skill-upgrader

• "Upgrade this skill to v5"
• "Formalize this skill with decision theory"
• "Add MDP structure to this skill"
• "Apply the skill-upgrader to X"

Ragie RAG with indexed books:

• decision-theory partition: LaValle Planning Algorithms, Sutton & Barto RL
• modal-logic partition: Blackburn Modal Logic, Huth & Ryan Logic in CS

Step 1: Setup Session

```bash

SESSION=$(date +%Y%m%d-%H%M%S)-upgrade-{skill_name}

mkdir -p thoughts/skill-builds/${SESSION}

```

Step 2: Initialize Blackboard

Create thoughts/skill-builds/{session}/00-blackboard.md:

```markdown

# Skill Upgrade: {skill_name}

Started: {timestamp}

{path_to_skill}

Decision Theory v5 Hybrid

(Agents append below)

---

```

Step 3: Launch 4 Agents in Parallel

Use Task tool to spawn all 4 agents simultaneously. Each agent:

1. Reads the input skill
2. Queries Ragie for their specific book
3. Appends findings to the blackboard

---

Book: LaValle's "Planning Algorithms" (decision-theory partition)

Focus: States, Actions, Transitions

```

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:

```bash

uv run python scripts/ragie_query.py -q "MDP state space definition" -p decision-theory

uv run python scripts/ragie_query.py -q "action space sequential decisions" -p decision-theory

uv run python scripts/ragie_query.py -q "POMDP partial observability" -p decision-theory

```

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.

"""

)

```

---

Book: Sutton & Barto's "Reinforcement Learning" (decision-theory partition)

Focus: Policy, Termination, Value

Depends on: Agent 1

```

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:

```bash

uv run python scripts/ragie_query.py -q "policy deterministic stochastic" -p decision-theory

uv run python scripts/ragie_query.py -q "episodic termination conditions" -p decision-theory

uv run python scripts/ragie_query.py -q "reward function design" -p decision-theory

```

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.

"""

)

```

---

Book: Blackburn's "Modal Logic" (modal-logic partition)

Focus: Constraints (temporal, epistemic, deontic)

```

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:

```bash

uv run python scripts/ragie_query.py -q "temporal logic LTL operators" -p modal-logic

uv run python scripts/ragie_query.py -q "epistemic logic knowledge" -p modal-logic

uv run python scripts/ragie_query.py -q "deontic logic obligations" -p modal-logic

```

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

"""

)

```

---

Book: Huth & Ryan's "Logic in Computer Science" (modal-logic partition)

Focus: Validation, Safety, Liveness

Depends on: Agents 1-3

```

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:

```bash

uv run python scripts/ragie_query.py -q "safety properties verification" -p modal-logic

uv run python scripts/ragie_query.py -q "liveness properties eventually" -p modal-logic

uv run python scripts/ragie_query.py -q "model checking CTL" -p modal-logic

```

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.

"""

)

```

---

After all agents complete, read the blackboard and create:

Output: thoughts/skill-builds/{session}/SKILL-upgraded.md

Use v5 Hybrid template:

```yaml

---

name: {original_name}

description: {original_description}

version: 5.1-hybrid

---

# Option: {name}

[From original + Agent 1 state analysis]

[From Agent 1 POMDP analysis]

[From Agent 1 actions]

[From Agent 2 state→action rules]

[From Agent 2 episode structure]

[From Agent 2 value guidance]

[From Agent 3 modal logic]

[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"

```

```bash

# 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

```

After upgrade:

```

thoughts/skill-builds/{session}/

├── 00-blackboard.md # Agent collaboration

├── SKILL-upgraded.md # Final v5 Hybrid skill

└── validation-report.md # Agent 4 verification

```