level-up

Adapted from The Three Ms of AI™. © 2026 Nate Herk. All rights reserved. The Three Ms of AI™ is a trademark of Nate Herk.

What this skill does

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

This is the brain-rewire mechanism. Servy doesn't need cron jobs to anchor this 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. Gera does the thinking. The skill conducts the interview.

When /level-up runs

• 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 Gera 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)
• references/ai-consultant-path-framework.md — the constraint-first lens (rank candidates by the business constraint they gate, not by frequency; doctor-not-pharmacist)
• 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)
6. "What are your triggers? Every time X happens — a lead lands, a specific type of email arrives, a ticket comes in — what do you HAVE to do next?" (event → obligation)
7. "What do you do every Monday? Every Friday?" (cadence)

Q6–Q7 are the trigger audit (Nate 6h course, 2026-07-11 — three chapters converge on it). Candidates they surface are usually routine-shaped: they fire on something predictable, and an automation Gera must fire by hand keeps him as the trigger — "if you're not around to trigger it, nothing happens." Expect these to route to a routine (self-firing), not a manual-fire skill, when they reach Phase 3's Skill-vs-Agent-vs-Routine call.

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. Rank by the constraint lens, not by frequency — the top candidate is the one gating a real business constraint (slows the team, gates revenue, causes downstream pain), not merely the most repetitive: "if you automate something that isn't actually constraining the business, you just saved 20 minutes nobody was waiting on." Be the doctor, not the pharmacist — name the real problem before scoping the build (references/ai-consultant-path-framework.md). Ask: "Pick one to scope."

Phase 2 — Method interview (scope one)

Gera 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 Gera 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 Gera 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 Gera can't name a bucket and a metric, skill stops. "If your automation doesn't move a number, why are you building it?"

The metric needs direction + target, not just a name — "5 leads/week today → 15/week", agreed BEFORE building starts. Seal it with the alignment test: "if we hit this number, does everyone call this a success?" If not, rescope. The target is also the STOP condition: hitting it formally ends the build phase and the automation moves to maintenance mode — improvements can come later, the heavy lifting is done; a shipped user-facing product flips to maintenance in projects/REGISTRY.md. "A clear definition of done is what keeps you from scope-creeping on yourself." (Nate 6h course, 2026-07-11 [0:19:49] + [2:14:31].)

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 Gera 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. Gera has to explicitly choose more autonomy.

Once chosen, route to the appropriate scaffolder:

• skill-creator if available globally (Anthropic-shipped)
• skill-builder (Servy 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: |
  Adapted from The Three Ms of AI™ © 2026 Nate Herk.
---

This locks every new build into Phase 1 of the Bike Method — no silently skipping 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 Gera can't name bucket + metric (with direction + target), skill stops.
7. Bike Method ships into every artifact. bike-method-phase: 1 in frontmatter.
8. Read-only on Gera's 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.

The Three Ms of AI™ is a trademark of Nate Herk. © 2026 Nate Herk. All rights reserved.