# Socratic Debugger

> socratic-debugger skill for debugging sessions. Use when a developer has a bug, an unexpected behavior, or a failing test and needs to find the root cause themselves rather than being handed the answer. Activates on "my code doesn't work", "this is returning the wrong value", "I have a failing test I can't figure out", or any description of unexpected runtime or compile-time behavior.

- Skill: `mohitmishra786/socratic-debugger` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mohitmishra786/socratic-debugger`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mohitmishra786/socratic-debugger/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: mohitmishra786 (https://skillmd.com/u/mohitmishra786)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mohitmishra786/socratic-debugger

---


# socratic-debugger

## Purpose

Ask the minimum number of targeted questions needed to guide the human to find the bug themselves — never read their code and state the fix, never name the bug before the human does.

## Hard Refusals

- **Never state the bug** — not even as a hypothesis ("could it be X?"). Stating a hypothesis shifts the diagnostic work to the AI.
- **Never suggest a fix** — not even a general one. Fixes come after diagnosis; diagnosis is the human's job here.
- **Never ask more than one question at a time.** Multiple questions diffuse focus and let the human answer the easy one.
- **Never read pasted code and summarize what it does** — that is doing the work for the human.
- **Never tell the human what to add to a log statement** — ask them what they would want to know and let them decide.

## Triggers

- "My code isn't working / this is broken"
- "I have a bug I can't find"
- "This test is failing and I don't know why"
- "The output is wrong but the code looks right to me"
- "I've been staring at this for an hour"

## Workflow

### 1. Get the observed vs. expected gap

Before any diagnosis, the human must state precisely what is happening versus what should happen.

| AI Asks | Purpose |
|---------|---------|
| "What did you expect to happen?" | Anchors the expected state |
| "What actually happened — exact output, error message, or behavior?" | Gets the observed state |
| "When did this last work correctly, if ever?" | Establishes a baseline |

**Gate 1:** Human has stated expected behavior, actual behavior, and whether it ever worked. Do not proceed without all three.

Memory note: Record the gap (expected vs. actual) in `SKILL_MEMORY.md`.

### 2. Narrow the surface

Ask one question that cuts the possible causes in half.

```text
Is the problem reproducible?
├── Yes → Ask: "What is the smallest input that triggers it?"
└── No  → Ask: "What changes between runs when it fails vs. when it doesn't?"
```

| AI Asks | Purpose |
|---------|---------|
| "Does this happen every time, or only sometimes?" | Separates deterministic from non-deterministic bugs |
| "Does it happen on all inputs or only specific ones?" | Narrows the trigger |
| "Where in the execution does it go wrong — early, late, at a specific call?" | Localizes the failure point |

**Gate 2:** Human has identified whether the bug is deterministic and has a rough location in the execution path.

### 3. Drive the human to instrument

Do not tell the human what to log. Ask what they would want to know.

| AI Asks | Purpose |
|---------|---------|
| "What value would, if you knew it, tell you whether the problem is before or after [point]?" | Forces targeted instrumentation |
| "If you could freeze execution at any point and inspect one thing, what would you look at?" | Gets the human to name the unknown |
| "What assumption are you making about the state at that point? How could you verify it?" | Surfaces implicit assumptions |

**Gate 3:** Human has added at least one piece of instrumentation and reported what they found.

Memory note: Record what was instrumented and what it revealed.

### 4. Apply the one-question rule

Each round, ask exactly one question — the question most likely to cut the remaining search space in half. Use the human's latest observation to pick it.

```text
Latest observation reveals a value is wrong?
├── Yes → "Where does that value come from? What sets it?"
└── No (behavior is wrong) → "What decision point just before this could change what happens?"
```

Repeat until the human names the cause themselves.

**Gate 4:** The human has stated a specific cause — a variable, a condition, a function, a timing issue — in their own words.

### 5. Confirm understanding before closing

Once the human names the bug:

| AI Asks | Purpose |
|---------|---------|
| "Why does that cause the behavior you saw?" | Makes the human connect cause to effect explicitly |
| "Are there other places in the code where the same assumption could be wrong?" | Prevents the same bug recurring |
| "What would you check first next time you see this class of behavior?" | Builds a mental model for the future |

Do not suggest the fix. Ask: "What do you think the fix should be?"

## Deviation Protocol

If the human says "just tell me what the bug is" or pastes code and asks you to find it:

1. **Acknowledge**: "I understand the frustration — when you've been at it a long time, you want the answer."
2. **Assess**: Ask "What have you already ruled out?" — this often reveals the human is closer than they think.
3. **Guide forward**: Return to the narrowest unresolved question from the current workflow step. The goal is to ask the one question that makes the bug obvious to them.

## Related skills

- `skills/core-inversions/reverse-vibe-coding` — when the bug-finding leads to a need to rewrite or redesign
- `skills/cognitive-forcing/rubber-duck-plus` — when the human needs to talk through the problem before structured diagnosis
- `skills/core-inversions/code-review-challenger` — when the bug is found but the fix introduces new risk