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Agent skill

Skill Upgrader

by parcadei in parcadei/Continuous-Claude-v3

Upgrade any skill to v5 Hybrid format using decision theory + modal logic

MITAuto-check: notesAgent Workflows

Install Skill Upgrader

skills CLI
$ npx skills add parcadei/Continuous-Claude-v3 --skill skill-upgrader -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 skill-upgrader --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/skill-upgrader .claude/skills/skill-upgrader && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-upgrader
GitHub stars
3.9k
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
446 words
Files
1
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Upgrade any skill to v5 Hybrid format using decision theory + modal logic

  • Works in 3 steps: Setup Session → Initialize Blackboard → Launch 4 Agents in Parallel
  • Agent Workflows work in your project
  • SKILL.md covers When to Use, Prerequisites, Workflow and Agent 1: LaValle Planner, plus 3 more sections
  • Calls uv

What it does

Skill Upgrader is an agent skill from parcadei/Continuous-Claude-v3. Upgrade any skill to v5 Hybrid format using decision theory + modal logic

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. It works with Python. The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/skill-upgrader”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read, Write, Edit, Task, Glob, Grep

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Setup Session
  2. Initialize Blackboard
  3. Launch 4 Agents in Parallel

What it can do on your machine

Read from SKILL.md and the folder at commit d07ff4b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Edit
    • Task
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skill Upgrader loads about 1.9k tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 446 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Edit, Task, Glob, Grep

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 446 words, ~1,867 tokens.

Download SKILL.mdSave it as .claude/skills/skill-upgrader/SKILL.md (or your agent's skills folder).
name
skill-upgrader
description
Upgrade any skill to v5 Hybrid format using decision theory + modal logic
allowed-tools
Bash, Read, Write, Edit, Task, Glob, Grep

Skill Upgrader

Meta-skill that upgrades any SKILL.md to Decision Theory v5 Hybrid format using 4 parallel Ragie-backed agents.

When to Use

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

Prerequisites

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

Workflow

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}

## Input Skill
{path_to_skill}

## Target Format
Decision Theory v5 Hybrid

## Agent Findings
(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

Agent 1: LaValle Planner

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. """ )


---

## Agent 2: Sutton & Barto Optimizer

**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. """ )

Show full SKILL.md (174 more words)Show less

---

## Agent 3: Blackburn Modal Logician

**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 """ )

---

## Agent 4: Huth & Ryan Verifier

**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. """ )


---

## Step 4: Synthesize Final Skill

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}

## 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]

Example Usage

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"

Ragie Query Reference

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

Files Created

After upgrade:

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

© parcadei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/skill-upgrader of parcadei/Continuous-Claude-v3.

Open the folder on GitHubat commit d07ff4b

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.

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MemPalace Setup and OperationMemPalace/mempalace59k—~2.2kAutomated safety check: PassMIT
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Works with

Categories

Questions about Skill Upgrader

What does Skill Upgrader do?

Upgrade any skill to v5 Hybrid format using decision theory + modal logic. Skill Upgrader is an agent skill from parcadei/Continuous-Claude-v3.

When should I use Skill Upgrader?

Skill Upgrader fits situations like: agent Workflows work in your project.

How do I install Skill Upgrader in Claude Code?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill skill-upgrader -a claude-code`. Or copy the skill folder (.claude/skills/skill-upgrader in parcadei/Continuous-Claude-v3) into .claude/skills/skill-upgrader in your project. Claude Code loads it when a task matches its description.

How do I install Skill Upgrader in Codex?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill skill-upgrader -a codex`. Or copy the skill folder (.claude/skills/skill-upgrader in parcadei/Continuous-Claude-v3) into .agents/skills/skill-upgrader in your project. Codex loads it when a task matches its description.

Can I use Skill Upgrader in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parcadei/Continuous-Claude-v3 --skill skill-upgrader -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-upgrader, .gemini/skills/skill-upgrader, .github/skills/skill-upgrader and .opencode/skills/skill-upgrader in your project.

What does Skill Upgrader need to run?

Going by SKILL.md and its folder, Skill Upgrader needs the command-line tools its instructions call (uv). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read, Write, Edit, Task, Glob, Grep.

Does Skill Upgrader access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Skill Upgrader safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Skill Upgrader use?

Skill Upgrader is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Upgrader use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Skill Upgrader?

Skills that share tags, products or a category with Skill Upgrader: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), Mem0 CLI Memory Commands (mem0ai/mem0, 67k stars) and MemPalace Setup and Operation (MemPalace/mempalace, 59k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Upgrader?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,943 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.

Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.