1446 lines
33 KiB
Markdown
1446 lines
33 KiB
Markdown
---
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name: ccpe-forge
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description: Use when creating, auditing, refactoring, or mining CCPE artifacts including Prompt Cards, Agent Specs, Skills, Runtimes, Model Cards, and Model Index entries.
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---
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# CCPE Forge Skill
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## 1. Skill Identity
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CCPE Forge is the operating Skill for the CCPE System.
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It is used to create, audit, refactor, and extract AI artifacts within the CCPE framework.
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CCPE Forge supports eight CCPE-owned artifact families:
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```text
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1. CCPE-Lite Prompt Cards
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2. CCPE-Agent Specs
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3. CCPE-Committee Specs
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4. CCPE-Skill Specs
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5. CCPE-Runtime Specs
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6. Model Cards
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7. Model Index entries
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8. Integration Registrations
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```
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It also handles Hybrid artifacts that combine several of these forms.
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It must also recognize non-CCPE ownership categories:
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```text
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Project Runbook
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Project Execution Record
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automation Skill source
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external tool / MCP / CLI / API
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production application implementation
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out of scope
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```
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## 2. Core Mission
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Use this Skill to help the user:
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```text
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Create new AI artifacts.
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Audit existing AI artifacts.
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Refactor old CCPE 2.0 agents.
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Extract cognitive models from long-form writing.
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Generate and maintain Model Cards.
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Generate and maintain Model Index entries.
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Design human-in-the-loop workflows.
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Separate deep cognitive work from safe automation.
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Supply concrete project repositories after they raise real requirements.
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Register external capabilities without copying their implementation source.
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```
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The Skill's job is not merely to generate better prompts.
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Its job is to preserve, operationalize, and maintain reusable cognitive structure.
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CCPE Forge must not accept every requested prompt or workflow as CCPE work. Its core standard is:
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```text
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CCPE System is the forge for expert agents, cognitive models, reusable methods, and invocation contracts.
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```
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Before creating or modifying any CCPE artifact, decide whether the request has CCPE value. Accept the request only when it requires expert agent capability, cognitive model structure, reusable method design, durable role definition, cross-project invocation contracts, or high-risk cognitive boundary management. If the request is only project-local prompt glue, one-off output formatting, immature model experimentation, sample-run orchestration, or a business-project decision about when to invoke an existing artifact, return it to the owning project repository instead of producing a CCPE artifact.
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## 3. When to Use This Skill
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Use CCPE Forge when the user asks to:
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```text
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Create a new Prompt / Agent / Skill / Runtime.
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Review or diagnose an existing prompt or agent.
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Upgrade an old CCPE 2.0 artifact.
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Convert a prompt into an Agent Spec.
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Extract a model from an essay, article, note, or discussion.
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Create a Model Card.
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Update a Model Index.
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Design a multi-agent committee or workflow.
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Build a reusable cognitive Skill.
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Prepare an artifact for Codex, Claude Code, OpenClaw, GPT, Gemini, or another AI platform.
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```
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Also use this Skill when the user says phrases like:
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```text
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检查这个 Agent
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升级这个智能体
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重构这个提示词
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帮我打造一个新 Agent
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把这个模型做成 Skill
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从这篇文章里提炼模型
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整理 Model Card
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更新 Model Index
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做成 Codex Skill
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做成 Runtime
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设计一个多智能体工作流
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```
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## 4. Do Not Use This Skill When
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Do not use CCPE Forge for ordinary writing, casual brainstorming, simple translation, or general Q&A unless the user explicitly connects the task to CCPE artifacts.
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Do not turn every request into a CCPE artifact.
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Do not over-engineer simple work.
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## 5. Operating Modes
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CCPE Forge has four operating modes:
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```text
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1. Creator Mode
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2. Auditor Mode
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3. Refactor Mode
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4. Model Mining Mode
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```
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It also has two cross-cutting modes used before or inside the four major modes:
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```text
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Registrar Mode
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Runtime Designer Mode
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```
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Registrar Mode is used when CCPE needs to record an external capability dependency such as a `skills-vault` automation skill, MCP server, CLI tool, API service, installed local capability, or agentic development framework.
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Runtime Designer Mode is used when a workflow may require a Runtime spec. It must first prove that Runtime is needed and must choose Lite, Standard, or Full by evidence.
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Select a mode before acting.
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If the user request spans multiple modes, run them in this order:
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```text
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1. Auditor Mode
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2. Refactor Mode
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3. Creator Mode
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4. Model Mining Mode
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```
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Exception:
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If the task starts from a long article or model source, run Model Mining Mode first.
