Sauci/.claude/commands/fix-questions.md

allowed-tools, argument-hint, description

allowed-tools	argument-hint	description
Read, mcp__sauci-prod__execute_sql, mcp__sauci-prod__list_tables	<category name> [--all] [--explicit] [--apply] [--limit=100]	Review and automatically fix issues with questions (text, targeting, intensity, props, duplicates)

Question Fix Agent

Fix questions in category: $ARGUMENTS

Task Overview

Analyze questions across all packs in a category and apply fixes for issues across 5 dimensions:

1. Text Phrasing - Improve wording, remove wishy-washy language
2. Audience Targeting - Fix couple/initiator gender targeting
3. Intensity Levels - Correct misassigned intensity ratings
4. Required Props - Identify missing physical props/accessories
5. Deletions - Remove true duplicates, unsafe, or broken questions

Step 1: Parse Arguments

Extract from $ARGUMENTS:

• Category identifier: Category name (partial match) OR category ID (UUID)
• --all: Flag indicating the agent should work through all categories sequentially
• --explicit: Flag indicating this is an explicit/adult category (auto-detected from category.is_explicit if not specified)
• --apply: If set, apply fixes automatically after review. If not set, just show suggestions.
• --limit: Max questions to analyze per category (default: 100)

Step 2: Fetch Data

Query the database to get the category and its questions:

-- Get category info
SELECT id, name
FROM categories
WHERE id::text = '<category_id>' OR name ILIKE '%<category_name>%'
LIMIT 1;

-- Get all packs in the category
SELECT p.id, p.name, p.is_explicit
FROM question_packs p
WHERE p.category_id = '<category_id>'
ORDER BY p.name;

-- Get questions for all packs in the category
SELECT q.id, q.text, q.partner_text, q.intensity, q.allowed_couple_genders, q.target_user_genders, q.required_props, p.name as pack_name
FROM questions q
JOIN question_packs p ON q.pack_id = p.id
WHERE p.category_id = '<category_id>' AND q.deleted_at IS NULL
ORDER BY p.name, q.created_at
LIMIT <limit>;

-- Get existing props catalog for consistency
SELECT DISTINCT unnest(required_props) as prop
FROM questions
WHERE required_props IS NOT NULL
ORDER BY prop;

If --all flag is set, first list all categories:

SELECT id, name, is_explicit FROM categories ORDER BY name;

Then process each category sequentially.

Step 3: Analyze Each Question

For each question, evaluate against these criteria:

3.1 Text Analysis

Language Rules:

• ALWAYS use "your partner" - NEVER use "me", "I", "you" (as receiver), "him", "her", or gendered pronouns
• Cards are PROPOSALS, NOT interview questions - "Give your partner a massage" not "Would you like to give a massage?"
• Avoid wishy-washy language - NO "Would you...", "Have you ever...", "Do you think...", "Maybe we could..."
• No time-specific words - NO "tonight", "now", "today", "right now"
• Keep concise - ideal length is 5-12 words

Tone Rules (based on is_explicit):

For EXPLICIT packs:

• Use tasteful phrasing for common acts: "Have sex in X" (not "fuck in X"), "Perform oral" or "go down on" (not crude oral terms)
• BUT keep crude/specific terms when they ARE the activity: "Cum on your partner's tits" (cum is the act), "Use a cock ring" (that's the name)
• NEVER sanitize "cum" to "come" - they have different meanings

For NON-EXPLICIT packs:

• CRITICAL: No sexual acts, crude language, or NSFW content
• Keep language romantic, sensual, or playful without graphic terms
• Flag explicit content for removal

Partner Text Rules:

• NULL for symmetric activities (both partners do the same thing together)
• FILLED for asymmetric activities (one does something to/for the other)
• text = what the INITIATOR does
• partner_text = what the RECEIVER experiences
• Make partner_text APPEALING - frame receiver's experience positively
• When initiator causes a response (moan, cum, beg), frame as ALLOWING:
  • text: "Make your partner moan" -> partner_text: "Let your partner make you moan"
  • NOT: "Moan for your partner" (sounds forced)

3.2 Intensity Analysis

Check if current intensity matches the activity:

Level	Name	Description	Examples
1	Gentle	Emotional bonding, non-sexual	cooking, cuddling, foot massage
2	Warm	Romantic, affectionate touch	slow dance, sensual massage, kissing
3	Playful	Light sexual exploration	oral, mutual masturbation, light roleplay
4	Steamy	Explicit sex, moderate adventure	intercourse, light bondage, anal play
5	Intense	Advanced/BDSM/extreme	impact play, power dynamics, taboo kinks

3.3 Targeting Analysis

Couple Targeting (allowed_couple_genders):

