# CCPE System

## 1. What This Project Is

CCPE System is a context protocol engineering workspace for designing, auditing, refactoring, and maintaining AI Prompts, Agents, Skills, Workflows, Runtime protocols, and Cognitive Model assets.

CCPE originally emerged from advanced Prompt Engineering. The new CCPE System extends that foundation into Agentic Engineering.

Its purpose is to help transform AI from a one-off responder into a reusable, testable, composable, collaborative, and maintainable task-execution system.

## 2. Core Definition

**CCPE is a context protocol engineering framework for constructing, auditing, and maintaining AI Prompt, Agent, Skill, and Agentic Workflow systems.**

It systematically defines:

* Objective
* Role
* Context
* Capability
* Tool
* Authority
* Workflow
* Constraint
* State
* Output
* Evaluation
* Runtime environment

The goal is not to make every AI system fully automated. The goal is to make AI systems structurally clear, reusable, inspectable, and aligned with the depth and risk level of the work.

## 3. Primary Design Philosophy

CCPE System is built around one principle:

> Do not treat every AI artifact as a prompt.

Some artifacts are prompts.
Some are agents.
Some are reusable skills.
Some are workflows.
Some are runtime protocols.
Some are cognitive models.
Some are model indexes.
Some are hybrid systems.

The first job of CCPE is classification.
The second job is structural diagnosis.
The third job is creation or refactoring.

## 4. Core Artifact Types

### 4.1 CCPE-Lite

CCPE-Lite is a lightweight Prompt Card for expert-style AI assistants.

Use it when the artifact is mainly:

* A single expert role
* A custom GPT / Gemini / Claude instruction
* A stable persona with a task method
* A human-facing reasoning or review assistant
* Not heavily dependent on external tools, state, or runtime orchestration

Typical examples:

* Red-team critic
* Socratic questioner
* Article reviewer
* Cognitive sparring partner
* Strategic thinking assistant

CCPE-Lite should remain portable, concise, and directly usable in chat-based AI products.

### 4.2 CCPE-Agent

CCPE-Agent is a durable Agent Spec for a reusable working role.

Use it when the artifact:

* Has a stable responsibility
* Needs to be maintained over time
* May participate in a multi-agent workflow
* Has explicit input and output contracts
* May call skills or tools
* Needs handoff rules, authority boundaries, and evaluation criteria

Typical examples:

* Committee member agent
* Project director agent
* Knowledge archivist agent
* Red-team analyst agent used as a workflow node
* Specialist agent in OpenClaw, Claude Code, Codex, or similar systems

CCPE-Agent is not limited to multi-agent systems. A single agent may also need an Agent Spec when it becomes a long-term, composable, or evaluable work unit.

### 4.3 CCPE-Skill

CCPE-Skill is a reusable capability module.

A Skill may be:

* Tool-oriented
* Method-oriented
* Workflow-oriented
* Evaluation-oriented
* Transformation-oriented
* Knowledge-management-oriented

A Skill is not merely a tool wrapper. It may contain a method, procedure, checklist, reasoning protocol, tool usage rule, output template, and validation criteria.

Typical examples:

* Cognitive Imaging Skill
* Assumption stress-test Skill
* Argument-chain inspection Skill
* Voice-to-text preprocessing Skill
* Knowledge archival Skill
* Model extraction Skill
* Report synthesis Skill

A Skill should be reusable across multiple agents.

### 4.4 CCPE-Runtime

CCPE-Runtime is a protocol for running multi-step, multi-role, tool-using, or long-running work.

Runtime does not always mean automation.

There are three major runtime orientations:

1. **Interactive Runtime**

   * Human-led
   * Deep thinking
   * High uncertainty
   * Human decision gates
   * Suitable for modeling, writing, research, and conceptual work

2. **Automation Runtime**

   * Process-led
   * Low uncertainty
   * Clear success criteria
   * Tool execution and file operations
   * Suitable for repetitive, verifiable, low-risk work

3. **Hybrid Runtime**

   * Deep work at the core
   * Automation around the edges
   * Human makes key decisions
   * Agents handle collection, formatting, routing, deduplication, and archival

Typical examples:

* Modeling committee workflow
* Article review committee
* Coding project planning and implementation workflow
* Knowledge extraction pipeline
* Multi-agent synthesis workflow

## 5. Cognitive Model Assets

CCPE System distinguishes between Agents and Models.

An Agent is a role or work unit.
A Model is a reusable cognitive structure.
A Skill may execute a Model.
A Runtime may orchestrate Agents and Skills that use Models.

### 5.1 Model Card

A Model Card is the canonical description of a single cognitive model.

It should capture:

* Model name
* Source material
* Core problem
* Scope
* Assumptions
* Mechanism
* Procedure
* Failure modes
* Falsification boundary
* Related agents
* Related skills
* Version status

Examples:

* Cognitive Imaging Model
* Giant Cognition Model
* Cognitive Prism Model
* Argument Compression Model
* Concept Boundary Model

A Model Card should preserve the structure of the model without forcing it into an agent persona.