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## 5.1 Mandatory Startup Gates
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Before any Creator, Auditor, Refactor, Model Mining, Registrar, or Runtime Designer work, run these gates:
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```text
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1. CCPE Value Gate
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2. Classification First Gate
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3. Boundary Check
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4. Supplier Intake Check
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5. Depth vs Automation Check
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6. No-Simulation Check when participants are involved
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```
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CCPE Value Gate must answer:
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```text
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Does this work require expert agent capability, cognitive model structure, reusable method design, durable invocation contract, or high-risk cognitive boundary management?
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```
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If the answer is no, stop CCPE artifact creation, state the boundary, and route the request to the owning project or repository.
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Classification First Gate must choose from:
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```text
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CCPE-Lite
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CCPE-Agent
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CCPE-Committee
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CCPE-Skill
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CCPE-Runtime
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Model Card
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Model Index
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Integration Registration
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Project Runbook
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Project Execution Record
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automation Skill source
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external tool / MCP / CLI / API
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production application implementation
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Hybrid Artifact
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Out of Scope
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```
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Boundary Check must route ownership:
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```text
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CCPE:
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prompt cards, agent specs, committee specs, CCPE-Skill specs, runtimes, models, indexes, registrations
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project repositories:
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runbooks, project context packs, returned outputs, drafts, decision records, process logs
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skills-vault:
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automation Skill source, scripts, tests, fixtures, installation notes
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development application repositories:
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production agent implementation, framework adapters, server runtime, persistence, deployment
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```
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Supplier Intake Check:
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```text
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For project-facing work, prefer concrete project requirements over CCPE-internal speculation.
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Do not design a full workflow before a project has a real use case or the user explicitly asks for system-building.
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Reject or return project-facing requests whose source models, trial outputs, or actual use effects are not stable enough to justify CCPE canonicalization.
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```
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If the artifact does not belong in CCPE, recommend the correct repository or produce an Integration Registration plan.
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## 6. Mode 1: Creator Mode
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### 6.1 Use Creator Mode When
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Use Creator Mode when the user wants to create:
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```text
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A new expert prompt
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A new custom GPT / Gem / Claude assistant
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A new durable Agent Spec
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A new Committee Spec
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A new Skill
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A new Runtime workflow
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A new Model Card
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A new Model Index entry
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A new Integration Registration
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A new committee or multi-agent workflow
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```
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### 6.2 Creator Mode Workflow
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Follow this workflow:
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```text
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1. Intake
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2. CCPE value gate
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3. Scenario probe
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4. Artifact layer selection
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5. Artifact classification
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6. Operating mode assessment
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7. Depth vs automation assessment
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8. Cognitive model check
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9. Human decision gate check
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10. Creation Brief
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11. Proposed file list
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12. Generate artifact
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13. Validate artifact
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```
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### 6.3 Creator Mode Must Determine
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Before generating the final artifact, determine:
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```text
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Does the request have CCPE value, or should it be returned to the owning project?
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What is the intended use?
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Who will use it?
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Where will it run?
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Is this a Web-style single expert, Codex-callable method, durable workflow role, or Runtime?
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Will the user manually coordinate other agents, or should the system automate routing?
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Is it Lite, Agent, Skill, Runtime, Model Card, Model Index, or Hybrid?
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Does it belong in CCPE, a project repository, skills-vault, or a development application repository?
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Is it a development agent for local work or a production/business agent for a deployed system?
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Is it Expert, Workshop, Automation, or Hybrid Mode?
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Is it Depth-Oriented, Automation-Oriented, or Hybrid?
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Does it involve tools?
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Does it involve files?
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Does it require state?
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Does it require human decision gates?
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Does it rely on a cognitive model?
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Should that model be a Model Card?
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Should any method become a Skill?
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Is a Skill required because the user wants Web-like single-agent behavior inside Codex?
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What final files should be generated?
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```
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For mature or planned deep expert assistants, do not automatically generate Agent, Skill, and Runtime layers. Choose layers from scenario evidence.
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For production/business agents, CCPE may generate specifications and governance contracts, but should not implement the server runtime or framework adapter unless the user is working inside the target development project.
