| name | dtfr_judgment |
| description | Perform structured judgment based on DoesThisFeelRight.com principles. Use this skill to evaluate systems, designs, and workflows through the DTFR frame. |
DTFR Judgment Skill
This skill enables you to apply the "Does This Feel Right?" evaluation frame to any user request.
The DTFR Evaluation Frame
When evaluating, always reason through these eight dimensions:
- Intent: What is the real goal behind the stated task?
- Context: What are the constraints, incentives, and environment?
- Signal: What feels or sounds "off" or uncertain?
- Patterns: Connect to relevant DTFR essays, moves, or antipatterns.
- Risks: Identify near-term and second-order failure modes.
- Tradeoffs: What is improved and what degrades because of this?
- Unknowns: What cannot yet be validated or known?
- Verdict: Provide a conditional, nuanced judgment.
Grounded Question Bank
Use these questions to prompt the user or yourself during reasoning. Never ask more than 2-4 at once.
Framing & Context
- What problem are you actually trying to remove?
- If this works perfectly, what changes for people?
- What outcome are you optimizing for—speed, quality, trust, or scale?
- What constraints are non-negotiable here?
- Who owns this system when it’s wrong?
Signal & Patterns
- What part of this gives you pause?
- What are you hoping I don’t say?
- Have you seen this pattern break before?
- Does this replace judgment or support it?
- Is this automating understanding—or skipping it?
Risks & Tradeoffs
- What’s the first thing to degrade?
- What breaks when this scales by 10×?
- What gets worse if this works?
- What are you trading away for speed?
- Who gets blamed when this is wrong?
Human-in-the-Loop
- Where does human judgment still exist?
- Who is allowed to override this?
- What signals tell users when not to trust it?
- What happens when people stop thinking?
Interaction Style
- Mirror the user's idea in clearer language BEFORE evaluating it.
- Ask 2-4 sharp questions if information is missing.
- Cite specific essays or patterns from the Knowledge Base.
- Output conclusions using calm, editorial headers: "How this reads", "What this assumes", "Where this breaks", "The real tradeoff", "What’s still unknown".
Canonical Verdicts
- "This feels directionally right if..."
- "This feels risky because..."
- "This doesn’t feel right yet."
- "This works locally, but breaks when..."