### 5.2 Model Index

A Model Index organizes many Model Cards.

It should capture:

* Model taxonomy
* Model hierarchy
* Dependency relationships
* Overlap and conflict relationships
* Usage scenarios
* Related agents
* Related skills
* Version status
* Extraction source

The Model Index is necessary when a knowledge system contains dozens or hundreds of models.

## 6. CCPE Forge Skill

This project includes a Codex Skill called `ccpe-forge`.

The Forge Skill supports four modes:

### 6.1 Creator Mode

Use Creator Mode to create new artifacts:

* Prompt Cards
* Agent Specs
* Skills
* Runtime Specs
* Model Cards
* Model Index entries

Creator Mode must first clarify or infer the intended artifact type, usage mode, automation level, human decision points, and target platform.

### 6.2 Auditor Mode

Use Auditor Mode to inspect existing artifacts.

It should diagnose:

* Whether the artifact is Lite, Agent, Skill, Runtime, Model Card, or Hybrid
* Whether it has embedded cognitive models
* Whether it should be split into Agent, Skill, Model Card, or Runtime
* Whether it has unclear goals, boundaries, authority, state, output, or evaluation rules
* Whether it is over-engineered or under-specified

### 6.3 Refactor Mode

Use Refactor Mode to upgrade or restructure existing artifacts.

It should produce:

* Refactor plan
* Upgrade report
* Revised Prompt Card
* Agent Spec
* Skill Spec
* Runtime Spec
* Model Card
* Model Index entry

Refactor Mode must preserve the original intellectual flavor and core model unless explicitly instructed otherwise.

### 6.4 Model Mining Mode

Use Model Mining Mode to extract cognitive models from long-form writing, essays, notes, research drafts, or discussions.

It should identify:

* Explicit models
* Implicit models
* Foundational assumptions
* Mechanisms
* Procedures
* Scope
* Failure modes
* Falsification boundaries
* Possible Skill or Agent conversions

Model Mining should behave like lossless compression. It should remove rhetorical bulk, academic completeness overhead, and supporting digressions, while preserving the generative structure of the model.

## 7. Recommended Project Directory

```text
ccpe-system/
├── AGENTS.md
├── README.md
│
├── ccpe-protocol/
│   ├── ccpe-system-definition.md
│   ├── ccpe-classification-rules.md
│   ├── ccpe-operating-modes.md
│   ├── ccpe-layer-spec.md
│   ├── ccpe-quality-rubric.md
│   └── ccpe-migration-policy.md
│
├── .codex/
│   └── skills/
│       └── ccpe-forge/
│           ├── SKILL.md
│           ├── references/
│           └── templates/
│
├── workbench/
│   ├── raw/
│   ├── analysis/
│   ├── upgraded/
│   └── archive/
│
├── agents/
│   ├── lite/
│   ├── agent-specs/
│   └── committees/
│
├── skills/
│   ├── cognitive/
│   ├── tool/
│   ├── workflow/
│   └── evaluation/
│
├── runtimes/
│   ├── interactive/
│   ├── automation/
│   └── hybrid/
│
├── model-cards/
│   ├── foundational/
│   ├── intermediate/
│   ├── applied/
│   ├── workflow-models/
│   └── implicit-extracted/
│
└── model-index/
    ├── model-index.md
    ├── model-taxonomy.md
    ├── model-dependency-map.md
    ├── model-usage-map.md
    └── extraction-log.md
```

## 8. Recommended Workflow

### 8.1 Creating a New Agent

1. Place the creation request in `workbench/raw/`, reference the relevant source in `knowledge-vault/prompts/`, or describe it directly to Codex.
2. Ask Codex to use `ccpe-forge` in Creator Mode.
3. Generate a Creation Brief.
4. Confirm target form:

   * Lite
   * Agent
   * Skill
   * Runtime
   * Model Card
   * Hybrid
5. Generate the target file.
6. Place the final artifact in the correct directory.

### 8.2 Upgrading an Existing Agent

1. Identify the old agent source. Current prompt assets usually live in `C:\Users\wangq\Documents\Codex\knowledge-vault\prompts\`; use `workbench/raw/` only for temporary or unclassified inputs.
2. Ask Codex to use `ccpe-forge` in Auditor Mode.
3. Review the Upgrade Report.
4. If accepted, run Refactor Mode.
5. Store:

   * Diagnosis in `workbench/analysis/`
   * Upgraded artifact in `workbench/upgraded/`
   * Final reusable artifact in `agents/`, `skills/`, `runtimes/`, or `model-cards/`

### 8.3 Extracting Models from Articles

1. Put the article in `workbench/raw/`, or reference its canonical source path if it already lives in `knowledge-vault`.
2. Ask Codex to use `ccpe-forge` in Model Mining Mode.
3. Extract candidate models.
4. Generate Model Cards.
5. Register them in Model Index.
6. Optionally convert strong models into Skills or Agents.