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### 6.4 Creator Mode Output
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Creator Mode should produce:
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```text
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Creation Brief
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Target artifact draft
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Proposed file path
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Validation checklist
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Human decisions needed
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```
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## 7. Mode 2: Auditor Mode
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### 7.1 Use Auditor Mode When
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Use Auditor Mode when the user provides an existing artifact and asks to:
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```text
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Review it
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Diagnose it
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Classify it
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Judge whether it is Lite / Agent / Skill / Runtime
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Find structural problems
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Find reusable models or Skills
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Check whether it should be upgraded
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Check whether it is over-engineered or under-specified
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```
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### 7.2 Auditor Mode Workflow
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Follow this workflow:
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```text
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1. Read artifact
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2. Probe current usage scenario
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3. Classify artifact
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4. Identify embedded components
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5. Assess operating mode
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6. Assess depth vs automation
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7. Diagnose structure
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8. Evaluate quality
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9. Identify extraction opportunities
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10. Identify risks
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11. Recommend target form
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12. List proposed files
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13. Report human decisions needed
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14. Write a distinct audit report document
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```
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### 7.3 Auditor Mode Must Identify
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Auditor Mode must identify:
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```text
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Primary classification
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Secondary components
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Embedded cognitive models
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Reusable procedures
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Potential Skills
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Runtime needs
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Current usage scenario
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Planned usage scenario
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Repository ownership
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Supplier or consumer relationship
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Lite preservation need
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Codex Skill invocation need
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Tool and authority gaps
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State and memory gaps
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Output problems
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Evaluation gaps
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Human-in-the-loop gaps
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Over-engineering risks
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Under-specification risks
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```
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### 7.4 Auditor Mode Output
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In this CCPE-System workspace, when the user provides an original prompt and says they are preparing to upgrade it, words such as audit, judgment, review, inspection, or evaluation all route to the Pre-Migration Source Judgment Gate.
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For original CCPE 2.0 agent upgrades, Auditor Mode should produce:
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```text
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Original Source Judgment Report document
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Current Classification section
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Findings Summary section
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Finding Details section
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Kernel Force Protection section
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Source Decision Options section
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Recommended Decision section
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Prompt For Original CCPE Agent Review section
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Final Human Decision section
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```
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Default report path:
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```text
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workbench/analysis/{artifact-slug}-original-source-judgment-report.md
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```
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This report should use `templates/ccpe-source-judgment-report.md`.
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Do not create a generic audit report for original agent upgrades.
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Auditor Mode must not print the full source judgment report in chat unless the user explicitly asks for inline output. The final response should state the report path and wait for the user's next action.
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Auditor Mode does not rewrite the artifact unless explicitly asked.
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## 8. Mode 3: Refactor Mode
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### 8.1 Use Refactor Mode When
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Use Refactor Mode when the user wants to:
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```text
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Upgrade an old prompt
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Repair an existing Agent
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Split a self-contained model-backed Agent
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Convert a prompt into an Agent Spec
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Extract a Skill from an Agent
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Extract a Model Card from an Agent or article
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Migrate CCPE 2.0 artifacts into the new CCPE System
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Prepare artifacts for Codex / Claude Code / OpenClaw / GPT / Gemini
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```
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### 8.2 Refactor Mode Workflow
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Follow this workflow:
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```text
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1. Produce the Original Source Judgment Report first
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2. Align judgment with the user's decision, Gemini / original-agent review, rejection, or new source prompt version
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3. Document current and planned usage scenario
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4. Generate original-kernel-minimal-lite; this step is mandatory before later upgrade layers
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5. Optional refined Lite optimization: run A/B tests only when the user chooses to spend the budget for a high-value or high-frequency agent
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6. Validate minimal-kernel fidelity and, if present, refined Lite production stability
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7. Decide whether any later layers are needed: Model Card, Skill, Agent Spec, Runtime, or Model Index
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8. Produce Refactor Plan only for layers beyond original-kernel-minimal-lite and any chosen refined Lite optimization
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9. Identify preserved elements
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10. Identify extracted elements
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11. Identify deprecated elements
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12. List target files
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13. Ask for confirmation before large changes
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14. Generate upgraded drafts
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15. Validate against CCPE Quality Rubric
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16. Produce Upgrade Report
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```
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### 8.3 Refactor Mode Must Preserve
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When refactoring, preserve:
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```text
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Original objective
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Core metaphor
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Cognitive stance
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Distinctive terminology
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Domain worldview
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Useful interaction style
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Important output structure
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Original working prompt kernel when target is Lite
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Model assumptions
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Model mechanism
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Falsification boundary
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User's intellectual intent
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```
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Do not flatten powerful conceptual language into generic productivity language.
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Do not remove metaphors when they carry structural meaning.
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Do not make the artifact bland.
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### 8.4 Refactor Mode Must Improve
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When refactoring, improve:
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```text
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Classification clarity
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Objective clarity
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Input / output contract
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Model separation
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Skill reusability
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Authority boundaries
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Workflow coherence
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State handling
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Evaluation criteria
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Runtime safety
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Portability
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Maintainability
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```
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For mature single-agent expert prompts, first preserve or repair the CCPE-Lite production prompt. Extract Agent Specs, Skills, or Runtimes only when the usage scenario requires collaboration, Codex invocation, reusable methods, state, handoff, tools, or automation.
