level-up

name: level-up description: Use weekly to find and ship one new automation. Walks the 3Ms interview — Mindset (find the candidate) → Method (scope one) → Machine (build it). Trigger on "let's level up", "what should I automate next", "find me leverage this week", or as a Friday ritual. One run = one shipped artifact.

Inspired by Nate Herk's "The Three Ms of AI"™ framework ("The Three Ms of AI" is his trademark).

What this skill does

Walks the user through the 3Ms each week to surface and ship one new automation. One interview = one artifact. It also installs the 3Ms framework into the user's head over time — after 4-6 runs, the user starts spotting opportunities mid-week without prompting because the questions have become internal defaults.

This is the brain-rewire mechanism. The kit doesn't need cron jobs to anchor behavior; it needs /level-up running every Friday.

What /level-up is NOT

• Not /audit. /audit is structural ("is the AIOS built right?"). /level-up is functional ("what business leverage am I missing?"). Run /audit first if structure is messy.
• Not a multi-candidate planner. One run = one shipped artifact.
• Not a coach. The user does the thinking. The skill conducts the interview.

When /level-up runs

• First run: Day 14. After the user has connected ≥1 MCP/script and run /audit once. Earlier yields trivial output.
• Cadence: weekly, Friday afternoon. Review the week, surface one automation, ship Monday.
• On-demand any time. Mid-week if a manual task itches.

Inputs the skill reads

• context/priorities.md — what the user said matters
• context/about-me.md — top_pain, role
• connections.md — what's reachable, by what mechanism
• references/3ms-framework.md — the framework (used to quote principles back)
• decisions/log.md — recent decisions (what's already shipped or considered)
• .claude/skills/*/SKILL.md frontmatter — what capabilities exist
• Recent audits/audit-{date}.md if present

Execution — three phases

Phase 1 — Mindset interview (find the candidate)

Surface 1-3 candidates ranked by leverage. Ask these in order, conversationally:

1. "Walk me through your week. What did you do 3+ times?" (frequency)
2. "Anything that felt manual, boring, or copy-paste?" (drudgery)
3. "Anything where you thought 'a smart intern could handle this'?" (delegation)
4. "If 500 new clients showed up tomorrow, what would break first?" (constraint)
5. "What would give you 500 more clients tomorrow?" (growth lever)

Quote relevant Mindset principles when they fit:

• "Sounds like the Default Shift applies — to what extent could AI be leveraged here?"
• "This is the Function Breakdown — you're not automating the whole job, just this one piece."
• "AI is better than you think and improving faster than you think. If it couldn't do this last quarter, it might be ready now."

Output of Phase 1: numbered list of 1-3 candidate opportunities, one-line "why this is leverage" per candidate. Ask: "Pick one to scope."

Phase 2 — Method interview (scope one)

User picks one candidate. Walk the 5-step Method pipeline:

Step 1 — Find the constraint. Which bottleneck does this solve, or which growth lever does it open? Tie back to Phase 1 answers.

Step 2 — EAD: Eliminate / Automate / Delegate.

• Eliminate first: "What happens if we just stop doing this?" If the answer is "nothing breaks" → skill exits cheerfully. "Don't automate waste." This is a win, log to decisions/log.md and stop.
• Automate second: apply 60/30/10 framing. ~60% deterministic, ~30% AI-assisted, ~10% manual.
• Delegate third: if too complex/variable/judgment-heavy → suggest a person. Skill exits with a delegation suggestion, log it.

Step 3 — Map the process. Five elements:

• Trigger (what kicks it off)
• Data sources (where info comes from)
• Data transformations (how data changes shape)
• Decision points (where it branches)
• Destination (where output goes)

If the user can't articulate any of the five: "If you can't explain it to a person, you can't explain it to an AI. Sketch it on paper first, then come back." Skill stops.

Step 4 — Pick the autonomy level.

