The ELF Checkin skill is a sophisticated context management and session initialization tool designed for the Emergent Learning Framework. It automates the preparation of the AI environment by loading institutional knowledge, golden rules, and historical session data through a structured 8-step interactive workflow, ensuring the agent is fully aligned with project-specific heuristics before work begins.

When should I use checkin?

checkin is useful in the following scenarios: • Session Initialization: Automatically loading 'Golden Rules' and architectural heuristics at the start of a project to ensure the AI follows specific institutional standards. • Context Continuity: Retrieving and summarizing the most recent session history to provide a seamless transition between different work periods. • Multi-Model Orchestration: Interactively selecting and configuring the optimal AI model (Claude, Gemini, or Odex) based on the specific requirements of the upcoming task. • Workflow Governance: Checking for pending 'CEO decisions' and system health via a dashboard to align agent actions with high-level administrative priorities.

name	checkin
description	Load and review Emergent Learning Framework context, institutional knowledge, golden rules, and recent session history. Runs the checkin workflow interactively with banner, context loading, and dashboard/multi-model prompts.
license	MIT

ELF Checkin Command

Interactive workflow to load the building context before starting work.

What It Does

The /checkin command:

Shows the ELF banner with ASCII art first (before any prompts)
Queries the building for golden rules and heuristics
Displays relevant context and frameworks
Asks if you want to launch the dashboard (first checkin only)
Asks which AI model you want to use (first checkin only)
Checks for pending CEO decisions
Loads and displays recent session context

Usage

/checkin

The checkin command is simple - just type /checkin to load framework context and prepare your session.

Execution

This skill runs the new Python-based orchestrator:

python ~/.claude/emergent-learning/elf.py checkin
# OR directly:
python ~/.claude/emergent-learning/src/query/checkin.py

The orchestrator is a complete 8-step workflow:

Step 1: Display banner
Step 2: Load building context
Step 3: Display golden rules & heuristics
Step 4: Previous session summary (optional/async)
Step 5: Dashboard prompt (first checkin only, with state tracking)
Step 6: Model selection prompt (first checkin only, with persistence)
Step 7: CEO decision checking
Step 8: Ready signal

Workflow Steps (8-Step Structured Process)

Show ELF ASCII art immediately

Always shown on every checkin
Signals that framework is loading

Step 2: Load Building Context ✓

Query the learning framework

Loads golden rules (Tier 1)
Loads heuristics (Tier 2)
Loads recent patterns and learnings

Step 3: Display Golden Rules & Heuristics ✓

Parse and format context for readability

Shows rule count and key principles
Displays relevant patterns

Step 4: Previous Session Summary

Spawn async haiku agent to summarize recent work

Async execution (doesn't block)
Shows continuity with previous sessions

Step 5: Dashboard Prompt ⚡ NEW

Ask user if they want to start the dashboard

Only on first checkin (tracked via state file)
"Start ELF Dashboard? [Y/n]"
Launch in background if yes
Never asked again in same conversation

Step 6: Model Selection ⚡ NEW

Interactive prompt to select your active AI model

Only on first checkin (state-tracked)
Options: (c)laude / (g)emini / (o)dex / (s)kip
Selection stored in ELF_MODEL environment variable
Persists for subagent invocations

Step 7: CEO Decisions

Check for pending CEO decisions in ceo-inbox/

Lists count and first 3 items
Informational only

Step 8: Ready Signal ✓

Print completion message

"✅ Checkin complete. Ready to work!"
Marks first checkin complete (state file)

Key Improvements (Full Spec Compliance)

✅ Banner First - Displayed before any prompts, not after ✅ One-Time Prompts - Dashboard and model selection appear only on first checkin ✅ State Tracking - Uses ~/.claude/.elf_checkin_state to track conversation state ✅ Model Persistence - Selection stored in ELF_MODEL environment variable ✅ Structured Workflow - All 8 steps executed in proper sequence ✅ Context Parsing - Query output properly formatted for display

Interactive Prompts

Dashboard Prompt (First Checkin Only)

Start ELF Dashboard?
   The dashboard provides metrics, model routing, and system health.

Start Dashboard? [Y/n]:

Default: Yes (just press Enter)
Launches in background if accepted
Never asks again in same conversation

Model Selection Prompt (First Checkin Only)

Select Your Active Model
   Available models:
     (c)laude    - Orchestrator, backend, architecture (active)
     (g)emini    - Frontend, React, large codebases (1M context)
     (o)dex      - Graphics, debugging, precision (128K context)
     (s)kip      - Use current model

Select [c/g/o/s]:

Stores choice in ELF_MODEL environment variable
Used by subagent routing
Default: Claude (s)kip option

Integration with Building

The checkin workflow is your gateway to the building's knowledge:

Golden Rules - Constitutional principles (always loaded)
Heuristics - Reusable patterns and knowledge
Failures - What went wrong and lessons learned
Successes - What worked and can be replicated
Sessions - Previous work summaries for continuity

Running checkin at the start of each session ensures you're working with current institutional knowledge.

checkin

When & Why to Use This Skill

Use Cases