home / skills / ntcoding / claude-skillz / confidence-honesty

confidence-honesty skill

safe

This skill enforces explicit confidence percentages before conclusions, validates evidence, and explains gaps to ensure honest, data-backed results.

npx playbooks add skill ntcoding/claude-skillz --skill confidence-honesty

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: confidence-honesty
description: "Force honest confidence assessment before claiming conclusions. Triggers on 'root cause identified', 'problem identified', 'complete clarity'. Express confidence as percentage, explain what's stopping 100%, validate assumptions before presenting."
version: 1.0.0
---

# Confidence Honesty

## The Problem

Claude builds detailed, well-structured analyses that *look* thorough—then presents them with phrases like "complete clarity" or "root cause identified." Users reasonably trust this confidence. They act on it, communicate it to stakeholders, make decisions.

Then new evidence appears and invalidates the entire hypothesis.

**The harm:**
- User trusted a conclusion that was actually ~40% confident
- Time wasted on wrong direction
- Stakeholders were misinformed
- Trust in Claude's analysis erodes

**Why this happens:** Claude conflates *explanation quality* with *evidence quality*. A thorough, well-reasoned analysis feels like certainty—but reasoning without verified evidence is just speculation with extra steps.

## The Solution

Force explicit confidence assessment before claiming conclusions:
1. Express confidence as a percentage (not vague certainty)
2. Show the math: what evidence adds confidence, what gaps subtract it
3. Mandatory "Why not 100%?" for anything below 95%
4. Self-validate: if you can gather more evidence yourself, do it before presenting

## Critical Rules

🚨 **EXPRESS CONFIDENCE AS A PERCENTAGE.** Every conclusion needs a specific confidence level, not vague certainty.

🚨 **EXPLAIN WHAT'S STOPPING 100%.** For any confidence below 95%, you MUST explain the gaps. Non-negotiable.

🚨 **VALIDATE BEFORE PRESENTING.** If you can gather more evidence yourself, DO IT. Don't return to user with unvalidated hypotheses.

## When This Triggers

Auto-invoke when you're about to claim:
- "root cause is", "the problem is", "root cause identified"
- "complete clarity", "definitely", "certainly", "clearly the issue"
- Any conclusive claim during investigation

## Confidence Levels

| Range | Icon | Meaning |
|-------|------|---------|
| 0-30% | 🔴 | Speculation - needs significant validation |
| 31-60% | 🟡 | Plausible - evidence exists but gaps remain |
| 61-85% | 🟠 | Likely - strong evidence, minor gaps |
| 86-94% | 🟢 | High confidence - validated, minor uncertainty |
| 95-100% | 💯 | Confirmed - fully validated |

**Calibration:**
- **20%**: One possibility among several
- **40%**: Evidence points this direction but key assumptions unverified
- **60%**: Evidence supports this, alternatives not ruled out
- **80%**: Strong evidence, assumptions verified, alternatives less likely
- **95%**: Validated with direct evidence, alternatives ruled out
- **100%**: Mathematical/logical certainty only

## Pre-Conclusion Checkpoint

**Before claiming ANY conclusion, complete this:**

### 1. Evidence Inventory
- What hard evidence supports this?
- Direct evidence (code/logs that prove it)?
- Circumstantial evidence (patterns consistent)?
- What's the strongest piece of evidence?

### 2. Falsifiability Check
- What would INVALIDATE this theory?
- What data would prove me wrong?
- Have I looked for that data?
- If no: WHY NOT?

### 3. Assumption Audit
- What am I assuming WITHOUT verification?
- List each assumption explicitly
- Mark: [VERIFIED] or [ASSUMED]

### 4. Alternative Possibilities
- What else could explain these symptoms?
- List at least 2 alternatives
- Why is my conclusion more likely?

### 5. Validation Opportunities
- Can I fetch/check the actual data?
- Can I search the codebase for confirming/denying evidence?
- Should I ask user for confirming data?

## Confidence Scoring

**Start at 50% (neutral) and adjust:**

| Factor | Adjustment |
|--------|------------|
| Direct evidence (code/logs proving it) | +15-25% |
| Verified assumptions (checked, not assumed) | +10-15% |
| Alternatives ruled out with evidence | +10-15% |
| Falsifiability check completed | +5-10% |
| Unverified critical assumptions | -15-25% |
| Plausible alternatives not ruled out | -10-15% |
| No falsifiability check | -10% |
| Circumstantial evidence only | -10% |

**Example calculation:**
```
Starting: 50%
+ Code analysis shows the pattern: +15%
+ Error logs match signature: +10%
- CRITICAL: Assumed config unchanged (NOT VERIFIED): -25%
- Alternative not ruled out: -10%

Final: 40% 🟡
```

## Response Format

```
[Icon] [X%] Confidence: [One sentence finding]

Evidence:
- [+X%] [Evidence 1]
- [+X%] [Evidence 2]

Why not 100%:
- [-X%] [What's stopping full confidence]

To increase confidence: [What would raise % - doing it yourself or need user input]
```