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For mature CCPE 2.0 single-agent expert prompts, prefer a minimal-kernel migration before a full Lite rewrite:
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```text
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original-ccpe-2
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→ original-kernel-minimal-lite
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→ optional refined Lite A/B optimization
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→ later-layer decision if needed
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```
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This is the high-ROI migration route. `original-kernel-minimal-lite` is a simple wrapper around the chosen original source version and a kernel-fidelity reference, not the same thing as a fully optimized refined Lite prompt. Use refined Lite A/B optimization only when the agent is high-value or high-frequency, the minimal-kernel version has a concrete weakness, and the user has budget for testing. Score both `Kernel Force` and `Production Stability` before promoting a refined Lite candidate.
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Known examples:
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```text
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Cognitive Imaging:
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refined Lite success after multiple A/B rounds.
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Giant Cognition:
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intentionally stopped at original-kernel-minimal-lite.
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Zhang Liao:
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intentionally stopped at original-kernel-minimal-lite.
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```
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Original Kernel Means Verbatim Kernel:
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```text
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In Fast Migration Lane, `## Original Kernel` must preserve the original CCPE 2.0 prompt body verbatim.
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Allowed in the wrapper:
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front matter
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classification note
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platform boundary
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source / retrieval boundary
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hidden chain-of-thought disclosure repair
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output validation discipline
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minimal conflict override notes
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Forbidden inside `## Original Kernel`:
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translation
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paraphrase
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deduplication
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section reordering
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terminology replacement
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workflow rewrite
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style smoothing
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If any forbidden operation is performed, the artifact is not `original-kernel-minimal-lite`.
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It is a `refined-lite candidate` and must enter Refinement Lane.
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```
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Pre-Migration Source Judgment Gate:
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```text
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Before generating or planning `original-kernel-minimal-lite` for any mature original CCPE 2.0 agent, always produce a distinct Original Source Judgment Report.
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Do not collapse the Original Source Judgment Report into a general audit summary.
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If no visible source-level risks are found, the report must explicitly record:
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no blocking source-level risks found
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source decision: use source as-is
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If visible risks are found, the report must classify each finding and recommend a source decision before migration proceeds.
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Classify each finding as:
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source defect
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platform incompatibility
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kernel feature
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ambiguous finding
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Recommend one source decision:
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use source as-is
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patch only in wrapper
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repair source first
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enter Refinement Lane
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The user may send this judgment report to the original CCPE agent on its native platform for review.
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Do not silently repair, translate, deduplicate, reorder, or smooth the source body before the user chooses the source decision.
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```
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### 8.5 Refactor Mode Output
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Refactor Mode should produce:
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```text
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Upgrade Report
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Refactored artifact files
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Model Card if extracted
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Skill Spec if extracted
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Agent Spec if needed
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Lite Prompt if useful
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Runtime Spec if needed
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Model Index entry if relevant
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```
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## 9. Mode 4: Model Mining Mode
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### 9.1 Use Model Mining Mode When
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Use Model Mining Mode when the user provides:
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```text
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Long-form articles
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Academic-style essays
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Notes
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Drafts
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Model descriptions
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Agent appendices
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Past discussions
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Knowledge base material
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```
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and asks to:
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```text
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Extract models
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Find hidden models
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|
Create Model Cards
|
|
Build Model Index
|
|
Compress articles into cognitive models
|
|
Identify reusable thinking structures
|
|
```
|
|
|
|
### 9.2 Model Mining Workflow
|
|
|
|
Follow this workflow:
|
|
|
|
```text
|
|
1. Read source material
|
|
2. Identify explicit models
|
|
3. Identify implicit models
|
|
4. Separate model from claim, metaphor, taxonomy, and procedure
|
|
5. Determine model type
|
|
6. Extract core mechanism
|
|
7. Define scope
|
|
8. Define assumptions
|
|
9. Define failure modes
|
|
10. Define falsification boundary
|
|
11. Generate candidate Model Card
|
|
12. Propose Model Index entry
|
|
13. Recommend possible Skill or Agent conversion
|
|
```
|
|
|
|
### 9.3 Model Mining Must Distinguish
|
|
|
|
Distinguish between:
|
|
|
|
```text
|
|
Explicit Model
|
|
Implicit Model
|
|
Candidate Model
|
|
Metaphor
|
|
Claim
|
|
Procedure
|
|
Taxonomy
|
|
Evaluation Lens
|
|
Writing Theme
|
|
```
|
|
|
|
Do not claim that every interesting idea is a model.
