level-up

name	level-up
description	Weekly improvement loop. Asks 5 reflection questions, maps answers to Three Ms, recommends ONE next skill/script/connection/doc/cadence change, and produces a shippable artifact plan. Does not build anything until the user approves. Pairs with /audit.
disable-model-invocation	true

/level-up — Weekly Improvement Loop

Adapted from AIS-OS. Pairs with /audit (the score loop). Goal: convert a week's friction into one concrete improvement.

When to use

• After /audit — use the audit's top gap as input
• Friday weekly cadence (see docs/CADENCE.md)
• After a frustrating week — surface what changed and why
• Before planning a sprint — pick the highest-leverage capability to build

When NOT to use

• Mid-task — interrupts flow
• During a fire — you can't reflect during the incident; do it after
• Daily — the questions need a week of signal to answer well

Inputs

• Last /audit report (if recent)
• decisions/log.md entries from past 7 days
• ~/.claude/usage.jsonl (if available — what tools were used most)
• The operator's memory of the week

Outputs

• Updated decisions/log.md with a level-up entry
• A single proposed artifact: skill name, command name, script path, connection row, or cadence change
• A small plan to build it (file paths, estimated lines, validation steps)
• No code is written by this skill. It produces the plan, not the build.

Hard rules

• MUST recommend exactly ONE artifact. Never a menu of options.
• MUST favor the smallest useful POC. "Bike before motorcycle" (references/3ms-framework.md).
• MUST refuse to build. Output the plan, log it, exit. Building happens in a separate session via /ship or direct work.
• MUST require user approval before logging the recommendation as "accepted." If the user wants something different, log that instead.

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Steps

1. Pre-flight

• Read the most recent docs/audits/*.md (if any)
• Read last 7 days of decisions/log.md
• Optionally inspect ~/.claude/usage.jsonl for last 7 days

2. Ask the 5 questions (one at a time, do not batch)

Q1. Repetition

"What did you do 3+ times this week?"

Listen for: copy-paste flows, manual data formatting, looking up the same thing twice, repeated email patterns.

Q2. Drudgery

"What was drudgery — copy-paste, repetitive admin, formatting, manual transcription?"

Listen for: tasks the operator visibly hates. These are the highest-ROI automations because the operator will actually use the result.

Q3. Smart-intern tasks

"What was a 'smart intern' task — something AI could draft to 80% with review?"

Listen for: writing, summarising, classification, structured extraction, code-shaped work with clear acceptance criteria.

Q4. The 10x stress test

"What would break first if your workload increased 10x next month?"

Listen for: the first bottleneck. This is usually the highest-leverage thing to fix, but only if the rest of the system is healthy.

Q5. The growth lever

"What would help get 10x more customers / users / outcomes on autopilot?"

Listen for: the constraint on the upside, not just the downside. Often surfaces a missing connection or a missing capability.

3. Map answers to Three Ms

For each answer, classify:

Theme	Maps to
"I keep doing X manually"	Method → Eliminate (does it need to exist?) → Automate (script/skill?)
"I asked Claude to do X but it kept missing Y"	Mindset → Curiosity Rule → context gap, fix the reference
"I built X but barely use it"	Method → bad fit, retire to archives/, log learning
"I need a thing that does Z"	Machine → which is the right artifact (script vs skill vs agent vs recipe)?
"I'm overloaded by inbound"	Method → Eliminate first, then Find Constraint

4. Score each candidate

For every candidate that surfaces from Q1-Q5, score:

Dimension	1	2	3
Impact (hours saved per week)	<1	1-3	3+
Cost (hours to build)	8+	2-8	<2
Confidence (will I actually use it?)	low	medium	high
Reversibility (if it fails, how fast to rip out?)	days	hours	minutes

Pick the candidate with the highest (impact × confidence) ÷ cost. Tie-break on reversibility.

5. Produce the plan

Output format:

## Recommended next artifact

**Type:** skill | script | command | recipe | connection | cadence change | reference doc
**Name:** <kebab-case>
**Path:** <where it goes>
**Trigger:** <when does it activate>
**Goal:** <one sentence>
**Why this one:** <impact × confidence ÷ cost reasoning>

**POC plan (Bike Method):**
1. ...
2. ...
3. Validation: ...

**Estimated:** <hours>
**Kill switch:** <how to disable in 60s>
**Will this run on a schedule?** <no, until proven manually 1 week>

6. Confirm with user

"Proceed with this artifact, or pick something else?"

If yes: log to decisions/log.md and END. If no: ask what should be built instead, log that. If "skip this week": log "skipped — no clear winner this week." That's a valid outcome.

7. Log

## YYYY-MM-DD — Level-up: <artifact name>

**Decision:** Build <artifact> next. Reasoning: <one line>.
**Type:** <skill|script|...>
**Estimated cost:** <hours>
**Status:** Planned.
**Follow-up:** Implement via /ship or direct edit. Re-audit after build to verify movement on the targeted Four-Cs pillar.

8. END

Do not implement anything. The build is the operator's decision in a separate session.

---

Failure modes

Symptom	Cause	Fix
Operator answers "nothing notable" to all 5	Either an unusually quiet week, or skill ran too early	Defer; suggest re-running in 3-4 days. Don't fabricate a candidate.
All candidates have impact = 1	Real situation: the OS is humming and there's no obvious leverage	Log "no recommendation this week — system stable." That's a valid outcome.
Same recommendation surfaced 3 weeks ago and was ignored	Operator is dodging	Surface this honestly: "This was recommended in and not built. Build, defer formally, or remove from candidates."
Operator wants to build something not on the candidate list	Often valid — they have context this skill doesn't	Log their choice. Don't argue. The Curiosity Rule cuts both ways.

Reference files

• references/3ms-framework.md — the mindset
• references/four-cs-framework.md — the system structure
• references/skill-building-framework.md — how to actually build the artifact next
• docs/CAPABILITIES.md — the registry the artifact will join
• decisions/log.md — where this run lands

Improvement loop

After every run:

1. Did the operator build the recommended artifact? If no → recommendation is wrong, or operator priorities shifted (both are signal)
2. Did the next /audit show movement on the targeted pillar? If no → recommendation didn't address the root cause
3. Did the operator come back with the same drudgery in Q2 next week? If yes → previous recommendation didn't deliver