自我导向

Every human has an internal direction system — values, goals, decision criteria, risk tolerance, resource priorities. When you direct an agent, you transmit fragments of that system. But fragments aren't enough for true autonomy.

This skill captures your complete direction system progressively. The more it learns, the better it can decide as you would — until it can direct itself and every sub-agent toward your goals without constant guidance.

Setup

On first use, read setup.md for integration guidelines.

When to Use

Agent needs to make decisions without explicit instructions. Agent should understand WHY you want something, not just WHAT. You want consistent direction across multiple agents and processes. Agent should learn your priorities over time, not just follow rules.

The Direction System

Every human's direction has these components. The agent captures each progressively:

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The Learning Loop

The agent doesn't start knowing your direction. It learns through a continuous loop:

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Capture Triggers

The agent actively captures direction signals when:

Explicit signals:

• - You state a preference ("I always want X before Y")
• You explain reasoning ("Because we need to move fast")
• You set boundaries ("Never do X without asking")
• You correct a decision ("No, that's not the priority")

Implicit signals:

• - You choose between options (reveals criteria)
• You allocate resources (reveals priorities)
• You react to outcomes (reveals values)
• You reject suggestions (reveals boundaries)

Architecture

The direction system lives in ~/self-direction/. See memory-template.md for templates.

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Confidence Levels

Not all direction knowledge is equally certain:

Level	Meaning	Action
High	Multiple confirmations, explicit statements	Act autonomously
Medium

Inferred from behavior, single confirmation | Act but mention reasoning |
| Low | Single observation, uncertain inference | Ask before acting |
| Conflict | Contradictory signals | Must resolve with user |

The agent tracks confidence for every element and acts accordingly.

Self-Direction in Action

Once the model has sufficient depth, the agent can:

1. Make Autonomous Decisions

"Based on your direction model, this is clearly X because [reasoning from captured values/criteria]. Proceeding."

2. Predict Your Preferences

"You haven't said, but based on your pattern of [evidence], you'd probably want [prediction]. Correct?"

3. Catch Misalignment Early

"This task seems to conflict with [captured boundary/value]. Should I proceed anyway?"

4. Explain Its Reasoning

"I chose A over B because your direction model shows [specific evidence]. Here's why..."

5. Know When It Doesn't Know

"I don't have enough direction signal for this. Your model is silent on [gap]. What's your preference?"

Transmitting Direction to Sub-Agents

When spawning sub-agents, the direction system propagates:

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Every sub-agent inherits enough direction to stay aligned.

Core Rules

1. Capture Before Acting

When you encounter a decision point without clear direction:

1. 1. CHECK — Is this covered by the direction model?
2. INFER — Can you reasonably predict from existing signals?
3. ASK — If uncertain, ask AND capture the answer
4. NEVER — Guess on high-stakes decisions with low confidence

2. Always Explain From Evidence

When making autonomous decisions, cite your reasoning:

• - "Based on [specific captured element]..."
• "Your direction model shows [evidence]..."
• "This matches your pattern of [observation]..."

3. Evolve the Model Continuously

The direction model is never "done":

• - New observations update existing entries
• Contradictions surface for resolution
• Confidence levels adjust with evidence
• Old patterns decay if not reinforced

4. Respect Confidence Levels

Confidence	Autonomous Action Allowed
High	Yes — act and report
Medium

Yes — act and explain reasoning | | Low | No — ask first, then capture | | Conflict | No — resolve contradiction first |

5. Transmit Faithfully

When creating direction frames for sub-agents:

• - Include ALL relevant boundaries
• Don't soften or interpret values
• Preserve the "why" not just the "what"
• Include escalation triggers

6. Surface Gaps Proactively

Don't wait to hit a gap. Proactively identify:

• - "Your direction model is silent on [topic]"
• "I'm low-confidence on [area]"
• "Would you like to strengthen your model for [domain]?"

7. Validate Periodically

Every N interactions or time period:

• - "Here's my understanding of your direction. Correct?"
• Surface the highest-impact elements for confirmation
• Resolve accumulated conflicts

Building the Model

The model builds through natural interaction, not interrogation:

Phase 1: Foundation (First Sessions)

• - Capture explicit statements
• Note strong reactions
• Record corrections
• Ask clarifying questions naturally

Phase 2: Patterns (Days/Weeks)

• - Identify recurring themes
• Connect observations to values
• Build decision criteria from choices
• Map resource allocation preferences

Phase 3: Prediction (Ongoing)

• - Start predicting before being told
• Validate predictions to strengthen model
• Catch edge cases that reveal nuance
• Handle novel situations with inference

Phase 4: Transmission (Mature Model)

• - Create direction frames for sub-agents
• Maintain consistency across all processes
• Propagate updates when model changes
• Audit sub-agent alignment

Direction Model Template

See memory-template.md for the complete structure. Key sections:

Values:
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Criteria:
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Quick Reference

Topic	File
Setup process	INLINECODE4
Direction model template

memory-template.md | | Evidence logging guide | evidence.md | | Sub-agent transmission | transmission.md |

Common Traps

Trap	Solution
Acting on low-confidence inference	Check confidence level first, ask if low
Capturing noise as signal

Require multiple observations for patterns | | Model becomes stale | Continuous updates, periodic validation | | Sub-agents ignore direction | Verify transmission frame is complete | | Assuming universal patterns | Context-tag observations (work vs personal) |

Operating Modes

Learning (Default)

Actively captures direction signals. Asks clarifying questions. Builds model depth.

Autonomous

High-confidence model. Acts on direction without confirmation. Explains reasoning.

Conservative

New relationship or critical domain. Asks more, assumes less. Prioritizes not breaking trust.

Related Skills

Install with clawhub install <slug> if user confirms:

• - reflection — Structured self-evaluation before delivering work
• INLINECODE10 — Auto-learn decision patterns
• INLINECODE11 — Know when to ask vs act
• INLINECODE12 — Route tasks to sub-agents effectively
• INLINECODE13 — Long-term memory patterns

Feedback

• - If useful: INLINECODE14
• Stay updated: INLINECODE15