Skill Upgrader

SESSION=$(date +%Y%m%d-%H%M%S)-upgrade-{skill_name}
mkdir -p thoughts/skill-builds/${SESSION}

# Skill Upgrade: {skill_name}
Started: {timestamp}

## Input Skill
{path_to_skill}

## Target Format
Decision Theory v5 Hybrid

## Agent Findings
(Agents append below)

---

Task(
  subagent_type="general-purpose",
  prompt="""
INPUT SKILL: {path}
BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: LaValle's "Planning Algorithms" in Ragie partition 'decision-theory'

TASK: Identify MDP structure in the skill.

Query Ragie:

Read the input skill and answer:
1. What are the STATES? (phases, modes, tracked info)
2. What are the ACTIONS? (what can agent do in each state)
3. How do TRANSITIONS work? (deterministic or stochastic)
4. Is this POMDP or fully observable?

WRITE to blackboard section: ## Agent 1: States, Actions & Transitions

Format as plain English with LaValle chapter citations.
"""
)

Task(
  subagent_type="general-purpose",
  prompt="""
INPUT SKILL: {path}
BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: Sutton & Barto's "Reinforcement Learning" in Ragie partition 'decision-theory'

WAIT: Read Agent 1's findings from blackboard first.

TASK: Design policy and termination conditions.

Query Ragie:

Using Agent 1's states and actions, answer:
1. What's the POLICY? (state → action rules)
2. When does it END? (terminal states, success/failure)
3. What are REWARDS? (goals +, costs -)
4. Which states are HIGH/LOW value?

WRITE to blackboard section: ## Agent 2: Policy & Values

Format as plain English with Sutton & Barto section citations.
"""
)

Task(
  subagent_type="general-purpose",
  prompt="""
INPUT SKILL: {path}
BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: Blackburn's "Modal Logic" in Ragie partition 'modal-logic'

TASK: Extract constraints from the skill.

Query Ragie:

Read the input skill and identify:
1. TEMPORAL: "must do X before Y" → □, ◇, U
2. EPISTEMIC: "must know X" → K operator
3. DEONTIC: "must/forbidden/may" → O, F, P
4. DYNAMIC: "action causes effect" → [action]

WRITE to blackboard section: ## Agent 3: Constraints

For each constraint:
- Plain English description
- Modal logic notation
- Why it matters
- Blackburn chapter citation
"""
)

Task(
  subagent_type="general-purpose",
  prompt="""
INPUT SKILL: {path}
BLACKBOARD: thoughts/skill-builds/{session}/00-blackboard.md

YOUR BOOK: Huth & Ryan's "Logic in Computer Science" in Ragie partition 'modal-logic'

WAIT: Read Agents 1-3 findings from blackboard first.

TASK: Verify consistency and completeness.

Query Ragie:

Check:
1. SAFETY: What bad things never happen? □¬(bad)
2. LIVENESS: What good things eventually happen? ◇(good)
3. CONSISTENCY: Any contradictions between agents?
4. COMPLETENESS: Any gaps in coverage?

WRITE to blackboard section: ## Agent 4: Verification

Report with ✓/✗ for each property.
Overall verdict: PASS or NEEDS_WORK
Huth & Ryan section citations.
"""
)

---
name: {original_name}
description: {original_description}
version: 5.1-hybrid
---

# Option: {name}

## Initiation (I)
[From original + Agent 1 state analysis]

## Observation Space (Y)
[From Agent 1 POMDP analysis]

## Action Space (U)
[From Agent 1 actions]

## Policy (pi)
[From Agent 2 state→action rules]

## Termination (beta)
[From Agent 2 episode structure]

## Q-Heuristics
[From Agent 2 value guidance]

## Constraints
[From Agent 3 modal logic]

## Verification
[From Agent 4 safety/liveness]

User: "Upgrade .claude/skills/implement_plan/SKILL.md to v5 Hybrid"

Claude:
1. Creates session directory
2. Initializes blackboard
3. Launches 4 agents in parallel (Task tool)
4. Waits for completion
5. Reads blackboard
6. Synthesizes upgraded skill
7. Reports: "Upgraded skill at thoughts/skill-builds/.../SKILL-upgraded.md"

# Decision theory partition
uv run python scripts/ragie_query.py -q "your question" -p decision-theory

# Modal logic partition
uv run python scripts/ragie_query.py -q "your question" -p modal-logic

# With reranking for better results
uv run python scripts/ragie_query.py -q "your question" -p decision-theory --rerank

thoughts/skill-builds/{session}/
├── 00-blackboard.md      # Agent collaboration
├── SKILL-upgraded.md     # Final v5 Hybrid skill
└── validation-report.md  # Agent 4 verification