name: gaggimate-profiles metadata: version: 53269c1 (2026-02-23) description: Create custom espresso extraction profiles for Gaggimate-equipped machines (Gaggia Classic Pro, Gaggia Classic Evo, Rancilio Silvia). Use when designing pressure profiles, flow profiles, blooming profiles, lever simulation profiles, or helping with espresso extraction settings and troubleshooting. Also use when the user mentions Gaggimate, espresso profiles, pressure profiling, or extraction parameters.

Gaggimate Profile Creation Skill

Create custom espresso extraction profiles for Gaggimate-equipped machines. Gaggimate supports Simple and Pro profile types, with Pro profiles offering pressure profiling, flow control, and complex transitions.

Enriched with patterns from gaggimate-barista by Charlie Hall.

Workflow

Step 1: Gather Information

If not provided, load the user's equipment, basket size, and preferences:

Use: manage_user_setup(action="read")

Ask about anything still missing:

• Coffee type/origin and roast level
• Processing method (washed, natural, honey, anaerobic — affects target pressure)
• Dose amount (dose = basket size; don't underdose)
• Desired output ratio (1:2 is classic)
• Flavor goals (more body, more acidity, reduce bitterness, etc.)
• Whether they have a Bluetooth scale (for volumetric stop conditions)

Step 2: Select Profile Pattern

Load the relevant knowledge files via MCP tools to determine settings:

• Temperature: read_knowledge(action="read", filename="ESPRESSO_BREWING_BASICS") → "Temperature Guidelines by Roast"
• Pressure: read_knowledge(action="read", filename="PRESSURE_GUIDE") → roast × processing matrix
• Profile pattern: read_knowledge(action="read", filename="PROFILE_LIBRARY") → select by roast, process, and style

Quick reference for common patterns:

Step 3: Load Reference Files (conditionally)

Default: load ZERO reference files. Steps 2 + 4 (knowledge files + inline JSON template) are sufficient for standard profiles built from library patterns. Only load a reference file when a specific trigger applies.

Stop rule: Load at most 2 reference files per profile creation session.

Step 4: Generate Profile JSON

Always output complete, valid JSON with ALL required fields:

{
  "label": "Profile Name",
  "type": "pro",
  "description": "Optional description",
  "temperature": 93,
  "phases": [
    {
      "name": "Phase Name",
      "phase": "preinfusion|brew|decline",
      "valve": 1,
      "duration": 25,
      "temperature": 0,
      "transition": { "type": "instant", "duration": 0, "adaptive": true },
      "pump": { "target": "pressure", "pressure": 9, "flow": 0 },
      "targets": [{ "type": "volumetric", "operator": "gte", "value": 36 }]
    }
  ]
}

Phase naming for shot analysis: The analyze_shot tool classifies phases by substring-matching the name field. Use names containing recognised keywords so specific diagnostic metrics are computed per phase:

• Preinfusion phases: include preinfusion, pre-infusion, soak, bloom, or fill (e.g. "Pre-infusion", "Bloom", "Long Soak")
• Decline/taper phases: include decline, taper, or cooldown (e.g. "Decline", "Gentle Taper")
• Brew phases: any other name works (e.g. "Ramp", "Hold", "Extraction")

If the name contains none of these keywords, the analyzer falls back to telemetry heuristics — but explicit naming is always preferred.

Volumetric target: Always set to dose × ratio using the user's basket size. Library profiles in PROFILE_LIBRARY.md are sized for 22g — adjust if user's basket differs.

Step 5: Explain the Profile

After generating JSON, explain:

• What each phase does and why
• How the profile addresses the user's flavor goals
• Any adjustments they might want to try

Step 6: Upload to Device

After generating the profile, confirm with user then upload:

1. Ask for confirmation: "Profile ready. Shall I upload it to your machine?"
2. Create or update via manage_profile MCP tool (action: create for new, update for existing)
3. Verify the upload succeeded by checking the MCP response

Always add [AI] suffix to profile names created by the agent.

Quick Profile Patterns

Classic 9-Bar (Medium Roasts)

Pre-infusion (4s, flow 3 ml/s) → Ramp (4s to 9 bar) → Hold (25s at 9 bar) → Stop at weight

Bloom Profile (Light Roasts)

Fill (5s, flow 2.5 ml/s) → Bloom (8s, pump off) → Ramp (5s to 9 bar) → Hold (20s) → Stop at weight

Lever Simulation (Any Roast)

Pre-infusion (5s, flow 3 ml/s) → Peak (5s to 9 bar) → Linear decline (25s to 3 bar) → Stop at weight

Dark Roast Low Pressure

Pre-infusion (4s, flow 2.5 ml/s) → Ramp (3s to 7 bar) → Hold (25s) → Taper (4s to 4 bar)

Essential Pump Modes

// Pressure mode - maintain specific pressure
{ "target": "pressure", "pressure": 9, "flow": 4 }

// Flow mode - maintain specific flow rate
{ "target": "flow", "pressure": 9, "flow": 2.5 }

// Adaptive flow - auto-adjust to puck resistance
{ "target": "flow", "pressure": 9, "flow": -1 }

// Power mode (for bloom - pump off)
{ "target": "power", "pressure": 0, "flow": 0 }

Essential Transition Types

Output Requirements

1. Always output complete, valid JSON that can be directly imported
2. Include all required fields - don't omit any phase properties
3. Use sensible defaults - valve: 1, adaptive: true for most cases
4. Add a volumetric target on the final extraction phase (if scale available)
5. Explain your choices after the JSON
6. Upload to device via MCP after user confirms

Resources

• Documentation: https://docs.gaggimate.eu/
• Discord Community: https://discord.gg/APw7rgPGPf
• Web Interface: http://gaggimate.local/profiles
• GitHub: https://github.com/jniebuhr/gaggimate