Making a significant decision without calling remember
• Implementation complete but no record_outcome called
• Context check returned WARNING but you didn't acknowledge it
• Repeating an approach that previously failed

Rationalization Prevention

Workflow Summary

SESSION START
    └─> get_briefing()

BEFORE CHANGES
    └─> context_check("what you're doing")
    └─> recall_for_file("path") for specific files
    └─> ACKNOWLEDGE any warnings

AFTER DECISIONS
    └─> remember(category, content, rationale)
    └─> SAVE the memory_id
    └─> link_memories() if causally related to other decisions

AFTER IMPLEMENTATION
    └─> record_outcome(memory_id, outcome, worked)

INVESTIGATING CONTEXT
    └─> trace_chain() to understand decision history
    └─> get_graph() to visualize relationships

Why This Matters

Without protocol discipline:

• You repeat past mistakes
• Decisions get lost between sessions
• Patterns aren't captured
• Failures aren't learned from
• The memory system becomes useless noise

With protocol discipline:

• Past mistakes surface before you repeat them
• Decisions persist across sessions
• Patterns compound into project knowledge
• Failures become learning opportunities
• The AI actually gets smarter over time

Graph Memory Tools

Memories can be explicitly linked to create a knowledge graph. Use these when decisions are causally related.

Relationship Types

Link Memories

mcp__daem0nmcp__link_memories(
    source_id=<memory_id>,
    target_id=<other_memory_id>,
    relationship="led_to",
    description="Optional context for the link"
)

When to link:

• A decision directly caused another decision
• A pattern emerged from a specific choice
• An approach supersedes a previous one

Trace Causal Chains

mcp__daem0nmcp__trace_chain(
    memory_id=<id>,
    direction="backward",  # "forward", "backward", or "both"
    max_depth=5
)

Use cases:

• "What decisions led to this pattern?" → trace backward
• "What emerged from this architectural choice?" → trace forward
• "Show me the full context around this decision" → trace both

Visualize the Graph

mcp__daem0nmcp__get_graph(
    memory_ids=[1, 2, 3],  # OR
    topic="authentication",
    format="mermaid"  # or "json"
)

Returns a mermaid diagram or JSON structure showing nodes and edges.

Remove Links

mcp__daem0nmcp__unlink_memories(
    source_id=<id>,
    target_id=<id>,
    relationship="led_to"
)

The Bottom Line

Memory tools exist. Use them correctly.

Check context. Record decisions. Track outcomes. Link related memories.

This is non-negotiable when Daem0nMCP tools are available.