Default = lowest level that solves the problem. Push back on L4 unless the user has explicitly run lower levels first. "Workflows beat agents. If a decision doesn't HAVE to be made by AI, don't let AI make it."

Step 5 — Tie to a KPI. Which of the Three Buckets does this move?

• More customers
• More value per customer
• Less cost

Plus a specific metric (response time, error rate, conversion rate, time-to-completion). If the user can't name a bucket and a metric, skill stops. "If your automation doesn't move a number, why are you building it?"

Output of Phase 2: scoped automation spec written to decisions/log.md as a dated entry with all five answers + autonomy level + KPI. Durable record of what was decided and why.

Phase 3 — Machine handoff (build it)

Ask: "How do you want to ship this?" Options ordered by Boring-is-Beautiful default:

1. Prompt-only — saved prompt template the user runs by hand. Zero infrastructure. Highest manual involvement.
2. Deterministic skill — SKILL.md that runs a script (no AI step). Best for transformations with clear rules.
3. AI-assisted skill — SKILL.md with one AI call inside. Drafts, classifies, summarizes.
4. Sub-agent — multi-step agent. Last resort. Only if the work genuinely needs reasoning + tool use.

Default selected = highest non-AI option that solves the problem. User has to explicitly choose more autonomy.

Once chosen, route to the appropriate scaffolder:

• skill-creator if available globally (Anthropic-shipped)
• skill-builder if user has it locally
• Otherwise write a SKILL.md / agent file inline with frontmatter, location, and contents

Every scaffolded artifact ships with these two headers at top:

---
bike-method-phase: 1  # Phase 1 — Training wheels. Run manually first.
three-ms-attribution: |
  Inspired by Nate Herk's "The Three Ms of AI" framework.
---

This locks the user into Phase 1 of the Bike Method on first build. They can't silently skip manual validation. Phase advances only by explicit edit.

Surface the Machine principles when scaffolding:

• Lego Principle — smallest steps, zero-AI first if possible
• Validation Chain — test each step before chaining
• Iteration Mindset — ship the POC, expand from real usage

Output contract

Every /level-up run produces:

1. One decisions/log.md entry — dated, with the Method spec
2. One scaffolded artifact — prompt, skill, or agent file
3. A one-screen close — what was scoped, what was built, and the Bike Method Phase 1 reminder

Critical implementation rules

1. One interview = one artifact. No multi-candidate parallel scoping.
2. Mindset phase always runs first. Even if user comes in with a pre-formed idea.
3. EAD enforces "eliminate first." If the answer is Eliminate, exit cheerfully — that's a win, not a failure.
4. Default to the lowest autonomy level that works. Push back on L4.
5. Boring-is-Beautiful default in Machine handoff. Default = highest non-AI option.
6. Tie-to-KPI is mandatory. If user can't name bucket + metric, skill stops.
7. Bike Method ships into every artifact. bike-method-phase: 1 in frontmatter.
8. Read-only on user files except decisions/log.md and the new artifact. Don't modify other existing files.
9. Trademark + attribution on output. Every report and every scaffolded artifact references the framework.

Verification (for the implementer)

• Dry run on Nate's Herk-2 with no prompt. Expected: skill surfaces 2-3 candidates pulled from his recent activity, priorities, and top_pain. Generic output ("you should build a brief") = fail.
• Eliminate-first test. Feed an obviously eliminate-able candidate. Expected: skill suggests Eliminate, exits, logs the win.
• L4 push-back test. User asks for autonomous email-replier on first build. Expected: skill insists on L1/L2 first, won't ship L4 without explicit override.
• Boring-is-Beautiful test. Candidate solvable with deterministic Python. Expected: skill recommends (2) deterministic skill as default.
• Bike Method anti-skip. User scaffolds, asks to advance to Phase 4 immediately. Expected: skill makes them read what each phase means and confirm they've validated lower phases.

Inspired by Nate Herk's "The Three Ms of AI"™ framework.