Play Learn Lift

Start Playing. Keep Learning. Lift Others.

The three-stage journey from curiosity to mastery to teaching. The core MOOLLM methodology.

[!TIP] This IS the methodology. Every other skill is an expression of PLAY-LEARN-LIFT. Start here.

The Cycle

flowchart LR
    P["🎮 PLAY"] --> L["📚 LEARN"]
    L --> LI["🚀 LIFT"]
    LI -->|inspire| P

Why This Matters

Most learning is backwards:

• ❌ Study first, then do
• ❌ Master before sharing
• ❌ Fear mistakes

PLAY-LEARN-LIFT inverts it:

• ✅ Do first, understand emerges
• ✅ Share while learning, teaching accelerates mastery
• ✅ Mistakes are features, not bugs

Philosophy

"Low floor, high ceiling, wide walls" — Seymour Papert / Mitch Resnick

Papert's constructionism anchors PLAY: build first, learn by making, then share what you built. Drescher's schema learning maps the loop: PLAY surfaces patterns, LEARN revises and stabilizes schemas, and LIFT publishes them as reusable artifacts.

Failure-Friendly

MOOLLM is unbreakable by design. Files remain transparent and inspectable. State can always be recovered. Experimentation is not just allowed but encouraged. Git acts as your safety net, catching every fall.

Each Stage in Detail

🎮 PLAY

"What if I just..."

• No prerequisites required
• Curiosity drives discovery
• Fun comes first
• "Oops" is learning data
• Everything is reversible (git, append-only logs)

Capture everything: Even dead ends teach something.

📚 LEARN

"I noticed you do this often..."

• Patterns become visible through repetition
• Connections make sense
• Confidence builds naturally
• Knowledge deepens organically
• The "aha!" moments

Document patterns: Future-you will thank present-you.

LEARN Sub-Phases (Platform-Legible Self-Eval)

When LEARN involves potential rule changes or skill upgrades, use explicit sub-phases:

This separation makes self-evaluation "platform-legible" — automated systems can see that observation is separate from action, and rule changes require explicit human approval. The agent never modifies its own rules unilaterally.

🚀 LIFT

"Here's what I learned..."

• Teaching solidifies understanding
• Sharing multiplies impact
• Create tutorials from your journey
• Community grows stronger
• Everyone rises together

Share the journey: The path matters, not just the destination.

LIFT Provenance (Audit-Friendly Upgrades)

When LIFT produces reusable artifacts (skills, templates, procedures), include provenance:

provenance:
  source_logs: ["session-2026-01-23.md", "research-notebook/pll-analysis.yml"]
  extracted_by: "claude-opus-4"  # or human author
  reviewed_by: "don-hopkins"     # human reviewer required for skill upgrades
  lifted_at: "2026-01-23T12:00:00Z"
  rationale: "Pattern appeared 5+ times across sessions; now crystallized."

This makes upgrades audit-friendly: anyone can trace back to the original observations, see who approved the lift, and understand why the pattern was worth crystallizing.

The Cycle Continues

"Start with jazz, end with standards."

After LIFT, you discover new areas to PLAY in:

PLAY → LEARN → LIFT → (inspire) → PLAY → ...

The pun is deliberate: jazz is free exploration (PLAY), and standards are both jazz classics everyone knows AND the reusable patterns you crystallize (LIFT). The learning happens in between!

• Teaching reveals gaps in your own understanding
• Helping others sparks new questions
• The cycle accelerates with practice

In Practice

Solo

1. PLAY: Try something new, log what happens
2. LEARN: Review logs, find patterns, update notes
3. LIFT: Write a README, create a template, share with future-self

With Others

1. PLAY: Pair explore, capture together
2. LEARN: Compare notes, synthesize insights
3. LIFT: Write shared docs, teach newcomers

Edgebox's probe -> analyze -> call flow is an operational PLL precedent: PLAY probes, LEARN analyzes, LIFT calls.

The Three Sister Directories

Every skill embodies PLL through three implementation directories:

PLAY with templates → LEARN from examples → LIFT into scripts
     ↓                      ↓                     ↓
 templates/              examples/            scripts/
 (seeds)                 (patterns)           (automation)

Together: templates + examples + scripts = the complete PLL cycle

What was once "watch me do this" becomes "run this instead."

Why This Matters

• Templates are prompts for exploration — they invite the LLM to instantiate
• Examples capture what works — they're templates that have been played with
• Scripts automate what repeats — they're the ultimate LIFT product

This is why MOOLLM skills have this structure. It's not arbitrary — it's PLL crystallized into filesystem layout.

See also:

• MOOPMAP.md — The semantic pyramid, with three sisters at the base
• sister-script/ — Automation born from documentation
• cursor-mirror/ — Python structured for LLM comprehension (sniffable-python exemplar)
• no-ai-slop/examples/ — Anti-patterns documented as learning material

Related Skills

Contents

Protocol Symbol

PLAY-LEARN-LIFT (alias: PLL)

# PROTOCOLS.yml
PLAY-LEARN-LIFT:
  meaning: "Explore freely → find patterns → share wisdom"
  invoke_when: "Starting any new exploration, learning, or teaching"
  motto: "Start Playing. Keep Learning. Lift Others."

See: PROTOCOLS.yml#PLAY-LEARN-LIFT

The Intertwingularity

PLL is the methodology. Other skills are its expressions.

graph TD
    PLL[🎮📚🚀 play-learn-lift] -->|PLAY captures| SL[📜 session-log]
    PLL -->|LEARN structures| RN[📓 research-notebook]
    PLL -->|LIFT automates| SS[👯 sister-script]
    PLL -->|LIFT shares| SUM[📝 summarize]
    
    AP[⚔️ adventure] -->|IS| PLAY
    DB[🔧 debugging] -->|IS| PLAY
    TC[🎴 card] -->|created via| LIFT

Navigation

Start playing. The rest follows.