work-operating-model-activation

Work Operating Model Activation

A conversation-first workflow that interviews you about how your work actually runs, stores the answers as structured Open Brain data, and generates agent-ready operating files. [!NOTE] If you are coming from the Bring Your Own Context post or want the article-friendly entrypoint, start with Bring Your Own Context. This recipe is the structured profiling engine that BYOC uses under the hood.

What It Does

This recipe adds a dedicated MCP server plus schema for a 45-minute elicitation workflow. The interview runs in five fixed layers:

1. operating rhythms
2. recurring decisions
3. dependencies
4. institutional knowledge
5. friction

Each approved layer is saved into structured tables, summarized into one durable Open Brain thought through your existing core connector, and made available for later querying and export generation.

This recipe depends on the canonical Work Operating Model skill, which owns the interview behavior. The recipe owns the data model, remote MCP server, and export snapshots. The higher-level Bring Your Own Context recipe packages this workflow together with context extraction prompts and the portable bundle contract.

Prerequisites

• Working Open Brain setup (guide)
• Existing core Open Brain connector with search_thoughts and capture_thought
• AI client that supports reusable skills or prompt packs
• Supabase CLI installed and linked to your project
• Canonical Work Operating Model skill

Credential Tracker

WORK OPERATING MODEL ACTIVATION -- CREDENTIAL TRACKER
----------------------------------------------------

FROM YOUR OPEN BRAIN SETUP
  Project URL:           ____________
  Secret key:            ____________
  Project ref:           ____________
  MCP Access Key:        ____________
  Core Open Brain tools available:  yes / no

GENERATED DURING SETUP
  Default User ID:       ____________
  Function URL:          ____________
  MCP Connection URL:    ____________
  Current profile version: ____________

----------------------------------------------------

Steps

1. Install the skill dependency

Follow the installation steps in the Work Operating Model skill. The skill handles the interview, confirmation gates, contradiction pass, and summary-thought capture.

2. Run the schema

Open your Supabase SQL Editor and run schema.sql:

https://supabase.com/dashboard/project/YOUR_PROJECT_ID/sql/new

This creates:

• operating_model_profiles
• operating_model_sessions
• operating_model_layer_checkpoints
• operating_model_entries
• operating_model_exports

It also adds the helper RPC functions operating_model_start_session() and operating_model_save_layer() for atomic session and layer persistence.

3. Generate your default user ID

This recipe is single-user on purpose. Generate one UUID and reuse it for future sessions:

uuidgen | tr '[:upper:]' '[:lower:]'

Save it to your credential tracker, then set it in Supabase:

supabase secrets set DEFAULT_USER_ID=your-generated-uuid

4. Deploy the MCP server

Follow the Deploy an Edge Function guide using these values:

Setting	Value
Function name	work-operating-model-mcp
Download path	recipes/work-operating-model-activation

This function uses:

• SUPABASE_URL
• SUPABASE_SERVICE_ROLE_KEY
• MCP_ACCESS_KEY
• DEFAULT_USER_ID

5. Connect it to your AI client

Use the Remote MCP Connection pattern.

Setting	Value
Connector name	Work Operating Model
URL	Your function URL with the same MCP key pattern you use for your other OB1 servers

Keep your core Open Brain connector enabled too. This recipe stores structured data in its own tables, but the skill still uses the base search_thoughts and capture_thought tools for hints and summary memory writes.

6. Start the interview

With both connectors enabled and the skill installed, prompt your AI client:

Use the Work Operating Model workflow to interview me and build my operating model.

The skill should:

1. call start_operating_model_session
2. run the five layers in order
3. show a checkpoint summary after each layer
4. wait for your confirmation before saving
5. save the layer with save_operating_model_layer
6. capture one summary thought through your core Open Brain connector
7. run a contradiction pass
8. call generate_operating_model_exports

7. Review the exports

At the end of a successful run, the MCP server stores and returns:

• operating-model.json
• USER.md
• SOUL.md
• HEARTBEAT.md
• schedule-recommendations.json

These are stored in operating_model_exports, so they still exist even if your client cannot write files locally.

Available MCP Tools

1. start_operating_model_session Create or resume the active interview run. Returns profile status, session status, completed layers, pending layer, session checkpoints, and latest approved checkpoints.

2. save_operating_model_layer Atomically upserts the approved layer checkpoint plus canonical entries for that layer, then advances the session to the next layer or review state.

3. query_operating_model Reads the latest operating model or a filtered slice by layer, keyword, cadence, stakeholder, unresolved_only, or friction_priority.

4. generate_operating_model_exports Renders and stores the final JSON/markdown artifacts after all five layers are approved.

Expected Outcome

When this recipe is working correctly:

• a first session creates version 1 of a structured operating model
• pausing after layer 2 and restarting later resumes at the correct pending layer
• each approved layer writes one checkpoint plus canonical entries into the recipe tables
• the skill captures six summary thoughts in your core Open Brain:
  • one per layer
  • one final synthesis
• query_operating_model can find things like Monday planning block, finance email dependency, or handoff friction
• generate_operating_model_exports returns all five artifact blobs even if no local files are written

Troubleshooting

Issue: start_operating_model_session says no environment variable is configured Solution: Verify DEFAULT_USER_ID was set with supabase secrets set DEFAULT_USER_ID=... and redeploy the function if needed.

Issue: The skill can see the recipe connector but not search_thoughts or capture_thought Solution: This recipe does not replace the core Open Brain server. Re-enable your base connector alongside this one.

Issue: Export generation fails with missing layers Solution: At least one approved checkpoint must exist for each of the five layers. Resume the session and finish the missing layer summaries before exporting.

Issue: Querying by friction priority returns nothing Solution: friction_priority reads from details.priority, so the saved friction entries need low, medium, or high in that field.

Next Steps

• Feed USER.md, SOUL.md, and HEARTBEAT.md into any agent platform that uses operating files.
• Re-run the workflow quarterly or after a major role change to create a new version.
• Use the MCP Tool Audit & Optimization Guide once you add this server so your capture/query/admin tool surface stays manageable.