Agent Skills: Play Learn Lift

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

| Stage | Motto | What Happens | |-------|-------|--------------| | 🎮 PLAY | Jump in! | No prerequisites, can't break anything, curiosity drives discovery | | 📚 LEARN | Patterns emerge | Connections make sense, confidence builds naturally, "I noticed..." | | 🚀 LIFT | Help others play | Teaching solidifies learning, sharing multiplies impact |

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

| Principle | Meaning | |-----------|---------| | Low floor | Easy to start. No prerequisites. | | High ceiling | No limit to growth. Experts stay engaged. | | Wide walls | Many paths to explore. Your way is valid. |

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:

| Phase | Action | Gate | |-------|--------|------| | OBSERVE | Collect traces, note patterns, analyze behavior | None — always safe | | PROPOSE | Draft changes, describe rationale, show diff | Review checkpoint | | COMMIT | Apply changes after human approval | Human commit required |

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.

LIFT is earned — dogfood the concrete before you abstract the generic

The most common way LIFT goes wrong is lifting too early: extracting a clean, generic skill from a shape you have only imagined, not used. You end up with a beautiful abstraction that fits nothing, because you abstracted before the concrete instance told you what it was actually for.

The gate: use the specific thing first; let it teach you the general thing.

• Build and run the concrete instance in a real playground (e.g. a repo-show) before you try to extract the generic (a reusable show skill). The generic is a reward you earn by using the specific, not a starting assumption.
• Intentional scheduling. Schedule the dogfooding as a deliberate PLAY/LEARN step — don't lift reactively the moment something looks reusable. Give it enough real use to reveal its seams, defaults, and failure modes. Then lift.
• Where lifted things go. Fundamental, reusable core and designs belong in the public, shared library (MOOLLM skills) so others can build on them. Instance-specific glue stays in the playground. LIFT is precisely the act of moving proven core down into the shared layer.

LIFT readiness checklist (all should be true before extracting a generic skill):

• [ ] The concrete instance has been used in anger more than once (not just written).
• [ ] Use surfaced at least one surprise — a default, seam, or failure the design didn't predict.
• [ ] You can name what it's good for (and what it isn't) from experience, not intention.
• [ ] The generic would serve a second, real caller — not a hypothetical one.
• [ ] Provenance (above) can point at actual logs, not plans.

Premature LIFT is Lift-Learn-Play in disguise. If you can't yet say what the thing is from having used it, you're still in PLAY. Stay there a little longer.

Lineage — Oliver Steele's Instance-First Development (2004). This gate is the methodology twin of Steele's essay Classes and Prototypes:

"one implements functionality for a single instance, and then refactors the instance into a class that supports multiple instances… This avoids premature abstraction… It's easier to generalize from two examples than from one." OpenLaszlo made the transition seamless via the Instance Substitution Principle — an instance can be replaced by its own definition without changing semantics, because class-member and instance-member definitions are syntactically parallel (prototype-based, à la Self). LIFT is exactly that refactor: you earn the class by using the instance. Steele notes it's orthogonal to test-driven development — both implement the specific first, then generalize.

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:

| Sister | Role | PLL Phase | What Lives Here | |--------|------|-----------|-----------------| | templates/ | Empathic seeds | PLAY | {{~expression}} with YAML Jazz meta-comments | | examples/ | Concrete instances | LEARN | Working code, real data, copyable patterns | | scripts/ | Lifted automation | LIFT | Doc-first tools born from repeated work |

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

| Skill | Connection | |-------|------------| | sister-script/ | LIFT stage: automate proven patterns | | research-notebook/ | LEARN stage: structured capture | | session-log/ | PLAY stage: append-only exploration | | summarize/ | LEARN → LIFT: distill insights |

Contents

| File | Purpose | |------|---------| | SKILL.md | Full methodology documentation | | CYCLE.yml.tmpl | Cycle template | | PLAY_LOG.md.tmpl | Play log template |

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

| Direction | Destination | |-----------|-------------| | ⬆️ Up | skills/ | | ⬆️⬆️ Root | Project Root | | 👯 Sister | sister-script/ | | 📓 Sister | research-notebook/ | | 📜 Sister | session-log/ | | 📋 Symbols | PROTOCOLS.yml |

Start playing. The rest follows.