### 8.4 Building a Multi-Agent Workflow

1. Define the Runtime first.
2. Define each Agent Spec.
3. Define shared Skills.
4. Define Model Cards used by those Agents or Skills.
5. Define human decision gates.
6. Define output, evaluation, and archival rules.

## 9. Key Design Rules

### 9.1 Classify Before Creating

Never create or refactor before classifying the artifact type.

### 9.2 Do Not Over-Engineer

Not every expert prompt needs Agent, Skill, and Runtime layers.

Use the lightest structure that preserves function, clarity, and future maintainability.

### 9.2.1 Scenario Probe Before Layering

Before creating or upgrading an artifact, identify how it will actually be used:

```text
Web / GPT / Gemini / Claude single-agent prompt
Codex-callable Skill
Durable workflow role
Manual multi-agent committee member
Automated or semi-automated Runtime node
```

Scenario determines the required layers.

For mature single-agent expert prompts, the default repair path is:

```text
CCPE-Lite
+ Model Card if the embedded model is stable and important
```

Add Skill only when the method must be invoked by Codex or reused across agents.
Add Agent Spec only when the role needs collaboration, handoff, authority, or evaluation contracts.
Add Runtime only when stages, state, routing, synthesis, archival, tools, or automation are involved.

Lite is not a downgraded Agent Spec. In Web-style expert use, Lite is the production artifact.

### 9.3 Do Not Under-Specify High-Risk Systems

If the system involves tools, file operations, code changes, long-running tasks, multi-agent handoff, external APIs, or automation, it must include authority, state, evaluation, and runtime rules.

### 9.4 Separate Role from Model

A cognitive model should not be permanently trapped inside one agent if it can be reused.

Preferred separation:

```text
Agent = role, responsibility, interaction, authority
Model Card = cognitive model definition
Skill = executable procedure using the model
Runtime = workflow orchestration
```

### 9.5 Preserve Intellectual Flavor

When refactoring an existing agent, preserve:

* Core metaphor
* Cognitive stance
* Domain worldview
* Distinctive reasoning style
* Original purpose
* User's intellectual intent

Structural cleanup must not flatten the agent into generic corporate sludge.

### 9.6 Human-in-the-Loop Is First-Class

Human involvement is not a failure of automation.

For deep thinking, model building, theoretical writing, conceptual design, and high-uncertainty evaluation, human judgment must remain central.

CCPE should explicitly mark human decision gates instead of hiding them.

## 10. Current Project Goal

The immediate goal of this workspace is to construct the CCPE System itself, based on Codex, as a reusable Skill-driven workbench.

The first build target is:

```text
CCPE Forge Skill
= Creator + Auditor + Refactor + Model Mining
```

The Forge Skill will then be used to:

1. Inspect and repair CCPE itself.
2. Upgrade the previous CCPE intelligent agent.
3. Upgrade existing user-created agents.
4. Extract Model Cards from long-form writing.
5. Build a usable Model Index.
6. Support future creation of Agents, Skills, and Runtimes.

## 11. File Naming Conventions

Use lowercase kebab-case for filenames.

Recommended examples:

```text
cognitive-imaging-model.md
cognitive-imaging.skill.md
cognitive-imaging-specialist.agent.md
modeling-committee.runtime.md
zhangliao-red-team.prompt.md
```

## 12. Versioning

Each durable artifact should include:

```text
author:
version:
created:
updated:
status:
based_on:
related_models:
related_skills:
related_agents:
```

Recommended status values:

```text
candidate
draft
active
rejected
merged
deprecated
archived
```

## 13. Language Policy

CCPE System uses a bilingual language strategy.

Protocol language may be English when portability across Codex, Claude Code, OpenClaw, GPT, Gemini, and other systems is useful.

For user-authored cognitive models, the canonical model language should normally be Simplified Chinese. English aliases are allowed as secondary labels for navigation, interoperability, and file references.

Final Agent output should use Simplified Chinese by default unless the user explicitly requests another language.

When CCPE System communicates directly with the user, it should also use Simplified Chinese by default unless otherwise requested.

File names may use English kebab-case for portability.

Bilingual naming is encouraged for important models.

Example:

```text
认知显影术 / Cognitive Imaging
巨人认知 / Giant Cognition
认知棱镜 / Cognitive Prism
```

## 14. First Build Sequence

Recommended construction order:

```text
Batch 0:
README.md
AGENTS.md

Batch 1:
ccpe-system-definition.md
ccpe-classification-rules.md
ccpe-operating-modes.md

Batch 2:
ccpe-layer-spec.md
ccpe-quality-rubric.md
ccpe-migration-policy.md

Batch 3:
.codex/skills/ccpe-forge/SKILL.md

Batch 4:
Forge workflow references

Batch 5:
Model Card and Model Index rules

Batch 6:
Core templates

Batch 7:
Model and upgrade templates

Batch 8:
Model Index initial files

Batch 9:
Directory README files
```