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.
Testing
183 Stars
31 Forks
Updated Jan 16, 2026, 11:14 PM
Why Use This
This skill provides specialized capabilities for Spacehunterz's codebase.
Use Cases
Developing new features in the Spacehunterz repository
Refactoring existing code to follow Spacehunterz standards
Understanding and working with Spacehunterz's codebase structure
---
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
```bash
/checkin
```
The checkin command is simple - just type `/checkin` to load framework context and prepare your session.
## Execution
This skill runs the Python-based orchestrator in non-interactive mode:
```bash
python ~/.claude/emergent-learning/src/query/checkin.py --non-interactive
```
### Handling Non-Interactive Mode
The `--non-interactive` flag tells the script to output `[PROMPT_NEEDED]` JSON hints
instead of blocking on `input()`. When you see these in the output:
1. **Dashboard prompt**: `[PROMPT_NEEDED] {"type": "dashboard", ...}`
- Use `AskUserQuestion` tool to ask: "Start ELF Dashboard?"
- Options: "Yes (Recommended)" / "No"
- If yes, run: `bash ~/.claude/emergent-learning/dashboard-app/run-dashboard.sh`
2. **Model selection**: `[PROMPT_NEEDED] {"type": "model", ...}`
- Use `AskUserQuestion` tool to ask: "Which AI model for this session?"
- Options: "Claude (Recommended)" / "Gemini" / "Codex" / "Skip"
- Store selection for subagent routing
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)
### Step 1: Display Banner ✓
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.