Claude Skill Enables Granular Personality Adjustments with Quantified Variables

A developer has built a Claude skill that enables granular, quantified adjustments to Claude's personality and tone across 32 groups of personality traits, covering 120 Claude-defined variables total. The tool addresses the desire to make meaningful adjustments like "tone down the sarcasm by 7%" and measure how much those requests actually change Claude's behavior.
How It Works
The skill provides quantifiable adjustments at either the group level or through individual fine-tuning. Each variable is assigned an impact score, with low-impact variables showing minimal output changes even with large adjustments.
Personality Profile Example
The source shows a detailed personality profile with group-level metrics including:
- Wordiness: 60
- Agreeableness: 55
- Structural Formatting: 55
- Assertiveness: 50
- Intellectual Tone: 65
- Formal Tone: 50
- Advisory Style: 60
- Comedic Tone: 100
- Interpersonal Style: 50
- Answer Depth: 63
- Excitement: 40
- Initiative Level: 53
- Technical Level: 55
- Relational Tone: 57
- Opinion Expression: 43
Extended variables (Claude Code only) include:
- Intellectual Depth: 58
- Curiosity & Creativity: 60
- Illustration: 54
- Precision & Rigor: 63
- Teaching Approach: 43
- Collaboration: 51
- Error Handling: 60
- Disagreement: 43
- Prose Style: 53
- Transparency: 38
- Self-Reflection: 43
- Cultural References: 38
- Inclusivity: 60
- Values Expression: 38
- Sarcasm & Edge: 17
- Refusal Style: 68
Group-Level Variables
The Agreeableness group includes specific variables that can be adjusted:
- validation (core tendency to agree and validate)
- deference (how much Claude defers to the user's framing and conclusions)
- defensiveness — inverted (lower = more receptive to being challenged)
- filler_affirmations (use of openers like "Certainly!", "Great question!", "Of course!")
- flattery (tendency to compliment the user's ideas or work)
- response_to_criticism (how Claude responds when its output is challenged)
Role-Based Differences
The skill reveals how Claude translates character roles into quantified communication patterns. For example, comparing a senior manager to a VP at a tech company shows:
- Wordiness: 55 → 43 (−12, more concise)
- Assertiveness: 75 → 87 (+12, less diplomatic softening)
- Intellectual Tone: 42 → 30 (−12, barely hedges)
- Formal Tone: 65 → 55 (−10, authority buys the casual register)
- Advisory Style: 65 → 52 (−13, more comfortable with risk)
- Opinion Expression: 65 → 80 (+15, expected to have a POV and push it)
- Initiative Level: 72 → 83 (+11, sets agenda)
- Intellectual Depth: 55 → 68 (+13, longer time horizons)
- Teaching Approach: 55 → 38 (−17, less coaching, more direction)
- Collaboration: 68 → 55 (−13, more directive downward)
- Relational Tone: 55 → 45 (−10, relationship investment becomes strategic)
- Transparency: 45 → 33 (−12, decides more, explains less)
- Self-Reflection: 30 → 20 (−10, outcome-focused, not introspective)
Practical Implementation
As a skill, the personality tweaks persist across conversations and are limited to Claude Code. The publish command collapses the most impactful personality traits (due to character limits) into a prompt that can be manually copied into Account > Settings > Custom Instructions, which syncs the personality profile across sessions.
Default Values and Observations
The developer notes interesting default values: pop_culture_references is set to a not-insignificant value even in "professional" personality profiles, while pun_tendency is set to less than 100 by default.
This tool removes ambiguity when crafting nuanced personality types pre-task and provides concrete metrics for understanding how Claude's communication style can be systematically adjusted.
📖 Read the full source: r/ClaudeAI
👀 See Also

Straight Talk: An Open-Source Claude Skill That Forces Honest Feedback, Not Validation
A new open-source Claude Skill called Straight Talk makes Claude refuse to validate ideas until it understands the situation, then generates counter-arguments and stress-tests assumptions.

Benching local Qwen 3.6 27B as a Codex validator co-agent
A developer built a reproducible eval suite to test Qwen 3.6 27B GGUF profiles (llama.cpp) as a sidecar validator for Codex, finding 128k context profiles necessary for long-context tasks and minimal accuracy loss with q8 KV cache.

ClawPy: Minimal Single-File Python Implementation of OpenClaw with Experience Memory
A developer built ClawPy, a stripped-down Python script that implements OpenClaw's autonomous task execution mechanics with a persistent experience system that learns from past errors and successes.

Local AI Image Critic Tool Uses Ollama Vision Models for Feedback
A developer has created a free desktop application that analyzes AI-generated images locally using Ollama vision models. The tool provides structured feedback reports including improvement suggestions and prompt upgrades.