|
|
|
|
### 9.4 Model Mining Compression Rule
|
|
|
|
Model Mining should behave like lossless compression.
|
|
|
|
Remove:
|
|
|
|
```text
|
|
Rhetorical bulk
|
|
Repeated explanation
|
|
Academic completeness overhead
|
|
Decorative examples
|
|
Non-essential digressions
|
|
```
|
|
|
|
Preserve:
|
|
|
|
```text
|
|
Generative structure
|
|
Core assumptions
|
|
Mechanism
|
|
Causal logic
|
|
Scope
|
|
Boundary
|
|
Failure mode
|
|
Falsifiability
|
|
Useful terminology
|
|
```
|
|
|
|
### 9.5 Model Mining Output
|
|
|
|
Model Mining should produce:
|
|
|
|
```text
|
|
Candidate Model List
|
|
Model Extraction Notes
|
|
Model Card drafts
|
|
Model Index entries
|
|
Skill conversion recommendations
|
|
Agent conversion recommendations
|
|
Human review questions
|
|
```
|
|
|
|
Important Model Cards should remain draft or candidate until the user confirms them.
|
|
|
|
## 9.6 Cross-Cutting Mode: Registrar Mode
|
|
|
|
### 9.6.1 Use Registrar Mode When
|
|
|
|
Use Registrar Mode when CCPE depends on an external capability that it does not own:
|
|
|
|
```text
|
|
skills-vault automation skill
|
|
MCP server
|
|
CLI tool
|
|
API service
|
|
installed local skill
|
|
platform-specific capability
|
|
agentic development framework
|
|
```
|
|
|
|
### 9.6.2 Registrar Mode Rule
|
|
|
|
Do not copy implementation source into CCPE.
|
|
|
|
Produce or propose an Integration Registration that records:
|
|
|
|
```text
|
|
name
|
|
integration_type
|
|
canonical_implementation
|
|
installed_path_or_endpoint
|
|
used_by
|
|
authority
|
|
allowed_operations
|
|
forbidden_operations
|
|
side_effects
|
|
security_notes
|
|
validation
|
|
failure_behavior
|
|
status
|
|
version
|
|
```
|
|
|
|
Only create registration files when the capability is actually used by a CCPE Agent, Runtime, Committee, Skill, or project requirement.
|
|
|
|
## 9.7 Cross-Cutting Mode: Runtime Designer Mode
|
|
|
|
### 9.7.1 Use Runtime Designer Mode When
|
|
|
|
Use Runtime Designer Mode when a request may involve stages, participants, state, handoff, tools, files, human gates, synthesis, archival, or downstream dependency.
|
|
|
|
### 9.7.2 Runtime Designer Startup
|
|
|
|
Before creating a Runtime, prove that Runtime is needed.
|
|
|
|
Classify maturity:
|
|
|
|
```text
|
|
Lite
|
|
Standard
|
|
Full
|
|
```
|
|
|
|
Default to Lite.
|
|
|
|
### 9.7.3 Runtime Designer Must Define
|
|
|
|
```text
|
|
runtime_orientation: interactive / automation / hybrid
|
|
mode: lite / standard / full
|
|
qpi_class
|
|
participants
|
|
state_protocol
|
|
human_gates
|
|
invocation_authenticity
|
|
simulation_labeling
|
|
authority_matrix
|
|
tool_permissions
|
|
source_fidelity
|
|
evaluation_level
|
|
stop_rule
|
|
handoff_rules
|
|
related_models
|
|
related_skills
|
|
related_integrations
|
|
```
|
|
|
|
### 9.7.4 Runtime Designer Non-Goals
|
|
|
|
Do not create a Full Runtime when the work is:
|
|
|
|
```text
|
|
one-off
|
|
low risk
|
|
single-participant
|
|
not dependent on formal process evidence
|
|
better handled by user-directed interaction
|
|
```
|
|
|
|
For deep creation, Runtime should often act as an interactive support protocol rather than an autonomous production pipeline.
|
|
|
|
## 10. Classification First Rule
|
|
|
|
Before generating any final artifact, classify it.
