self-validation-loop
Run self-validation loops for triadic color systems using prediction vs observation and error minimization.
Best use case
self-validation-loop is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run self-validation loops for triadic color systems using prediction vs observation and error minimization.
Teams using self-validation-loop 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/self-validation-loop/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How self-validation-loop Compares
| Feature / Agent | self-validation-loop | 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?
Run self-validation loops for triadic color systems using prediction vs observation and error minimization.
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
# Self-Validation Loop Use when training or evaluating self-validation for 3-stream color systems. ## Inputs - seed, indices - sources: splitmix_ternary, xoroshiro_3color, gay_mcp - comparator: reafference or comparator ## Workflow 1. Predict expected colors (efference copy). 2. Observe actual colors (color_at or stream generation). 3. Compare predictions with observations. 4. Aggregate accuracy and surprise. ## Gay MCP tools - gay_seed, efference_copy, color_at, reafference, comparator, active_inference, self_model ## Metrics - accuracy = matches / total - surprise = mismatch count or summed error - pass threshold: accuracy >= 0.99 or surprise == 0 ## Output - JSON log with seed, indices, predicted, observed, errors, accuracy, surprise ## Example prompt "Run a self-validation loop over indices 1..20 and report accuracy and surprise."
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