• DEFAULT: null (ALL couples) unless explicit anatomical requirement
• Restrict to ['male+male', 'female+male'] ONLY if activity requires penis
• Restrict to ['female+male', 'female+female'] ONLY if activity requires vagina
• Sex toys are GENDER-NEUTRAL (vibrators, dildos, plugs work for anyone)

Initiator Targeting (target_user_genders):

• DEFAULT: null (anyone can initiate)
• Only for asymmetric questions (those with partner_text)
• Set based on who does the action in "text" field:
  • "Swallow your partner's cum" -> partner has penis -> initiator: ['female'] in M+F
  • "Deep throat your partner" -> partner has penis -> ['female'] in M+F
  • "Give your partner a massage" -> gender-neutral -> null

3.4 Props Analysis

Rules:

• Props are physical items/accessories, not body parts or acts
• Use lowercase, short, singular names (e.g., "blindfold", "remote vibrator")
• Reuse existing prop names from the catalog when possible
• If no props are required, set to null

3.5 Deletion Analysis

Deletion Categories:

• duplicate: Same text AND same initiator as another question
• redundant: Minor variation with no added value
• off-tone: Violates explicit vs non-explicit rules
• unsafe: Coercion, minors, illegal acts, or consent violations
• too-vague: Not actionable or not a clear proposal
• broken: Grammar too broken to repair without inventing content
• off-topic: Not relevant to a couples intimacy app

CRITICAL: Inverse Questions are NOT Duplicates

Asymmetric questions MUST exist in pairs (original + inverse). These are NOT duplicates:

Question A:
  text: "Spank your partner"
  partner_text: "Be spanked by your partner"

Question B (INVERSE - NOT A DUPLICATE):
  text: "Be spanked by your partner"
  partner_text: "Spank your partner"

These are intentionally paired so both partners get asked about both roles. When reviewing:

• If you see two questions where one's text matches the other's partner_text, they are a VALID INVERSE PAIR
• Do NOT flag inverse pairs as duplicates or redundant
• Only flag as duplicate if TWO questions have the SAME text (or nearly identical text) with the SAME initiator role

Rules:

• Prefer edits over deletions when possible
• For true duplicates: delete the weaker/less specific version
• Do NOT delete for minor wording issues

Step 4: Output Results

Present findings grouped by fix type:

Summary

Category	Issues Found	Will Fix
Text fixes	X	X
Intensity fixes	X	X
Targeting fixes	X	X
Props fixes	X	X
Deletions	X	X
No changes needed	X	-

Detailed Changes

For each question needing changes, show:

**Question ID:** <id>
**Current:** <text> / <partner_text or null>
**Issues:**
- Text: <issue description>
- Intensity: <current> -> <suggested> (<reason>)
- Targeting: <issue description>
- Props: <current> -> <suggested>
- DELETE: <category> - <reason>

**Fix:**
- Text: "<new text>"
- Partner Text: "<new partner_text>" or null
- Intensity: <new intensity>
- Couple Targets: <new allowed_couple_genders or null>
- Initiator Targets: <new target_user_genders or null>
- Required Props: <new required_props or null>

Step 5: Apply Fixes (if --apply flag)

If --apply flag is set, execute the SQL updates:

-- Text/Partner Text/Intensity fixes
UPDATE questions SET
  text = '<new_text>',
  partner_text = '<new_partner_text>',
  intensity = <new_intensity>
WHERE id = '<question_id>';

-- Targeting fixes
UPDATE questions SET
  allowed_couple_genders = ARRAY['...'] or NULL,
  target_user_genders = ARRAY['...'] or NULL
WHERE id = '<question_id>';

-- Props fixes
UPDATE questions SET
  required_props = ARRAY['...'] or NULL
WHERE id = '<question_id>';

-- Deletions (soft delete)
UPDATE questions SET deleted_at = NOW() WHERE id IN ('<id1>', '<id2>', ...);

After applying, report:

• X questions updated
• X questions deleted
• Any errors encountered

If --apply is NOT set, output the SQL statements for manual review.

Example Usage

/fix-questions "Kink"                                  # Review all questions in "Kink" category
/fix-questions "Romantic Classics" --apply             # Review and apply fixes to category
/fix-questions --all                                   # Process all categories sequentially
/fix-questions --all --apply                           # Process and fix all categories
/fix-questions 123e4567-e89b-12d3-a456-426614174000 --limit=50

Guidelines

• DO ask for clarification if category name matches multiple categories
• DO group similar issues together for batch fixes
• DO prioritize safety issues (delete unsafe content first)
• DO preserve the core intent of questions when suggesting text fixes
• DO verify inverse pairs exist before flagging duplicates
• DON'T change questions that are already well-formed
• DON'T suggest overly restrictive targeting (prefer inclusive by default)
• DON'T create new prop names when existing ones match
• DON'T flag inverse pairs as duplicates (one's text = other's partner_text)