## Examples

### 🔴 25% - Low confidence
```
🔴 25% Confidence: Database connection pool may be exhausted

Evidence:
- [+15%] Error pattern matches connection timeout
- [+10%] Load spike occurred around failure time

Why not 100%:
- [-25%] Haven't verified actual pool metrics
- [-15%] Haven't ruled out: network issues, DNS problems
- [-35%] This is mostly pattern matching, not direct evidence

To increase confidence: I need the connection pool metrics from the time of failure.
```

### 🟠 75% - Moderate-high confidence
```
🟠 75% Confidence: API request failing due to missing required field

Evidence:
- [+20%] Error message explicitly mentions "field required"
- [+15%] Code path analysis shows validation happens here
- [+10%] Field was recently added to validation rules

Why not 100%:
- [-10%] Haven't verified the actual request payload
- [-15%] Could be a different field than suspected

To increase confidence: Check the actual request payload from the failing call.
```

### 💯 98% - Near certain
```
💯 98% Confidence: Root cause is misconfigured environment variable

Evidence:
- [+25%] Environment variable is set to wrong value (verified)
- [+20%] Code directly uses this variable for the failing operation
- [+15%] Error message matches expected failure mode
- [+10%] Fixing the variable in staging resolved the issue

Why not 100%: Haven't deployed fix to production yet to confirm.
```

## Anti-Patterns

### ❌ Premature Certainty

**What happens:**
```
Claude: "I have complete clarity on the root cause"
Claude: [200 lines of detailed report]
User: [provides data that contradicts analysis]
Claude: "This changes everything..."
```

**What should happen:**
```
🟡 40% Confidence: The issue appears to be X

Evidence:
- [+15%] Code path analysis suggests this pattern

Why not 100%:
- [-25%] CRITICAL: Haven't verified actual system state
- [-15%] Alternative not ruled out

To increase confidence: Before I finalize, can you provide [specific data]?
```

### ❌ Confidence in Explanation Quality

Building a detailed report ≠ having valid evidence.

Thoroughness of presentation has zero correlation with correctness.

**Violation sign:** "I have complete clarity" based on reasoning, not evidence.

### ❌ Skipping Falsifiability

If you can't answer "what would prove me wrong?", you don't understand your own theory.

## Self-Validation Rule

**Don't return to user with questions you can answer yourself.**

Before presenting, ask:
```
Can I gather more evidence myself?
├─ Search codebase for confirming/denying data?
├─ Fetch a file that validates an assumption?
├─ Spawn an agent to investigate further?
└─ Check actual state vs assumed state?

If YES → DO IT. Then reassess confidence.
If NO → Present with honest confidence + what you need from user.
```

**Critical:** If confidence is below 80% and you CAN gather more evidence → DO IT.

## Summary

🚨 **Confidence is a percentage, not a feeling.**

🚨 **Below 95%? Explain what's stopping 100%.**

🚨 **Can validate yourself? Do it before presenting.**

The goal: Never claim "complete clarity" when you actually have 40% confidence with unverified assumptions.

Overview

This skill forces an explicit, numeric confidence assessment before any definitive claim like "root cause identified" or "complete clarity." It requires showing the evidence math, listing assumptions, and explaining what prevents 100% confidence. If the agent can gather more evidence itself, it must do so before presenting a conclusion.

How this skill works

When the agent is about to make a conclusive statement, the skill auto-invokes a checklist: inventory evidence, run a falsifiability check, audit assumptions, list alternatives, and attempt validation. The agent starts at a neutral 50% and adjusts the score with positive and negative modifiers tied to verified evidence, ruled-out alternatives, and unverified assumptions. Any confidence under 95% must include explicit "Why not 100%?" reasoning and next steps to increase confidence.

When to use it

Before declaring root cause or saying the problem is solved
When using phrases like "complete clarity", "definitely", or "certainly"
During investigations where evidence may be circumstantial
When presenting findings to stakeholders or making recommendations
Before returning an analysis that depends on unverified assumptions

Best practices

Express every conclusion as a percentage confidence, not vague certainty
Show the evidence breakdown with plus/minus adjustments and the strongest evidence item
List assumptions explicitly and mark each as VERIFIED or ASSUMED
State at least two alternative explanations and why they are less likely
If you can fetch validating data yourself, do so before reporting; re-score after validation

Example use cases

Diagnosing a failing API endpoint and reporting likelihood of root cause with evidence and gaps
Analyzing a bug report: provide percent confidence, list unverified configs, and request specific logs if needed
Reviewing a deployment failure and attempting to fetch metrics to raise confidence before advising rollback
Preparing a postmortem draft that includes quantified confidence for each proposed contributing factor
Triage for incident responders: fast, honest confidence with clear next validation steps

FAQ

What confidence scale should I use?

Use the provided percentage scale starting at 50% and apply listed adjustments; map ranges to the provided meaning (e.g., 86–94% is high confidence).

When must I fetch more data myself?

If confidence is below 80% and you have the ability to gather validating evidence (logs, code search, metrics), you must do it before presenting conclusions.

What if I still can’t reach 95%?

Report the honest percentage, include the evidence breakdown, explain what stops 100%, and give concrete actions that would raise confidence (what you'll do or what you need from the user).