/improve

name: improve
description: "Use this skill to autonomously improve any other skill in solo-founder-superpowers. Point it at a skill (e.g., 'improve the seo skill') and it runs an autoresearch-style loop: diagnoses weaknesses, experiments with improvements, evaluates each change via A/B comparison, keeps winners, discards regressions, and reports results. Originals are always preserved — nothing changes until you approve."

Improve

Autonomous skill improvement loop inspired by [karpathy/autoresearch](https://github.com/karpathy/autoresearch). You modify a skill, evaluate whether it got better, keep or discard, repeat.

**Your originals are always safe.** The loop works on a copy. Nothing changes until you say so.

---

Setup

When the user says "improve [skill name]":

1. **Locate the skill.** Read `skills/<name>/SKILL.md`. If it doesn't exist, ask the user which skill they mean.

2. **Back up the original.** Copy the full `skills/<name>/` directory to `eval/experiments/<name>/`. Create `eval/experiments/` if it doesn't exist.

   mkdir -p eval/experiments/<name>
   cp skills/<name>/SKILL.md eval/experiments/<name>/SKILL.md
   cp skills/<name>/*.md eval/experiments/<name>/  (if supporting files exist)

3. **Read the design philosophy.** Read `CLAUDE.md` to ground yourself in the plugin's standards. The rubric below is derived from these.

4. **Read neighbor skills.** Identify skills with overlapping scope (check the Related Skills section, check skills in the same category from CLAUDE.md). Read their descriptions — you need to know the boundaries.

5. **Initialize the log.** Create `eval/results.tsv` (or append if it exists from a previous run):

   experiment	skill	tier	dimension	score_before	score_after	status	change_description

6. **Run the baseline diagnosis.** Score the original against the rubric (see below). Log it as experiment 0.

7. **Show scores and ask for focus.** Print the rubric scores in a compact tiered table:

   Tier 1 (Critical):  trigger-precision=1  trigger-phrases=1  checklists=2  tell-ai-prompts=2  → 6/12
   Tier 2 (Important): boundary=1  tool-specific=N/A  founder-pov=2  mistakes=1             → 4/9
   Tier 3 (Polish):    conciseness=✓  disclosure=✗  scannable=✓  cross-refs=✗
   Total: 10/21

(Example shows a knowledge skill where tool-specific is N/A. For implementation skills, all 8 dimensions apply and max is /24.) Then ask: > "I'll focus on the lowest-scoring dimensions, Tier 1 first. Want me to auto-prioritize (default) or focus on specific areas?"

If the user says "go," "auto," or anything non-specific → auto-prioritize (Tier 1 first, then Tier 2). If the user names specific dimensions → focus the loop on those, skip others.

---

Rubric: Diagnosing Weaknesses

Score each dimension 0-3 (0=missing, 1=weak, 2=adequate, 3=strong). Dimensions are tiered by impact.

Tier 1 — Critical (scored 0-3, experimented first)

These determine whether the skill fires and delivers value. All must reach ≥2 before moving to Tier 2.

• **Trigger precision:** Does the description activate for the right prompts and NOT for neighbor skills' prompts?
• **Trigger phrases:** Does it include phrases users actually say (natural language, not jargon)?
• **Actionable checklists:** Does it have concrete workflows with checkboxes?
• **"Tell AI:" prompts:** Does it include copy-paste prompts founders can use with any AI tool?

Tier 2 — Important (scored 0-3, experimented after Tier 1 ≥2)

These improve quality but only matter if the skill fires correctly.

• **Boundary clarity:** Does it explicitly state what this skill is NOT for and where to go instead?
• **Tool-specific guidance (conditional):** Does it differentiate advice by tool (Claude Code vs Lovable vs Replit)? **Mark N/A** for knowledge/decision skills where the same guidance applies regardless of tool — e.g., compliance, hiring, legal, pricing, finances, market-research, customer-research, validate, analytics, retention. If the skill's advice changes depending on which tool runs it, score it. If not, skip it and reduce the denominator.
• **Founder perspective:** Is it written for a non-technical founder, not a developer?
• **Common mistakes:** Does it cover what founders typically get wrong?

Tier 3 — Polish (binary checklist, batch pass at end)

Not scored individually. Handled as a single cleanup pass after the main loop converges.

• [ ] **Conciseness:** Avoids explaining concepts Claude already knows?
• [ ] **Progressive disclosure:** SKILL.md first, supporting files for depth?
• [ ] **Scannable:** Headers, tables, short paragraphs — not walls of text?
• [ ] **Cross-references:** Links to related skills where relevant?

**Loop score: Tier 1 + Tier 2 = applicable dimensions × 3pts.** Max is 24 when all 8 dimensions apply, 21 when tool-specific guidance is N/A.

Tier 3 is not part of the loop score. It's a yes/no sweep after convergence.

---

The Loop

REPEAT:
  1. PICK the lowest-scoring dimension, respecting tier order:
     - Tier 1 dimensions below 2 → always first
     - Tier 2 dimensions → only after all Tier 1 ≥ 2
     - N/A dimensions → skip entirely, never experiment on them
     - Tier 3 → never (handled in polish pass)
     - User-specified focus areas override this order

  2. MAKE ONE CHANGE to eval/experiments/<name>/SKILL.md
     - Target the specific weakness identified
     - One focused change per iteration, not a rewrite

  3. EVALUATE via A/B comparison (do reasoning internally, log to experiment-log.md):

     a. Generate 3 representative user prompts for this skill
        (realistic things a non-technical founder would say)

     b. For each prompt, reason through what guidance the ORIGINAL
        skill would produce vs what the MODIFIED version would produce

     c. Judge: "Which version gives a non-technical founder better,
        more actionable guidance?" Score: Original wins / Modified wins / Tie

     d.