القائمة
Provide feedback on CYNIC judgments to improve future evaluations. Use when asked to correct a judgment, provide feedback, mark as correct/incorrect, or help CYNIC learn from mistakes.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".
Research methodology before coding: digest → search → patterns → wisdom → ecosystem. For when you need to research before any code, ensuring no ideas from the 500k lines are lost.
Analyze code for simplification: orphans, hotspots, giants, duplicates. 'Don't extract, burn' — three similar lines beat a premature abstraction. Use when asked to simplify, reduce complexity, or clean up code.
Evaluate code, decisions, or content with 36-dimension φ-bounded scoring. 5 axioms, geometric mean Q-Score, max 61.8% confidence. Verdicts: HOWL/WAG/GROWL/BARK. Use when asked to judge, assess, rate, score, or evaluate quality.
Philosophical grounding for technical decisions across 19 traditions. Cross-domain synthesis mapped to 5 axioms (FIDELITY/PHI/VERIFY/CULTURE/BURN). φ-bounded at 61.8%. Use when facing dilemmas, questioning assumptions, or seeking wisdom.
Evaluate any item using CYNIC's 36-dimension judgment system (5 axioms × 7 dims + THE_UNNAMEABLE). Use when asked to judge, evaluate, assess, rate, score, or analyze the quality of code, tokens, decisions, patterns, or any content. Returns Q-Score (0-100), verdict (HOWL/WAG/GROWL/BARK), and dimension breakdown.
| name | learn |
| description | Provide feedback on CYNIC judgments to improve future evaluations. Use when asked to correct a judgment, provide feedback, mark as correct/incorrect, or help CYNIC learn from mistakes. |
| user-invocable | true |
"φ learns from φ"
/learn <judgment_id> correct|incorrect [reason]
Provides feedback to improve CYNIC's judgment accuracy:
| Outcome | When to Use |
|---|---|
correct | Judgment was accurate |
incorrect | Judgment was wrong |
partial | Some parts right, some wrong |
/learn jdg_abc123 correct
/learn jdg_abc123 incorrect "missed security vulnerability"
/learn jdg_abc123 incorrect --score 35 "should have been BARK not WAG"
Use the brain_cynic_feedback MCP tool:
brain_cynic_feedback({
judgmentId: "jdg_abc123",
outcome: "correct|incorrect|partial",
reason: "explanation of feedback",
actualScore: 35 // What it should have been (0-100)
})
CYNIC uses feedback to:
Check learning state:
brain_learning({ action: "state" })
View detected biases:
brain_learning({ action: "biases" })
Trigger calibration:
brain_learning({ action: "calibrate" })
| Feedback Volume | Learning Effect |
|---|---|
| 10+ feedbacks | Initial calibration |
| 50+ feedbacks | Bias detection |
| 100+ feedbacks | Refined accuracy |
/judge - Create judgments to learn from/patterns - See learned patterns/health - Check learning system status