Level Up

The Three Ms of AI is a framework by Nate Herk. See references/3ms-framework.md.

What this skill does

Walks the user through the 3Ms each week to surface and ship one new automation. One interview = one outcome: one shipped artifact, or one logged decision to eliminate, delegate, or stop. 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 /aios-audit. /aios-audit is structural ("is the AIOS built right?"). /level-up is functional ("what leverage am I missing?"). Run /aios-audit first if structure is messy.
• Not a multi-candidate planner. One run scopes one candidate, ending in one artifact or one logged early-exit decision.
• Not a coach. The user does the thinking. The skill conducts the interview.

When /level-up runs

• First run: after setup is real. Once the user has connected ≥1 MCP/script and run /aios-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)

Fresh clone, no history? If decisions/log.md is empty and there's no audits/ yet (a brand-new kit), don't stall. Skip the "what did you do in the repo this week" mining and run the interview straight from what the user tells you, plus top_pain in context/about-me.md and context/priorities.md. Suggest running /aios-audit once so future runs have signal, but you can still ship one small automation today.

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 your workload doubled tomorrow, what would break first?" (constraint)
5. "What would let you take on twice the work without breaking?" (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 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."

Side-effect gate (overrides the autonomy level). Before locking the level, ask: does any step send, delete, publish, pay, or deploy? If yes, that step is a mandatory human-gate regardless of the autonomy level chosen — the AI may prepare the action, a human approves the irreversible part. This carries into the scaffolded artifact. (Loop-engineering doctrine: references/agent-loops.md.)

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

• More output / more customers
• More value per unit of work
• 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?"

Step 6 — Define "done = right." How will the user know the output is correct? Pick the cheapest check that fits: a boolean test (pass/fail), a quick look, a rubric, or your eyes. Default to a functional test or a manual check. For content-quality judgments, route the check to a different model lineage — don't let the model that produced the work grade its own work (references/agent-loops.md). This line carries into the scaffolded artifact.

Output of Phase 2: scoped automation spec written to decisions/log.md as a dated entry with the Method answers + autonomy level + KPI + the "done = right" check. 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 the user has it locally (ships with this kit)
• For a sub-agent, route to agent-builder
• Otherwise write a SKILL.md / agent file inline with frontmatter, location, and contents

Every scaffolded artifact keeps standard frontmatter (name + description, only the fields skill-builder allows — don't add fields just because you can) and carries a short note block in the body, right under the frontmatter:

> **Bike Method — Phase 1 (training wheels).** Run this by hand first; advance only after you've validated it manually.
> **Side-effect gate:** any step that sends / deletes / publishes / pays / deploys stays human-gated, whatever the autonomy level.
> **Done = right when:** <boolean test / a look / a rubric / your eyes>. Route content-quality checks to a second model (different lineage).
> *The Three Ms of AI is a framework by Nate Herk.*

This is a convention, not enforcement — nothing in the kit reads a bike-method-phase field, so the reminder lives in the body where the user actually sees it, instead of an invented frontmatter key that breaks the skill-builder rule. It nudges the user to validate manually first and to keep irreversible steps gated; it doesn't mechanically block skipping. The loop-engineering doctrine behind training-wheels phases, side-effect gates, and verification lives in references/agent-loops.md.

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. Either one scaffolded artifact (prompt, skill, or agent file) or one logged early-exit decision — eliminate, delegate, or stop. Early exits are wins, not failures, and still get logged.
3. A one-screen close — what was scoped, and either what was built (with the Bike Method Phase 1 reminder) or why the run stopped early.

Critical implementation rules

1. One interview = one outcome (one artifact, or one logged early-exit decision). No multi-candidate parallel scoping.
2. Mindset phase always runs first. Even if the 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 the user can't name bucket + metric, skill stops.
7. Bike Method ships into every artifact as a body note (a convention, not a frontmatter field — nothing reads it).
8. Read-only on user files except decisions/log.md and the new artifact. Don't modify other existing files.
9. Attribution on output. Every report and every scaffolded artifact references the framework.

Verification (for the implementer)

• Dry run on a populated profile with no prompt. Expected: skill surfaces 2-3 candidates pulled from recent activity, priorities, and top_pain. Generic output ("you should build a brief") = fail.
• Fresh-clone test. Run on a brand-new kit with an empty decisions/log.md and no audits/. Expected: skill doesn't stall or error on missing history — it interviews from top_pain + priorities + what the user says, and suggests /aios-audit for next time.
• Eliminate-first test. Feed an obviously eliminate-able candidate. Expected: skill suggests Eliminate, exits, logs the win.
• L4 push-back test. User asks for an 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 conversationally walks them through what each phase means and asks them to confirm they've validated the lower phases (a nudge — the body note is a convention, not a hard lock).

The Three Ms of AI is a framework by Nate Herk. See references/3ms-framework.md.