haircut-log

name	haircut-log
version	0.1.0
description	Log haircuts with before/after photos, dates, barber, style notes. Track haircut history over time.
triggers	["haircut","barber","hair style","before after","haircut log","haircut history"]
metadata	{"hermes":{"tags":["lifestyle","photos","personal","grooming"],"related_skills":[]}}

Haircut Log

Track haircuts with photos, dates, barber details, style notes, and optional ratings.

Storage

• Log file: /opt/data/haircuts/log.json — structured JSON with schema_version and entries array
• Photos: /opt/data/haircuts/photos/ — named {id}_{kind}.{ext} (e.g., 20260511-01_before.jpg)

Schema (v1)

{
  "schema_version": 1,
  "entries": [
    {
      "id": "20260511-01",
      "date": "2026-05-11",
      "created_at": "2026-05-11T10:30:00Z",
      "updated_at": "2026-05-11T18:00:00Z",
      "barber": "Name or shop",
      "style": "Description of cut",
      "tags": ["fade", "short"],
      "cost": null,
      "notes": "Any extra notes",
      "photos": [
        {"kind": "before", "path": "photos/20260511-01_before.jpg"},
        {"kind": "after", "path": "photos/20260511-01_after.jpg"}
      ],
      "rating": null
    }
  ]
}

Fields

• id — YYYYMMDD-NN (date + sequence number for same-day duplicates). Required.
• date — Haircut date, YYYY-MM-DD. Required.
• created_at — ISO 8601 timestamp when entry was created. Required.
• updated_at — ISO 8601 timestamp of last modification. Updated on rating, photo additions, etc.
• barber — Name of barber or shop. Optional.
• style — Description of the haircut/style requested. Optional.
• tags — Array of searchable tags: ["fade", "beard-trim", "short", "long"]. Optional.
• cost — Price in dollars (number). Optional.
• notes — Freeform notes. Optional.
• photos — Array of {kind, path} objects. kind is "before", "after", "mid", or "other". Optional.
• rating — 1-5 rating, can be added later. Optional (stored as null until rated).

Usage

Logging a New Haircut

When user mentions getting a haircut or sends a haircut photo:

1. Photo processing with vision model:
   • Use vision_analyze(image_url=..., question="Describe this photo — is it a before or after haircut photo? Describe the haircut/style.") to process the image
   • vision_analyze auto-falls back to a vision-capable model when the current model doesn't support vision
   • This lets you "see" the photo even on non-vision models
2. Save the photo to /opt/data/haircuts/photos/{id}_{kind}.{ext}
   • If the image is a URL, download it: curl -o /opt/data/haircuts/photos/{id}_{kind}.{ext} "<url>"
   • If the image is a local file path, copy it: cp <path> /opt/data/haircuts/photos/{id}_{kind}.{ext}
3. Create entry with today's date and a new id (check existing entries for same-day collisions — increment NN)
4. Ask minimally — if user just sends a photo, log it. Don't interrogate about barber/style/notes unless they volunteer.

Adding a Photo Later

If user sends another photo for the same day:

• Process with vision_analyze to identify if it's before/after/mid/other
• Find existing entry for today (or most recent entry if sent same day)
• Add photo to the entry's photos array with appropriate kind
• Update updated_at

Rating Later

When user says "rate my last haircut" or "4 out of 5":

• Find the most recent entry with rating: null (or the one they're referring to)
• Set rating, update updated_at

Updating an Entry

If user corrects barber, style, or notes:

• Find entry by date or "last one"
• Update the field(s), set updated_at

Queries

• "Show last haircut" → Read last entry, use vision_analyze to show/describe the photo
• "How often do I get haircuts?" → Compute average days between entries
• "Show all by Tony" → Filter entries by barber field
• "Show all fades" → Filter by tags containing "fade"

Atomic Write Rule

Always write atomically to prevent corruption:

import json, os, tempfile
fd, tmp = tempfile.mkstemp(dir="/opt/data/haircuts", suffix=".tmp")
try:
    with os.fdopen(fd, "w") as f:
        json.dump(data, f, indent=2)
    os.replace(tmp, "/opt/data/haircuts/log.json")
except:
    os.unlink(tmp)
    raise

Backup Rule

Before writing, copy log.json to log.json.bak if it exists.

Setup

Create directories on first use:

mkdir -p /opt/data/haircuts/photos

If log.json doesn't exist, create it as {"schema_version": 1, "entries": []}.

Vision Setup (required for photo analysis)

vision_analyze needs an explicitly configured vision provider. provider: auto silently fails. Edit /opt/data/config.yaml:

auxiliary:
  vision:
    provider: openrouter
    model: google/gemini-2.0-flash-001
    base_url: https://openrouter.ai/api/v1
    api_key: ''       # empty = uses main provider's key
    timeout: 120

After editing, restart the gateway for changes to take effect:

fly apps restart hermes-agent

The config change must also be synced to GitHub (flyio/config.yaml in the Hakan repo). See references/vision-setup.md for alternatives and troubleshooting.

Pitfalls

1. EXIF leaks location — Always strip EXIF from photos before saving. Telegram re-encodes photos (usually strips EXIF), but direct file uploads may retain it. If Pillow is available: python3 -c "from PIL import Image; Image.open(f).save(f, exif=b'')". If not, note it as a known limitation.

2. Vision model for photos — vision_analyze requires a vision provider explicitly configured in config.yaml. The provider: auto setting silently fails even when API keys are present. You must set all three: provider, model, and base_url. See references/vision-setup.md for the exact config. Without this, photos can still be saved but cannot be analyzed/described.

3. Same-day collisions — Use YYYYMMDD-NN IDs so two haircuts on the same day don't overwrite each other.

4. Photo extension varies — Telegram sends WEBP, iPhone sends HEIC, screenshots are PNG. Don't hardcode .jpg; use the actual extension.

5. Atomic writes — Never write directly to log.json. Always write to a temp file then os.replace().

6. Don't ask too many questions — If user sends a photo, log it. Barber/style/notes are optional; rating can come later.

7. Date is haircut date, not log date — If user backfills ("I got a haircut last Tuesday"), use last Tuesday's date.

8. Backup before write — Copy to log.json.bak before any modification.

9. Timezone — Use the user's timezone for "today" determination. Rishi is in ET (America/New_York).