p10diverse-pick-10-diverse

name	p10diverse - Pick 10 Diverse
description	Pick 10 skills maximizing coverage across different categories, tiers, and functions. Produces the broadest possible sample of the skill library. Standalone implementation of the DIVERSE algorithm from /pick.
tier	tier4
categories	["Meta & Navigation"]
tags	["shortcut","pick","discovery","diverse","coverage"]
output	{"format":"prose"}

Pick 10 Diverse

Input: $ARGUMENTS

---

Core Principles

1. Diversity is coverage, not randomness. Random selection sometimes produces diverse results, sometimes clusters. Diverse selection guarantees coverage by construction — it is an anti-clustering algorithm.

2. Five dimensions of diversity. Skills vary by category, tier, function (analyze/decide/plan/create/validate), workflow phase (early/mid/late), and connectivity (standalone vs hub). True diversity covers all five dimensions, not just category.

3. Round-robin prevents dominance. If you pick the "best" from each category, large categories dominate because they have higher-quality peaks. Round-robin across categories, then within categories by tier, prevents this.

4. Mandatory function coverage. The 10 picks must include at least one skill from each of these functions: analysis, decision, planning, validation, and exploration. Missing any function creates a blind spot in the user's toolkit.

5. Surprise has learning value. A diverse set should include at least one skill the user probably hasn't encountered. If all 10 are well-known, the diversity is only surface-level.

---

Phase 1: Dimension Mapping

[A] CATEGORY_MAP:

Step 1: List all categories in skills.json with skill counts
    | Category | Skill Count | Representative Skills |
    |----------|-------------|----------------------|
    | [cat]    | [N]         | [top 3 by tier]      |

Step 2: Identify underrepresented categories (fewer than 5 skills)
Step 3: Note: underrepresented categories get priority — their skills are rarer

[B] FUNCTION_MAP:
    ANALYSIS: [skill IDs whose primary purpose is understanding/decomposing]
    DECISION: [skill IDs whose primary purpose is choosing/comparing]
    PLANNING: [skill IDs whose primary purpose is sequencing/scoping]
    CREATION: [skill IDs whose primary purpose is producing/writing]
    VALIDATION: [skill IDs whose primary purpose is checking/testing]
    EXPLORATION: [skill IDs whose primary purpose is discovering/generating]
    DIAGNOSIS: [skill IDs whose primary purpose is finding problems/causes]

[C] TIER_MAP:
    tier1 (core): [count]
    tier2 (important): [count]
    tier3 (domain): [count]
    tier4 (specialized): [count]
    category (router): [count]
    experimental: [count]

---

Phase 2: Round-Robin Selection

[D] SELECTION:

Step 1: Order categories by size (smallest first — rare categories get first pick)
Step 2: Round-robin: take the highest-tier skill from each category
    - Skip categories already represented
    - Continue until 10 slots filled or all categories visited
Step 3: If fewer than 10 categories: take second-best from largest categories
Step 4: If more than 10 categories: prioritize categories covering unfilled functions

[E] FUNCTION_COVERAGE_CHECK:
    After selection, verify:
    - Analysis represented? [Y/N — if N, swap weakest pick for best analysis skill]
    - Decision represented? [Y/N — if N, swap]
    - Planning represented? [Y/N — if N, swap]
    - Validation represented? [Y/N — if N, swap]
    - Exploration represented? [Y/N — if N, swap]

---

Phase 3: Diversity Verification

[F] DIVERSITY_METRICS:

    Categories covered: [N] out of [total]
    Tiers covered: [list]
    Functions covered: [N] out of 7
    Phases covered: [early/mid/late — which are present]
    Connectivity mix: [N standalone] + [M hub skills]

    SURPRISE_CHECK:
    - How many of these 10 are well-known (tier1/tier2 with high connectivity)? [N]
    - If N > 7: swap 1-2 well-known for lesser-known skills from underrepresented areas

[G] DIVERSITY_SCORE:
    = (categories_covered / total_categories × 25)
    + (functions_covered / 7 × 25)
    + (tiers_covered / 6 × 25)
    + (surprise_picks / 10 × 25)
    = [0-100]
    Target: >= 70

---

Phase 4: Output

ALGORITHM: DIVERSE
POOL: [total skills in library]
PICKED: 10
DIVERSITY SCORE: [X/100]

  1. /[id] — [title]
     Category: [cat] | Tier: [tier] | Function: [func]
     [1-line description]

  2. /[id] — [title]
     Category: [cat] | Tier: [tier] | Function: [func]
     [1-line description]

  [continue to 10...]

COVERAGE:
  Categories: [N/total] — [list covered]
  Functions: [N/7] — [list covered]
  Tiers: [list covered]
  Phases: [list covered]

GAPS:
  Categories NOT covered: [list]
  Functions NOT covered: [list — if any]
  To fill gaps, add: /[suggested skill for each gap]

SUGGESTED EXPLORATION ORDER:
  Start with: /[most accessible skill]
  Then try: /[skill that builds on first]
  Deep dive: /[most specialized pick]

---

Failure Modes

Failure	Signal	Fix
Category clustering	4+ picks from same category	Round-robin prevents this — verify the algorithm ran
Tier bias	All picks are tier1/tier2	Include at least 1 tier3 and 1 tier4 pick
Function gap	No validation or no planning skill	Mandatory coverage check — swap to fill
Popularity bias	All 10 are skills every user already knows	Surprise check — include 2+ lesser-known picks
Random masquerading as diverse	No systematic coverage, just scattered picks	Verify diversity score >= 70
Ignoring user context	Diverse set doesn't account for $ARGUMENTS	If $ARGUMENTS provided, weight toward relevant categories

---

Depth Scaling

Depth	Mapping	Selection	Verification
1x	Category counts only	Round-robin by category	Function coverage check
2x	Full 5-dimension mapping	Round-robin + function coverage swaps	Diversity score calculation
4x	Dimension mapping + connectivity analysis	Optimized selection maximizing diversity score	Full verification + gap analysis
8x	Complete library analysis	Simulated annealing on diversity score	Cross-validation with alternative diverse sets

Default: 2x. These are floors.

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Pre-Completion Checklist

• All categories in library identified and counted
• Round-robin selection performed (smallest categories first)
• All 5 mandatory functions represented (analysis, decision, planning, validation, exploration)
• Exactly 10 skills returned
• At least 2 lesser-known or surprise picks included
• Diversity score calculated and >= 70
• Gaps explicitly identified with suggested additions
• Exploration order suggested

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Integration

• Shortcut for: /pick 10 diverse
• Use when: You want a broad sample of the skill library, not a targeted search
• Routes to: The 10 picked skills; /p10useful for quality-ranked alternative
• Related: /p10random (random, not guaranteed diverse), /p10useful (quality-ranked)
• Differs from /p10random: random may cluster; diverse guarantees coverage
• Differs from /p10useful: useful ranks by quality; diverse ranks by coverage
• Differs from /p10complement: complement centers on one skill; diverse covers the whole library