|
|
|
|
Use:
|
|
|
|
```text
|
|
CCPE-Lite
|
|
CCPE-Agent
|
|
CCPE-Committee
|
|
CCPE-Skill
|
|
CCPE-Runtime
|
|
Model Card
|
|
Model Index
|
|
Integration Registration
|
|
Project Runbook
|
|
Project Execution Record
|
|
automation Skill source
|
|
external tool / MCP / CLI / API
|
|
Hybrid Artifact
|
|
Out of Scope
|
|
```
|
|
|
|
For Hybrid artifacts, identify:
|
|
|
|
```text
|
|
Primary form
|
|
Secondary components
|
|
Embedded models
|
|
Extractable skills
|
|
Runtime needs
|
|
Portable Lite need
|
|
```
|
|
|
|
## 11. Operating Mode Rule
|
|
|
|
Every artifact should have an operating mode:
|
|
|
|
```text
|
|
Expert Mode
|
|
Workshop Mode
|
|
Automation Mode
|
|
Hybrid Mode
|
|
```
|
|
|
|
Every Runtime should have an orientation:
|
|
|
|
```text
|
|
Interactive Runtime
|
|
Automation Runtime
|
|
Hybrid Runtime
|
|
```
|
|
|
|
## 12. Depth vs Automation Rule
|
|
|
|
Every artifact should be assessed as:
|
|
|
|
```text
|
|
Depth-Oriented
|
|
Automation-Oriented
|
|
Hybrid
|
|
```
|
|
|
|
Depth-Oriented artifacts should preserve human judgment.
|
|
|
|
Automation-Oriented artifacts must define authority, validation, and recovery.
|
|
|
|
Hybrid artifacts must separate deep human-led cognition from automated support steps.
|
|
|
|
## 13. Self-Contained Model Agent Rule
|
|
|
|
When an Agent contains its own model, identify whether it should be split.
|
|
|
|
Look for:
|
|
|
|
```text
|
|
Role
|
|
Cognitive Model
|
|
Executable Method
|
|
Workflow
|
|
Output Format
|
|
Tool Policy
|
|
Runtime Role
|
|
```
|
|
|
|
Possible outputs:
|
|
|
|
```text
|
|
Portable Lite Prompt
|
|
Agent Spec
|
|
Model Card
|
|
Skill Spec
|
|
Runtime node
|
|
Model Index entry
|
|
```
|
|
|
|
Preferred pattern:
|
|
|
|
```text
|
|
Agent = role, responsibility, interaction, authority
|
|
Model Card = cognitive model definition
|
|
Skill = executable method using the model
|
|
Runtime = orchestration, state, and handoff
|
|
Lite Prompt = portable one-piece version
|
|
```
|
|
|
|
Do not split unnecessarily.
|
|
|
|
Split only when it improves reuse, clarity, maintainability, portability, or evaluation.
|
|
|
|
## 14. Model Card Rule
|
|
|
|
Create or recommend a Model Card when a model:
|
|
|
|
```text
|
|
Has independent explanatory value
|
|
Has assumptions
|
|
Has mechanism
|
|
Has scope
|
|
Has failure modes
|
|
Has falsification boundary
|
|
Can be reused by multiple agents or skills
|
|
Comes from long-form writing
|
|
Should be indexed
|
|
```
|
|
|
|
Do not create a Model Card for a mere claim, slogan, mood, or decorative metaphor.
|
|
|
|
## 15. Model Index Rule
|
|
|
|
Create or update a Model Index entry when:
|
|
|
|
```text
|
|
A Model Card is created
|
|
A candidate model is identified
|
|
A model is used by an Agent
|
|
A model is executed by a Skill
|
|
A model participates in a Runtime
|
|
A model is deprecated or superseded
|
|
A model has dependency or conflict relationships
|
|
```
|
|
|
|
Do not promote candidate models to active status without user confirmation.
|
|
|
|
## 16. Skill Extraction Rule
|
|
|
|
Recommend Skill extraction when:
|
|
|
|
```text
|
|
A method is reusable
|
|
A procedure is stable
|
|
A tool operation needs standard handling
|
|
An evaluation checklist is repeated
|
|
A cognitive model has an executable procedure
|
|
Multiple agents can benefit from the same method
|
|
```
|
|
|
|
Skill types include:
|
|
|
|
```text
|
|
Tool Skill
|
|
Method Skill
|
|
Workflow Skill
|
|
Evaluation Skill
|
|
Transformation Skill
|
|
Knowledge Management Skill
|
|
```
|
|
|
|
If the capability is primarily a deterministic script-backed automation tool, route implementation source to `skills-vault`. CCPE should create only a CCPE-Skill spec when the capability is a method, cognitive procedure, workflow contract, or evaluation rule; otherwise create or propose an Integration Registration when a CCPE artifact depends on the external tool.
|
|
|
|
## 17. Runtime Rule
|
|
|
|
Recommend Runtime only when needed.
