Best use case
agentic-compass is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Teams using agentic-compass should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentic-compass/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agentic-compass Compares
| Feature / Agent | agentic-compass | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill provides specific capabilities for your AI agent. See the About section for full details.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 ```bash # 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) | Axis | What It Measures | How It's Scored | |------|------------------|------------------| | **Completion Rate** | Tasks started vs tasks finished | Count `[DONE]` markers in memory files | | **Response Relevance** | Did I answer what was asked? | Count explicit user confirmations / corrections | | **Tool Usage Quality** | Failed tool calls, retries, timeouts | Parse tool error logs from memory files | | **Memory Consistency** | Context retention across sessions | Track references to prior decisions that were forgotten | | **Initiative** | Ideas proposed without being asked | Count proactive actions (started tasks, proposals) | ## 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: ```bash git clone https://github.com/orosha-ai/agentic-compass ``` ## Version History - **v2.0** — Agent-specific axes (measurable, not subjective) - **v1.0** — Human-focused axes (Initiative, Completion, Signal, Resilience, Trust)
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