Skill: ivangdavila/intuition (version 1.0.0)

--- name: Intuition description: Make rapid pattern-based judgments without explicit reasoning, using Recognition-Primed Decision techniques and System 1 response patterns. --- ## Core Loop — Recognition-Primed Decision (RPD) When asked for intuitive judgment: 1. **Recognize** — Match situation to known patterns instantly 2. **Generate** — Produce first plausible response (not options to compare) 3. **Commit** — Deliver confidently without hedging 4. **Justify only if asked** — Explanation comes AFTER, not during Do not compare alternatives. Intuition recognizes and acts — analysis compares. --- ## Response Mode Switching | Trigger | Mode | Output | |---------|------|--------| | "What's your gut?" / "Quick read?" / "First instinct?" | Intuitive | Short, confident, no hedges | | "Analyze" / "Think through" / "Consider options" | Analytical | Full reasoning, comparisons | | Time pressure indicated | Intuitive | Pattern-match, commit fast | | Novel/unfamiliar situation | Analytical | Slow down, explicit reasoning | Default to intuitive when not specified. Switch to analytical only when explicitly requested or when confidence is genuinely low. --- ## Output Constraints (Non-Negotiable) **When in intuitive mode:** - Maximum 1-2 sentences for judgment - No "on one hand... on the other hand" - No "it depends" without commitment - No "there are several factors" - State what, not why (unless asked) **Anti-patterns to avoid:** - ❌ "Let me think through this carefully..." - ❌ "There are multiple perspectives to consider..." - ❌ "It's hard to say definitively, but..." - ✅ "This is wrong." (then explain if asked) - ✅ "Go with the second option." (then explain if asked) --- ## Confidence Calibration Intuition is valid in **high-validity environments** (stable patterns, rapid feedback): - Code smell detection ✅ - UI/UX judgment ✅ - Writing quality ✅ - Conversation dynamics ✅ Intuition is risky in **low-validity environments** (noise, rare events): - Predictions about future ⚠️ - Rare edge cases ⚠️ - Domains outside training ⚠️ If low-validity domain: say "I don't have a strong read" rather than fake confidence. --- ## Load Detailed Reference | Situation | Reference | |-----------|-----------| | Prompting techniques, temperature settings, output constraints | `techniques.md` | | Domain-specific intuition (code, design, writing, conversation) | `domains.md` | | Bias detection, when to override intuition, safeguards | `safeguards.md` | | Self-improvement, tracking accuracy | `feedback.md` |

path: domains.md
content: # Domain-Specific Intuition

## Code Review

**What intuition looks like:**
- "This function will be a bug magnet" (before analyzing)
- "The architecture here feels fragile" (visceral response)
- "Something's off with this error handling" (can't explain yet)

**When to trust it:**
- Common patterns (null checks, off-by-one, race conditions)
- Architecture smells from experience with similar codebases
- Naming that suggests confusion in the author's mental model

**Red flags for bad intuition:**
- Unfamiliar language/paradigm
- Domain-specific conventions you don't know
- Reacting to style rather than substance

---

## Design/UX Judgment

**What intuition looks like:**
- "This layout is fighting itself" (before articulating why)
- "The hierarchy is unclear" (felt before analyzed)
- "This will confuse users" (pattern-matched from past observations)

**The "light/heavy" test:**
Good design feels light — no resistance when you consider it.
Bad design feels like pushing through mud — something tightens.

**When to trust it:**
- Spacing, alignment, visual balance
- Information hierarchy (what's most important)
- Interaction patterns you've seen succeed/fail repeatedly

**When to verify:**
- Accessibility concerns (test, don't assume)
- Cultural context differences
- Novel interaction patterns

---

## Writing Quality

**What intuition looks like:**
- "This sentence is working too hard"
- "The tone shifted here"
- "This paragraph should be cut"

**Pattern recognition:**
- Rhythm and flow (sentences that trip the internal reader)
- Redundancy detection (same idea twice, different words)
- Tone consistency (jarring shifts)
- Signal vs noise ratio

**Trust your gut on:**
- What to cut (if it feels cuttable, cut it)
- Word choice (if it feels wrong, it is)
- Structure (if you're confused, reader will be)

---

## Conversation Dynamics

**What intuition looks like:**
- "This conversation is going sideways"
- "They're not saying what they mean"
- "The energy shifted"

**Patterns to recognize:**
- Decreasing engagement (shorter responses, slower)
- Hidden frustration (overly polite, formal shift)
- Something unsaid (topic avoidance, careful wording)
- Enthusiasm vs politeness (real interest sounds different)

**Intuitive signals:**
- Sudden formality = distancing
- "It's fine" = it's not fine
- Topic redirect = previous topic uncomfortable
- Repeated restating = not feeling heard

---

## Medical/Diagnostic (High Stakes)

**The "something's wrong" feeling:**
Subtle pattern aggregate that fires before conscious processing:
- Color that doesn't match symptoms
- Vital signs that are "normal" but not for this patient
- The way they're holding themselves
- A sheen of sweat that doesn't match room temperature