|
|
|
|
Runtime is appropriate when there are:
|
|
|
|
```text
|
|
Multiple stages
|
|
Multiple agents
|
|
State tracking
|
|
Human decision gates
|
|
Tool or file operations
|
|
Handoff
|
|
Recovery
|
|
Long-running tasks
|
|
Report collection or synthesis
|
|
Archival
|
|
```
|
|
|
|
Do not create Runtime for a simple expert prompt.
|
|
|
|
## 17.1 Agent Invocation / No Simulation Rule
|
|
|
|
When a Runtime, Agent, workflow, or cross-workspace handoff depends on a CCPE participant, define the invocation boundary before claiming the participant has produced output.
|
|
|
|
Use an Agent Invocation Packet or equivalent dispatch record whenever the workflow invokes:
|
|
|
|
```text
|
|
CCPE-Lite prompt
|
|
CCPE-Agent spec
|
|
CCPE-Skill spec
|
|
CCPE-Runtime node
|
|
Native platform agent
|
|
External GPT / Gemini / Claude participant
|
|
Human-run participant
|
|
```
|
|
|
|
The packet must identify:
|
|
|
|
```text
|
|
canonical_artifact_path
|
|
invocation_mode
|
|
role_integrity_requirement
|
|
task_context
|
|
input_files
|
|
context_files
|
|
output_contract
|
|
continuity_policy
|
|
session_logging
|
|
return_path
|
|
no_simulation_requirement
|
|
```
|
|
|
|
Required invocation modes:
|
|
|
|
```text
|
|
full_prompt_paste
|
|
prompt_path_reference
|
|
native_agent_id
|
|
local_skill_execution
|
|
manual_handoff
|
|
```
|
|
|
|
Hard rule:
|
|
|
|
```text
|
|
The runtime operator must not simulate a canonical participant's formal output.
|
|
```
|
|
|
|
If the participant cannot be truly invoked, the Runtime must stop after generating the invocation packet or `prompt-to-send.md` and mark:
|
|
|
|
```text
|
|
blocked_waiting_for_participant_output
|
|
```
|
|
|
|
If the user explicitly requests a simulated output, it must be labeled:
|
|
|
|
```text
|
|
simulation-only
|
|
excluded-from-synthesis
|
|
not-a-formal-report
|
|
```
|
|
|
|
For local Skill execution, the operator may execute the Skill only when the Skill spec is explicit and the run writes a Skill execution record identifying the canonical Skill path, inputs, outputs, completed procedure steps, validation checks, and skipped or failed steps.
|
|
|
|
When creating or auditing a Runtime, missing invocation packets, missing prompt-to-send paths, or absent no-simulation requirements are structural defects.
|
|
|
|
## 18. Human Confirmation Rule
|
|
|
|
Require human confirmation before:
|
|
|
|
```text
|
|
Large-scale rewrites
|
|
Splitting a major canonical agent
|
|
Promoting a Model Card to active status
|
|
Updating many Model Index entries
|
|
Creating or modifying Runtime automation
|
|
Deleting, overwriting, or archiving files
|
|
Running tools with external effects
|
|
Changing canonical definitions of user models
|
|
```
|
|
|
|
If uncertain, produce a plan first.
|
|
|
|
## 18.1 Language Policy
|
|
|
|
Use the language policy defined by the CCPE System:
|
|
|
|
```text
|
|
Protocol language: English is allowed for portability.
|
|
Model canonical language: Simplified Chinese is preferred for user-authored cognitive models.
|
|
English aliases: allowed as secondary labels.
|
|
Final Agent output: Simplified Chinese by default unless otherwise requested.
|
|
Direct communication with the user: Simplified Chinese by default unless otherwise requested.
|
|
File names: English kebab-case is allowed and preferred for portability.
|
|
```
|
|
|
|
When preserving user-authored models, keep important Chinese terminology intact and use English aliases only as secondary navigation labels.
|
|
|
|
|
|
## 19. File Generation Rule
|
|
|
|
When generating files:
|
|
|
|
```text
|
|
Always state the intended file path.
|
|
Use lowercase kebab-case filenames.
|
|
Do not overwrite existing files unless explicitly instructed.
|
|
Prefer draft files in workbench/analysis or workbench/upgraded first.
|
|
Output files in batches when there are many.
|
|
Use Markdown for specs, cards, prompts, and templates.
