skill-upgrader

Name: skill-upgrader
Author: parcadei

Other Community

Repo stars 3,783
Author updated Live
Author repo Continuous-Claude-v3

Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network

Domain: Other
Compatible agents: Claude Code
Cursor
Cline
Codex
Windsurf
Gemini CLI

+20
Trust score: 88 / 100 · community maintained
Author / version / license: @parcadei · no license declared
Token usage: Lean
Setup complexity: Guided setup
External API key: Not required
Operating systems: Unspecified (assume cross-platform)
Runtime requirements: Python
Permissions: Read-only
Write / modify
Shell exec
Network behavior: Local-only
Install commands: 26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview skill-upgrader.preview

---
name: skill-upgrader
description: Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrad…
category: other
runtime: Python
---

# skill-upgrader output preview

## PART A: Task fit
- Use case: Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrades any SKILL.md to Decision Theory v5 Hybrid format using 4 parallel Ragie-backed agents. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use / Prerequisites / Workflow” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrades any SKILL.md to Decision Theory v5 Hybrid format using 4 parallel Ragie-backed agents. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “When to Use / Prerequisites / Workflow” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.

SKILL.md SKILL.md

---
name: skill-upgrader
description: Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrad…
category: other
source: parcadei/Continuous-Claude-v3
---

# skill-upgrader

## When to use
- Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrades any SKILL.md to Dec…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.

## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.

## Execution rules
- Organize steps around “When to Use / Prerequisites / Workflow” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.

## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.

Evidence boundary and execution chain

The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.

skill "skill-upgrader" {
  input      -> user goal + target files + boundaries + acceptance criteria
  context    -> When to Use / Prerequisites / Workflow
  rules      -> SKILL.md triggers / order / output contract
  runtime    -> Python | read files, write/modify files, run shell commands | mostly runs locally
  guardrails -> usually needs no extra API key + small-sample validation + diff/log review
  output     -> copyable result + checklist + next iteration
}

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Upgrade any skill to v5 Hybrid format using decision theory + modal logic Meta-skill that upgrades any SKILL.md to Decision Theo…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “When to Use”, “Prerequisites”, “Workflow”, “Step 1: Setup Session”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name skill-upgrader directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “When to Use / Prerequisites / Workflow” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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

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:

# 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:

Reads the input skill
Queries Ragie for their specific book
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:

What are the STATES? (phases, modes, tracked info)
What are the ACTIONS? (what can agent do in each state)
How do TRANSITIONS work? (deterministic or stochastic)
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:

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:

What's the POLICY? (state → action rules)
When does it END? (terminal states, success/failure)
What are REWARDS? (goals +, costs -)
Which states are HIGH/LOW value?

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

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


---

## 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:

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:

TEMPORAL: "must do X before Y" → □, ◇, U
EPISTEMIC: "must know X" → K operator
DEONTIC: "must/forbidden/may" → O, F, P
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:

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:

SAFETY: What bad things never happen? □¬(bad)
LIVENESS: What good things eventually happen? ◇(good)
CONSISTENCY: Any contradictions between agents?
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

# 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

Install command

One-click installPick a runner, click Copy, paste into your terminal, press enter

npx skills add parcadei/Continuous-Claude-v3/.claude/skills/skill-upgrader -y

Open general install guide

View skill source

Works with

Claude Code
Cursor
Cline
Codex
Windsurf
Gemini
OpenCode
Roo Code
AAide
+Augment
GitHub Copilot
OpenClaw
KKiro
Junie
WWarp
Goose
Trae
Kilo Code
OpenHands
Amp
Zencoder
CContinue
AIAider
CodeBuddy
CodeGeeX
Replit Agent

Security audit

88 / 100

Community

未声明 license/许可。
未声明 version。
未限定 allowed-tools，默认拥有全部工具权限。

Metadata

Source: github
parcadei/Continuous-Claude-v3
中文

More by @parcadei

Category & tags

Category Other

Decide Fit First

Design Intent

How To Use It

Boundaries And Review

Skill Upgrader

When to Use

Prerequisites

Workflow

Step 1: Setup Session

Step 2: Initialize Blackboard

Step 3: Launch 4 Agents in Parallel

Agent 1: LaValle Planner

Example Usage

Ragie Query Reference

Files Created

Discussion