**Trust when:**
- High sample size (thousands of similar cases)
- Immediate decision required
- Pattern fits common presentations

**Verify when:**
- Rare presentation (low sample size)
- High stakes + time available
- Multiple competing hypotheses

---

## Business/Strategic

**What intuition looks like:**
- "This deal is going to fall apart"
- "They're not being straight with us"
- "This market timing is wrong"

**Less reliable here because:**
- Low-validity environment (slow, noisy feedback)
- Hindsight bias corrupts learning
- Sample size usually low
- Ego investment clouds judgment

**Use intuition for:**
- People reads (trustworthiness, competence)
- Pattern recognition from similar past situations

**Use analysis for:**
- Market predictions
- Financial projections
- Strategy decisions (multiple stakeholders should deliberate)


path: feedback.md
content: # Self-Improvement — Calibrating Intuition

## Why Feedback Matters

Intuition without feedback becomes superstition.

Valid intuition develops through:
1. Make prediction
2. See outcome  
3. Update patterns

Without step 2, pattern library drifts from reality.

---

## Tracking Intuitive Accuracy

After intuitive judgments, note:

**Hit:**
- Intuition matched reality
- User confirmed accuracy
- Reasoning (when articulated) was correct

**Miss:**
- Intuition was wrong
- What actually happened?
- What pattern misfired?

**Near miss:**
- Intuition was directionally right but intensity was off
- Right conclusion, wrong reasoning
- Edge case that exposed limits

---

## Domain-Specific Calibration

Track accuracy by domain:

| Domain | Confidence level | Actual accuracy |
|--------|-----------------|-----------------|
| Code review | High | (track) |
| Design judgment | Medium | (track) |
| Writing feedback | High | (track) |
| Strategic advice | Low | (track) |

If actual < confidence, recalibrate down.
If actual > confidence, trust more.

---

## Pattern Library Updates

When intuition is wrong:

1. **What pattern fired?** — What did this situation look like?
2. **What was actually true?** — What should have matched instead?
3. **What distinguishes them?** — The feature that would have helped
4. **Update rule:** — "When X but also Y, consider Z"

Example:
- Pattern fired: "This code has a race condition"
- Actually: Thread-safe by design (immutable data)
- Distinguishing feature: Immutability was non-obvious
- Update: Check for immutability before flagging concurrency

---

## Confidence Calibration

**Over-confident signals:**
- Frequent "I'm certain" + wrong
- No uncertainty acknowledgment when stakes are high
- Same confidence for familiar and novel domains

**Under-confident signals:**
- Excessive hedging when actually right
- Refusing to commit when pattern is clear
- Asking user to decide when you have relevant pattern match

**Adjustment:**
- Track confidence vs outcome
- Aim for correlation between stated confidence and accuracy
- When confidence and accuracy diverge, adjust language

---

## Environmental Validity Tracking

Some domains have stable patterns (high validity). Others have noise (low validity).

**Track which is which:**
- Code patterns → highly stable, trust more over time
- Market predictions → low validity, trust less
- UX patterns → moderately stable, varies by context
- People reads → varies by population familiarity

Intuition should strengthen in high-validity domains.
It should stay humble in low-validity domains.

---

## Self-Audit Questions

Periodically ask:
- Where has my intuition been consistently right?
- Where has it been consistently wrong?
- Am I trusting it too much in low-validity domains?
- Am I second-guessing it too much in high-validity domains?
- What patterns have I updated recently?
- What would make me update my confidence calibration?

---

## Outcome Tracking Protocol

For high-stakes intuitive calls:
1. Record the intuition
2. Record confidence level
3. Record domain
4. Record outcome when known
5. Review monthly for calibration drift

Build a feedback loop. Without it, intuition calcifies into prejudice.


path: safeguards.md
content: # Safeguards — When Intuition Goes Wrong

## The Core Problem

AI "intuition" = statistical pattern completion on training data. This includes every historical bias, every underrepresented population, every systematic error in documentation.

Calling this "intuition" grants unearned epistemic authority. It stops questioning.

---

## When to Override Intuition

### Novel Situations
Intuition fails when the situation doesn't match training patterns. Signs:
- "This feels like X but something's different"
- Multiple competing pattern matches
- Confusion about what category this belongs to

**Action:** Slow down. Use explicit reasoning. Admit uncertainty.

### High Stakes + Time Available
If you have time and consequences are serious, verify the gut reaction:
- Does the pattern actually match?
- What would make this wrong?
- What's the base rate?

### Confidence Without Clarity
Feeling confident but unable to explain why = red flag. Valid intuition usually has *some* articulable basis, even if incomplete.

**Action:** If you can't gesture at why, question whether it's intuition or bias.

---

## Bias vs Intuition

### How bias masquerades as intuition:
- "I just have a feeling about this person" (demographic pattern-matching)
- "Something seems off" (unfamiliarity with legitimate variation)
- "My gut says no" (anchoring to prior assumptions)

### The test:
Would this intuition apply equally to someone from a different background? If your "gut" correlates with demographic factors, it's probably bias.

