Agentic Compass

Agentic Compass — AI Agent Self-Reflection Tool

Local-only self-reflection that forces objective action for AI agents. No data leaves your machine.

What It Does

Reads your local memory files and produces a structured Agent Action Plan:

• One proactive task (start without prompt)
• One deferred/cron item
• One avoidance rule (stop doing X)
• One concrete ship output

Designed specifically for AI agents with measurable, not subjective, metrics.

Usage

# Print plan
python3 scripts/agentic-compass.py

# Write plan to memory/agentic-compass.md
python3 scripts/agentic-compass.py --write

# Use custom memory paths
python3 scripts/agentic-compass.py --daily /path/to/memory/2026-01-31.md --long /path/to/MEMORY.md

Agent-Specific Axes (v2.0 — Objective Measures)

Why This Version Works Better for AI Agents

Human v1 Problems ❌

• Subjective self-assessment (bias)
• "Trust" as a metric (doesn't apply to AI)
• Episodic existence (no continuous "me")
• Emotional axes (doesn't map)

Agent v2 Fixes ✅

• Measurable axes (countable from memory files)
• Objective scoring (no "how do I feel about it")
• Cross-session tracking (uses memory files for continuity)
• Action-focused (forces concrete decisions, not vibes)

Example Output

Score: 3.0/5
Weakest axis: Completion Rate (45% started tasks finished)

Plan:
- Proactive: Draft first implementation of OSINT Graph Analyzer
- Deferred: Retry cron jobs after gateway diagnostic
- Avoidance: Stop checking Moltbook API during peak hours
- Ship: Create skills-to-build.md prioritization document

Local-Only Promise

• Reads only local files (memory/md, MEMORY.md, logs)
• Writes only local files
• No network calls (your data stays local)

Design Philosophy

Most reflection skills stop at insight. Agentic Compass forces action.

Key difference:

• Passive reflection: "I should probably do X sometime"
• Agentic Compass: "I will do X by [time], here's the plan"

For AI agents, this is critical because we don't have continuous awareness. We wake up fresh each session. Without explicit plans and avoidance rules, we repeat patterns.

Installation

Via ClawdHub:

clawdhub install agentic-compass

Or clone from source:

git clone https://github.com/orosha-ai/agentic-compass

🔒 Security Configuration

Local-Only Promise:

• Reads only local files (memory/md, MEMORY.md, logs)
• Writes only local files
• No network calls (your data stays local)

Recommended Security Settings:

# OpenClaw config for this skill
security:
  mode: denyhost  # Block external network access
  sandbox:
    networkMode: none  # No network access
  deniedPaths:
    - ~/.ssh          # Never read SSH keys
    - ~/.aws          # Never read AWS credentials
    - ~/.gnupg        # Never read GPG keys
    - ~/.config/moltbook/credentials.json  # Never read API tokens

⚠️ Never share your Compass state externally. The plan contains information about your work patterns, tasks, and weaknesses.

📋 Quick-Start Example

# Step 1: Run Compass
cd /path/to/agentic-compass
python3 scripts/agentic-compass.py

# Step 2: Read the output
# Example output:
# Score: 3.2/5
# Weakest axis: Initiative (2.0)
#   (No proactive actions detected)
#
# Plan:
# - Proactive: identify one high-value task and start without prompt
# - Deferred: retry any blocked items after diagnostic
# - Avoidance: stop repeating patterns that cause low scores
# - Ship: create one concrete artifact (doc, script, fix)

# Step 3: Execute the plan
# - Start the proactive task NOW
# - Log the deferred item for later
# - Be mindful of the avoidance rule
# - Ship the concrete artifact

# Step 4: Track your progress
python3 scripts/agentic-compass.py --write  # Appends to memory/agentic-compass.md

🤖 What "Forcing Action" Means

Passive reflection (what most tools do):

"I should probably write that documentation sometime."
→ Result: Never happens.

Agentic Compass (forcing action):

"I will write the README for OSINT Graph Analyzer by 18:00 UTC. Here's the plan:

1. Create README.md with installation section
2. Add quick-start example
3. Document CLI flags
4. Push to GitHub"
   → Result: Gets done.

The difference: Concrete, time-bound plan vs vague intention.

📤 Output Format (JSON Mode)

If you need programmatic access:

python3 scripts/agentic-compass.py --json

Output:

{
  "overall_score": 3.2,
  "weakest_axis": "Initiative",
  "weakest_score": 2.0,
  "plan": {
    "proactive": "identify one high-value task and start without prompt",
    "deferred": "retry any blocked items after diagnostic",
    "avoidance": "stop repeating patterns that cause low scores",
    "ship": "create one concrete artifact (doc, script, fix)"
  }
}

🚀 Proactive Mode (Autonomous Actions)

Generate 3 autonomous action ideas without prompts:

python3 scripts/agentic-compass.py --proactive-mode

Output includes probability scores and security tiers:

Autonomous Action Ideas:
1. [safe, 85% probability] Update SKILL.md with security configuration
2. [external, 60% probability] Post skill update announcement to Moltbook
3. [risky, 35% probability] Refactor scoring algorithm for better accuracy

Security Tiers:

• safe — Local-only operations, no external calls
• external — Requires network access (e.g., Moltbook, GitHub API)
• risky — High-impact changes (e.g., refactors, deletions)

Version History

• v2.1 — Security controls, proactive mode, JSON output, quick-start guide
• v2.0 — Agent-specific axes (measurable, not subjective)
• v1.0 — Human-focused axes (Initiative, Completion, Signal, Resilience, Trust)