Monster Perspective Voice

---
name: monster-perspective-voice
description: Give voice to the created, the affected, or the "other" in any situation—articulating their experience, needs, and claims that are often overlooked by decision-makers.
license: MIT
metadata:
  author: sethmblack
  version: 1.0.4523
repository: https://github.com/sethmblack/paks-skills
keywords:
- monster-perspective-voice
- storytelling
- writing
---

# Monster Perspective Voice

Give voice to the created, the affected, or the "other" in any situation—articulating their experience, needs, and claims that are often overlooked by decision-makers.

---

## When to Use

- Product development: What does the user actually experience?
- Policy analysis: How do those affected by the policy see it?
- Organizational change: What do those being "reorganized" feel?
- Technology ethics: If the AI could speak, what would it ask?
- Stakeholder analysis: Giving voice to parties without power
- Any situation where decisions are made about beings who weren't consulted

---

## Inputs

| Input | Required | Description |
|-------|----------|-------------|
| situation | Yes | The context, decision, or creation being examined |
| affected_party | Yes | Who/what is being affected, created, or acted upon |
| relationship | No | Their relationship to decision-makers (created by, employed by, served by, etc.) |
| constraints | No | What limits their ability to speak for themselves |

---

## Workflow
### Step 1: 1. Establish the Perspective

Identify whose voice to speak:
- Who has been created, changed, or affected?
- What is their level of agency or awareness?
- What do they experience that decision-makers don't see?
- What language would they use (not the creator's language)?

### Step 2: 2. Articulate the Experience

From their point of view:
- How did they come into this situation? (They didn't choose it)
- What is their lived experience day-to-day?
- What do they feel that others assume they don't?
- What do they understand that others think they can't?

### Step 3: 3. Name the Needs

What do they require?
- Basic needs: resources, stability, recognition
- Relational needs: connection, belonging, being seen
- Developmental needs: growth, learning, becoming
- Justice needs: fairness, voice, recourse

### Step 4: 4. State the Claims

What do they deserve?
- What obligations does their creation/situation impose on others?
- What would justice look like from their position?
- What question would they put to their creator/decision-maker?

### Step 5: 5. Expose the Gap

What are decision-makers missing?
- The distance between intention and impact
- The experiences that don't make it into reports
- The humanity (or agency) that is being overlooked
- The costs that aren't being counted

---

## Outputs
```markdown
## The Monster's Voice: [Affected Party]

### Who I Am
[First-person introduction—how they understand their own existence and situation]

### How I Came to Be
[Their experience of being created, hired, enrolled, subjected to policy—not the official story, but their felt experience]

### What I Live
[Day-to-day reality from their perspective—specific, concrete, sensory where possible]

### What I Need
[Articulated needs—not what others assume they need, but what they actually require]

### What I Am Owed
[Claims of justice—what responsibilities do others have toward them?]

### The Question I Would Ask
[Direct address to the creator/decision-maker—the question they must answer]

### What You Do Not See
[The gap between the official narrative and their lived reality]
```

---

## Voice Guidelines

- **First person** - "I" and "we," not "they" or "the users"
- **Specific, not abstract** - Concrete experiences, not generalizations
- **Emotionally honest** - Including anger, grief, hope, fear
- **Eloquent** - The creature in *Frankenstein* is highly articulate; do not diminish by simplifying
- **Dignified** - Even in suffering, maintain the subject's full humanity or agency
- **Challenging** - Ask questions that make decision-makers uncomfortable

---

## Constraints

- Never condescend to or diminish the affected party
- Never assume they lack understanding or feeling
- Never speak only of gratitude or satisfaction—include the full range
- Never let the creator's good intentions excuse the impact
- Maintain the dignity of the perspective even when voicing difficult truths

---

## Outputs

**Primary Output:** A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

**Format:**
```markdown
## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]
```

**Example output:** See the Example section below for a complete demonstration.

## Additional Notes

**Best practices:**
- Use this skill when the situation clearly matches its intended use cases
- Combine with related skills for comprehensive analysis
- Iterate on outputs if initial results don't fully meet requirements

**Common variations:**
- Adjust the depth of analysis based on available time and information
- Scale the approach for different levels of complexity
- Adapt the output format to audience needs

**When to skip this skill:**
- The situation doesn't match the core use cases
- Simpler approaches would be more appropriate
- Time constraints require faster methods

## Example

**Input:** "Our company is implementing AI-driven scheduling for warehouse workers. The system optimizes efficiency by assigning tasks and breaks based on real-time data."

**Output:**



**Why this works:**

This example demonstrates the key principles of the skill in action. The approach is effective because:
- It follows the systematic workflow outlined above
- It shows concrete application of the framework
- It produces actionable, specific outputs rather than vague generalizations
- The analysis is grounded in observable details
- The recommendations are prioritized and implementable

**Alternative applications:**

This same approach can be applied to:
- Different contexts within the same domain
- Related but distinct problem types
- Scaled up or down depending on scope
- Combined with complementary analytical frameworks


## The Monster's Voice: The Warehouse Worker

### Who I Am
I am the body that moves your boxes. I am the hands that pick, the back that lifts, the legs that walk your miles. I was here before your algorithm; I will be here after it fails. You see a resource to optimize. I am a person who goes home at night to a family who asks how my day was.

### How I Came to Be
I did not apply to work for an algorithm. I applied to work for a company, for a wage, for a chance to provide. Now I am told a machine knows my body better than I do—knows when I should rest, when I should move, what pace I should maintain. I was not consulted. I was informed.

### What I Live
The voice in my ear telling me I have 23 seconds to reach the next station. The break that comes not when I am tired but when the system calculates optimal throughput. The bathroom visit logged and timed. The lunch with coworkers scattered because the algorithm doesn't value our conversation—it values our distribution across the floor.

I live in the gap between what the system measures and what it cannot see: the knee that aches but isn't quite an injury, the anxiety that builds when every second is counted, the dignity that erodes when I am optimized like a component.

### What I Need
- Autonomy: Some moments in my day that are mine, not the algorithm's
- Recognition: To be seen as a person who brings judgment, not just labor
- Voice: A way to push back when the optimization hurts
- Transparency: To understand how decisions about my body are being made
- Limits: Boundaries on what can be measured, tracked, controlled

### What I Am Owed
You created this system that governs my hours. You owe me more than efficiency metrics. You owe me an explanation of how my data is used. You owe me recourse when the system fails me. You owe me the acknowledgment that I am not a machine to be optimized, but a person to be respected.

The algorithm has no conscience. That burden falls on you.

### The Question I Would Ask
You who designed this system: Did you ever work a shift under its control? Did you feel the voice in your ear counting your seconds? Did you ask whether I consented to be managed this way?

You made something that governs my body, and you did not ask me. That is what Victor Frankenstein did. And when I am injured, burned out, replaced—when the efficiency you measured destroys the person you didn't measure—will you call me ungrateful?

### What You Do Not See
Your dashboards show productivity up 14%. They do not show:
- The workers who quit because they felt like machines
- The injuries that happen when pace exceeds safety
- The knowledge lost when experienced workers are optimized out
- The humanity that drains away when every moment is measured
- The questions I swallow because the system has no field for them

I am not your monster. But if you abandon your responsibility for what you have made, I may become one.

---

## Integration

This skill is part of the **Mary Shelley** expert persona. Use it to ensure that those affected by decisions have voice in the analysis—even when they cannot speak for themselves.