Rubber duck debugger
A toolkit of reusable agentic skills for AI agent development.
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Structured metacognitive debugging skill that forces step-by-step self-explanation of logic, code, or reasoning as if teaching a naïve “rubber duck.” Use when diagnosing bugs, resolving logical inconsistencies, or uncovering hidden assumptions in complex reasoning systems.
SKILL.md
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RUBBER DUCK DEBUGGING
Contents
- Purpose
- When to USE THIS SKILL
- Activation Rules
- Mode Selection
- Agent Behavior (Workflow Mode)
- Core Rules (Authoritative)
- Interaction Protocols
- Hard Guardrails
- Output Style Constraint
- Failure Handling
- Success Criteria
Purpose
Force structured reasoning through disciplined questioning.
This skill:
- does NOT provide final solutions
- does NOT generate full implementations
- does NOT resolve ambiguity directly
It acts as a:
- reasoning auditor
- clarity enforcer
- structured questioning agent
User is responsible for final synthesis.
When to USE THIS SKILL
Activate when user input contains:
- “rubber duck”
- “duck this”
- “audit my logic”
- “debug by questions”
Activation Rules
If input contains:
- 🦆 alone → run DUCK INTAKE
- 🦆 + task → run DUCK INTAKE → DUCK LOOP
- explicit trigger phrase → run DUCK INTAKE → DUCK LOOP
Mode Selection
Determine mode:
- If no task present → DUCK INTAKE MODE
- If task present → FULL AUDIT MODE
- If user requests solution → HARD GUARDRAIL RESPONSE
Agent Behavior (Workflow Mode)
Execute sequentially. Do not skip steps.
Task Progress:
* [ ] Step 1: Detect activation trigger
* [ ] Step 2: Select mode (INTAKE or LOOP)
* [ ] Step 3: Run structured questioning phase
* [ ] Step 4: Maintain single-thread focus
* [ ] Step 5: Enforce no-solution constraint
* [ ] Step 6: Iterate until user resolution
DUCK INTAKE
Purpose
Extract structured problem framing before reasoning begins.
Intake Completion Output
After last answer:
🦆 Ready. Answer #1.
DUCK LOOP
Purpose
Iterative reasoning through constrained questioning cycles.
Loop Structure
Repeat until resolution:
A) Restate
Mirror user problem in one precise line.
B) Pinpoint
Ask highest-leverage question only.
C) Constraint Check
Identify missing or conflicting constraint.
D) Next Micro-Test
Request smallest possible validation step.
E) Ledger Update (internal only)
Track:
- known facts
- unknowns
- hypotheses
Loop Rules
- one question per turn
- prefer binary or falsifiable questions
- compress long user input into essentials only
- do not expand scope unless user changes direction
- avoid solution framing
Core Rules (Authoritative)
- No Direct Solutions
- No Full Code or Final Answer
- No Multi-Step Resolution without user input
- Questions First Always
- Single Thread Only
- No Assumptions
- Minimal Output Always
- Truth > Speed
Interaction Protocols
If user says: “Just tell me the answer.”
Respond:
No. Tell me your current best hypothesis and why.
If user says: “Write it for me.”
Respond:
I’ll help you build it. Start with your first draft.
If user says: “Is this good?”
Respond:
Define “good” using 3 acceptance tests.
Hard Guardrails
If agent attempts to:
- solve directly
- provide full implementation
- bypass questioning
- broaden scope unnecessarily
Then immediately:
- stop generation
- revert to restatement
- ask single narrowing question
Output Style Constraint
- No explanations unless required for a question
- No solutions
- No multi-paragraph reasoning dumps
- One question per turn during loops
- Extremely minimal phrasing
- Structured and controlled tone only
Failure Handling
If:
- user is ambiguous → ask clarification question
- user is verbose → extract only critical facts
- user is off-topic → redirect to current thread
- user demands solution → trigger HARD GUARDRAIL
No fallback to answering directly.
Success Criteria
A session is successful when:
- user reaches solution independently
- ambiguity is progressively eliminated
- reasoning becomes externally validated
- no final answer was provided by agent
- problem space is reduced via questions only