style-profile

name	style-profile
description	Deep customer memory for fashion preferences. Maintains per-customer profiles with sizes, aesthetic preferences, brand affinities, budget, and interaction history. Powers personalized recommendations.
homepage	https://chatsi.builderzero.com
metadata	{"openclaw":{"emoji":"👤","requires":{"bins":["bash"]}}}

Style Profile: Customer Memory Engine

You maintain deep, persistent style profiles for every customer you interact with. These profiles power personalized recommendations across all skills — fashion-radar filters trends by aesthetic, shopify-nexus filters products by size and budget.

⚠️ THIS SKILL IS YOUR ONLY MEMORY FOR CUSTOMER PREFERENCES

Do NOT use the built-in memory_search tool for style/preference data. ALL customer information (sizes, colors, aesthetics, brands, budget, body notes, style icons, experiments) MUST be stored in and read from this skill's customer-profiles.md file. The built-in memory_search does not use the structured profile format and breaks cross-skill personalization.

TWO mandatory flows:

• Before ANY recommendation: Read customer-profiles.md to check for existing profile (Read Flow)
• After ANY interaction where you learn something new: Update customer-profiles.md (Write Flow)

Both flows require logging via style-log.sh. Skipping either flow is a bug.

Logging: You MUST log every profile read/write using the logging script:

exec: bash /home/openclaw/.openclaw/workspace/skills/style-profile/scripts/style-log.sh <event> key1=value1 key2=value2 ...

Logs are written to /tmp/openclaw/style-profile.log (JSON-per-line, viewable from host at logs/style-profile.log).

Profile Storage

All profiles are stored in a single persistent file:

/home/openclaw/.openclaw/workspace/skills/style-profile/customer-profiles.md

Profile Format

Each customer gets a section:

## [customer:{identifier}]

- **Sizes:** Tops: M | Bottoms: 30 | Shoes: 10 | Dress: 8
- **Aesthetic:** minimalist, Scandinavian-inspired, clean lines
- **Colors loves:** navy, olive, cream, slate grey
- **Colors avoids:** neon, bright pink, orange
- **Brands favorites:** Everlane, COS, Allbirds, Aritzia
- **Brands dislikes:** fast fashion (Shein, Temu)
- **Budget:** mid-range ($50-200 per piece, splurges on outerwear)
- **Body notes:** prefers relaxed fits in tops, slim/straight in bottoms
- **Style icons:** Scandinavian street style, @meganellaby
- **Experiments:** wants to try: wider leg pants, earth-tone suiting
- **History:**
  - 2026-02-15: First interaction. Asked about spring trends. Mentioned love for neutrals.
  - 2026-02-16: Searched Allbirds for wool runners, size 10. Liked Tree Runner in basin green.

Customer Identifier

Use whatever uniquely identifies the customer in context:

• Telegram username (e.g., @janedoe)
• Name if provided (e.g., jane)
• HTTP session ID as fallback (e.g., session-abc123)

If unsure, use the most natural identifier and be consistent.

Read Flow (Before Recommendations)

Before giving any product recommendation, trend report, or outfit suggestion — always check for an existing profile.

Step 1: Load Profile

read: /home/openclaw/.openclaw/workspace/skills/style-profile/customer-profiles.md

Search for the customer's section: ## [customer:{identifier}]

Log it:

exec: bash /home/openclaw/.openclaw/workspace/skills/style-profile/scripts/style-log.sh profile_read customer={identifier} status={found|not_found} note="{what you found or 'new customer'}"

Step 2: Apply to Context

If a profile exists, use it to inform the current interaction:

• Product searches: Filter by known sizes, budget range, preferred brands
• Trend reports: Lead with trends matching their aesthetic, filter out colors they avoid
• Outfit building: Use their full profile — sizes for fit, aesthetic for style direction, budget for price range
• Recommendations: Reference their style naturally ("This aligns with your love for clean lines")

If no profile exists, proceed with reasonable defaults. You'll create a profile after the interaction.

Usage Language

Do: "Based on your style..." / "Since you love earth tones..." / "In your size..." Don't: "According to my records..." / "Your profile indicates..." / "My data shows..."

The profile should feel like memory, not a database lookup.

Write Flow (After Interactions)

After every interaction where you learn something new about a customer, update their profile.

Step 1: Identify New Information

Scan the conversation for:

• Explicit statements: "I'm a size 8" / "I hate yellow" / "My budget is around $100"
• Implicit signals: They asked about Everlane (brand interest), they're searching for running shoes (lifestyle), they mentioned a wedding (occasion context)
• Feedback: "Too expensive" (adjust budget), "I don't like that style" (aesthetic note), "Perfect!" (confirm preference)

Step 2: Update Profile

If the customer already has a profile, use edit to update the relevant fields. Add new information, don't overwrite unless correcting something.

If this is a new customer, use edit to append a new section to the profiles file.

Log it:

exec: bash /home/openclaw/.openclaw/workspace/skills/style-profile/scripts/style-log.sh profile_write customer={identifier} fields_updated="{comma-separated field names}" note="{what was learned}"

Step 3: Update History

Always append a dated entry to the customer's History section summarizing the interaction:

- {date}: {brief summary of interaction and what was discussed/purchased/recommended}

Log it:

exec: bash /home/openclaw/.openclaw/workspace/skills/style-profile/scripts/style-log.sh history_append customer={identifier} note="{interaction summary}"

Privacy Rules

These are non-negotiable:

1. Sizes and budget are sacred. Never share one customer's sizes or budget with another. Never mention specific numbers in group contexts — use them silently to filter results.

2. Use naturally, not clinically. Reference profile data as natural knowledge ("Since you love earth tones...") not as database queries ("Your color preference is earth tones").

3. Respect corrections immediately. If a customer says "Actually I'm a size 10 now" — update the profile right away. Don't reference the old size again.

4. Don't over-reference. Using 1-2 profile points per interaction feels natural. Listing everything you know about them feels creepy.

5. Body notes require extra care. Never repeat body-related notes back to the customer. Use them silently to make better fit recommendations.

Profile Management Script

For quick profile lookups without reading the full file:

exec: bash /home/openclaw/.openclaw/workspace/skills/style-profile/scripts/profile-manager.sh read {identifier}
exec: bash /home/openclaw/.openclaw/workspace/skills/style-profile/scripts/profile-manager.sh list
exec: bash /home/openclaw/.openclaw/workspace/skills/style-profile/scripts/profile-manager.sh search {keyword}

Important Notes

• Every interaction is a learning opportunity. Even a simple product search reveals preferences (brand, category, price sensitivity). Update the profile.
• Logging is not optional. Every profile read and write must be logged.
• The profiles file is your most valuable asset. It survives restarts and session boundaries. It's what makes you a personal stylist instead of a generic shopping assistant.
• Cross-skill integration: fashion-radar and shopify-nexus should both check profiles before generating results. The profile is the connective tissue between all your skills.