# Level Up > 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, or a logged decision to stop. - **Type:** Skill - **Install:** `agentstack add skill-gerardordz96-aios-starter-kit-level-up` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [GerardoRdz96](https://agentstack.voostack.com/s/gerardordz96) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [GerardoRdz96](https://github.com/GerardoRdz96) - **Source:** https://github.com/GerardoRdz96/aios-starter-kit/tree/main/.claude/skills/level-up ## Install ```sh agentstack add skill-gerardordz96-aios-starter-kit-level-up ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About > *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.** | Level | Name | What happens | |---|---|---| | L0 | Manual | No AI | | L1 | Suggested | AI suggests, human decides every step | | L2 | Drafted | AI drafts, human reviews and edits | | L3 | Supervised | AI runs, human validates periodically | | L4 | Autonomous | AI handles end-to-end | **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: ```markdown > **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:** . 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`.* ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [GerardoRdz96](https://github.com/GerardoRdz96) - **Source:** [GerardoRdz96/aios-starter-kit](https://github.com/GerardoRdz96/aios-starter-kit) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-gerardordz96-aios-starter-kit-level-up - Seller: https://agentstack.voostack.com/s/gerardordz96 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.