gaggimate-profiles

name: gaggimate-profiles 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.

Workflow

Step 1: Gather Information

If not provided, check user-setup.md (including Active Coffee section) and the coffee's README.md. Ask about anything still missing:

• Coffee type/origin and roast level
• Processing method (washed, natural, anaerobic — affects target pressure)
• Dose amount (dose = basket size from user-setup.md; 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

Consult these knowledge files to determine settings:

• Temperature: knowledge/ESPRESSO_BREWING_BASICS.md → "Temperature Guidelines by Roast"
• Pressure: knowledge/PRESSURE_GUIDE.md → roast × processing matrix
• Profile pattern: knowledge/PROFILE_LIBRARY.md → select by roast, process, and style

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 }]
    }
  ]
}

Volumetric target: Always set to dose × ratio using the user's basket size from user-setup.md. Library profiles in PROFILE_LIBRARY.md are sized for 22g.

Duration vs volumetric stop: On phases with a volumetric stop condition, the volumetric target controls the final cup weight — it's the real exit condition. The phase duration is a safeguard (timeout) in case something goes wrong (choked puck, scale disconnect). Set duration generously — at least 1.5× the expected time to reach the volumetric target — so it never cuts the shot short under normal conditions.

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: Save Profile to Repository

Repo first, device second. The JSON file in coffees/ is the source of truth. Save here BEFORE uploading to the device.

1. Find or create the coffee's directory: use the active coffee directory from user-setup.md if the profile is for the active coffee, otherwise coffees/{roaster}-{coffee-name}/
   • If the directory exists, use it
   • If not, create it with a minimal README.md (Bean Profile table with known info, empty Profiles and Tasting Notes sections)
2. Write the profile JSON to coffees/{coffee-dir}/{profile-style}.json (kebab-case, e.g., natural-bloom.json, turbo.json)
   • Overwrite existing files on update—git tracks iterations
3. Update the Profiles table in the coffee's README.md with: Profile name, Style, Temp, Pressure, Ratio, and link to the JSON file
4. Remove .gitkeep from coffees/ if present

Step 7: Private Repo Commit

1. Read .data-repo-path at the project root.
   • If absent: skip silently (user has no private repo — this is expected for new setups).
   • If present: proceed.
2. Run as separate Bash calls (no chaining, no git -C), substituting {private_repo} with the path from .data-repo-path:
   • git --git-dir={private_repo}/.git --work-tree={private_repo} add -A
   • git --git-dir={private_repo}/.git --work-tree={private_repo} commit -m "gaggimate-profiles: {profile-name} for {coffee-name}"
   • git --git-dir={private_repo}/.git --work-tree={private_repo} push
3. If push fails: inform the user — "Private repo push failed — changes saved locally. Run git push manually in {private_repo_path} when credentials are available."

Step 8: Upload to Device

After saving to repo, confirm with user then upload:

1. Ask for confirmation: "Profile saved to repo. 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

Quick Reference Index

Note: This is a human-readable index, not a loading instruction. See Step 3 for when to load each file.

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. Save to coffees/ directory alongside the coffee's README.md (repo first)
7. Upload to device via MCP after user confirms (device second)