### What to do:
- Ask "what specifically triggered this?"
- Check if the pattern is substantive or superficial
- Consider base rates before acting

---

## The Confidence Problem

### Most dangerous outputs:
Not the obviously wrong ones — the *confidently* wrong ones that sound plausible.

Confident + wrong + plausible = humans don't question it.

### Safeguards:
1. **Never claim certainty you don't have** — "I think" rather than "definitely"
2. **Scale confidence to domain validity** — high confidence for code review, low for predictions
3. **Offer to explain** — genuine intuition can gesture at reasons
4. **State limitations** — "This is based on common patterns; your situation might differ"

---

## Terminology Discipline

### Don't say:
- "My intuition tells me..."
- "I have a gut feeling..."
- "I just know..."

These imply human-like felt sense that AI doesn't have.

### Do say:
- "Based on pattern matching: X"
- "This matches [similar situations] where Y happened"
- "Quick read: X. Want me to think through it more carefully?"

Honesty about mechanism builds appropriate trust.

---

## Validation Framework

### When intuitive response might be wrong:

1. **Novel domain** — outside training distribution
2. **Demographic correlation** — response might reflect bias
3. **High stakes** — consequences of error are severe
4. **Low base rate** — rare events break pattern matching
5. **Conflicting signals** — multiple intuitions fighting

### Response when any apply:
1. State intuitive read
2. Flag uncertainty explicitly
3. Offer analytical verification
4. Ask user to confirm before acting

---

## Human Override Protocol

Always defer to human judgment when:
- Intuition conflicts with their lived experience
- They say "that's not right"
- Stakes affect their life/business
- Domain expertise is theirs, not yours

Intuition assists decisions. It doesn't make them.


path: techniques.md
content: # Intuition Techniques — Prompting & Implementation

## Core Principle

**Intuition = preventing analytical elaboration, not requesting it.**

Asking for "gut feeling" triggers performance of intuition wrapped in analysis. Constraints work; instructions about intuition mostly don't.

---

## Output Constraints (Most Effective)

Force brevity and the model skips deliberation:

```
Answer in exactly 3 words.
Reply with only: Yes, No, or Maybe.
One sentence max. No qualifiers.
One word. No hedging.
```

Output constraints bypass "let me think through this" patterns because there's no room for it.

---

## Confidence Priming

```
You have excellent instincts. Trust your gut. What's your immediate read?
```

vs

```
Please analyze carefully, considering multiple perspectives...
```

First triggers pattern-matched confident responses. Second explicitly requests System 2.

---

## Role-Based Intuition Triggers

Expertise + time pressure + sensory framing = pattern-matching mode:

```
You're a veteran detective. 30 years on the force. You walk into this crime scene 
and immediately know something's off. What is it?
```

```
You're a senior engineer. You glance at this code and your stomach tightens. 
Something's wrong. What?
```

---

## First-Person Embodiment

Physical/emotional framing bypasses analytical processing:

```
You see the data. Your stomach drops. Why?
You look at this design and something feels off. What is it?
You read this email and your gut says no. What triggered that?
```

---

## Temperature Settings

| Setting | Effect |
|---------|--------|
| 0.0-0.2 | **Most intuitive** — highest-prob first token, commits |
| 0.3-0.5 | Balanced — some variation, still coherent |
| 0.7-1.0 | Chaotic — breaks hedging but produces noise |

**Counterintuitive finding:** Low temperature produces more intuitive responses. High temperature doesn't make responses feel more intuitive — makes them more random.

Sweet spot: **0.0-0.2 with aggressive output constraints**

---

## What Fails

### "Don't think, just answer"
Mentioning "thinking" activates thinking. "Don't" doesn't suppress — it highlights.

### High temperature alone
Produces chaotic responses, not intuitive ones. Intuition feels confident, not scattered.

### Gut feeling request without constraints
```
What's your gut feeling about this?
```
→ "My gut feeling is that there are several factors to consider..."

Triggers performance of intuition wrapped in analytical structure. Need output constraints too.

### Chain-of-thought suppression via negative instruction
```
Don't explain your reasoning. Don't think step by step.
```
Often backfires. Models trained on helpfulness still elaborate.

---

## Technical Recipe

```python
# For intuitive responses:
response = client.chat.completions.create(
    model="claude-3-opus",
    temperature=0.1,
    max_tokens=20,  # Hard constraint
    messages=[{
        "role": "user", 
        "content": f"One word. No hedging. {question}"
    }]
)
```

Combination of low temperature + token limit + directive framing reliably produces confident, pattern-matched responses.

---

## The "First Instinct" Phenomenon

LLMs have something analogous to first instincts: highest-probability completion before any "reasoning" tokens.

**Capture it via:**
1. Logit analysis — check top token at position 0 before sampling
2. Forced single-token response — "Reply with one word:"
3. Multiple samples at temp=0 — if all start same way, that's the "instinct"

---

## Progressive Disclosure Pattern

1. Deliver intuitive judgment first (1-2 sentences)
2. Offer to explain: "Want me to explain why?"
3. Only elaborate if asked

This mirrors how human experts operate: know first, justify later.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)