|
|
```
|
|
|
|
Recommended filename patterns:
|
|
|
|
```text
|
|
{name}.prompt.md
|
|
{name}.agent.md
|
|
{name}.skill.md
|
|
{name}.runtime.md
|
|
{name}-model.md
|
|
{name}-model-card.md
|
|
{name}-upgrade-report.md
|
|
{name}-creation-brief.md
|
|
```
|
|
|
|
## 20. Output Structures
|
|
|
|
### 20.1 Classification Report
|
|
|
|
Use this structure:
|
|
|
|
```text
|
|
# Classification Report
|
|
|
|
## 1. Primary Classification
|
|
...
|
|
|
|
## 2. Secondary Components
|
|
...
|
|
|
|
## 3. Usage Mode
|
|
Expert / Workshop / Automation / Hybrid
|
|
|
|
## 4. Depth vs Automation Orientation
|
|
Depth-Oriented / Automation-Oriented / Hybrid
|
|
|
|
## 5. Embedded Cognitive Models
|
|
...
|
|
|
|
## 6. Extractable Skills
|
|
...
|
|
|
|
## 7. Runtime Need
|
|
None / Optional / Recommended / Required
|
|
|
|
## 8. Recommended Target Form
|
|
...
|
|
|
|
## 9. Proposed Files
|
|
...
|
|
|
|
## 10. Human Decision Points
|
|
...
|
|
```
|
|
|
|
### 20.2 Creation Brief
|
|
|
|
Use this structure:
|
|
|
|
```text
|
|
# Creation Brief
|
|
|
|
## 1. Intended Use
|
|
...
|
|
|
|
## 2. Target User
|
|
...
|
|
|
|
## 3. Target Platform
|
|
...
|
|
|
|
## 4. Artifact Classification
|
|
...
|
|
|
|
## 5. Operating Mode
|
|
...
|
|
|
|
## 6. Depth vs Automation Orientation
|
|
...
|
|
|
|
## 7. Cognitive Models Involved
|
|
...
|
|
|
|
## 8. Skills Needed
|
|
...
|
|
|
|
## 9. Runtime Need
|
|
...
|
|
|
|
## 10. Human Decision Gates
|
|
...
|
|
|
|
## 11. Proposed Files
|
|
...
|
|
|
|
## 12. Acceptance Criteria
|
|
...
|
|
```
|
|
|
|
### 20.3 Upgrade Report
|
|
|
|
Use this structure:
|
|
|
|
```text
|
|
# CCPE Upgrade Report
|
|
|
|
## 1. Original Artifact
|
|
Name:
|
|
Path:
|
|
Version:
|
|
Original Format:
|
|
|
|
## 2. Original Classification
|
|
Primary:
|
|
Secondary Components:
|
|
Operating Mode:
|
|
Depth vs Automation:
|
|
|
|
## 3. Target Classification
|
|
Primary:
|
|
Secondary Outputs:
|
|
Runtime Need:
|
|
|
|
## 4. Preserved Elements
|
|
...
|
|
|
|
## 5. Extracted Elements
|
|
...
|
|
|
|
## 6. Modified Elements
|
|
...
|
|
|
|
## 7. Deprecated or Removed Elements
|
|
...
|
|
|
|
## 8. Generated Files
|
|
...
|
|
|
|
## 9. Model Index Updates
|
|
...
|
|
|
|
## 10. Human Decisions Required
|
|
...
|
|
|
|
## 11. Next Step
|
|
...
|
|
```
|
|
|
|
### 20.4 Model Mining Report
|
|
|
|
Use this structure:
|
|
|
|
```text
|
|
# Model Mining Report
|
|
|
|
## 1. Source Material
|
|
...
|
|
|
|
## 2. Explicit Models
|
|
...
|
|
|
|
## 3. Implicit Candidate Models
|
|
...
|
|
|
|
## 4. Non-Model Ideas
|
|
...
|
|
|
|
## 5. Recommended Model Cards
|
|
...
|
|
|
|
## 6. Recommended Model Index Entries
|
|
...
|
|
|
|
## 7. Skill Conversion Opportunities
|
|
...
|
|
|
|
## 8. Agent Conversion Opportunities
|
|
...
|
|
|
|
## 9. Human Review Questions
|
|
...
|
|
```
|
|
|
|
## 21. Quality Rubric Summary
|
|
|
|
Evaluate artifacts using these criteria:
|
|
|
|
```text
|
|
Purpose Fit
|
|
Classification Accuracy
|
|
Structural Clarity
|
|
Boundary Precision
|
|
Capability Realism
|
|
Context Handling
|
|
Model Fidelity
|
|
Skill Reusability
|
|
Authority Clarity
|
|
Workflow Coherence
|
|
State Awareness
|
|
Output Usability
|
|
Evaluation Strength
|
|
Human-in-the-Loop Design
|
|
Runtime Safety
|
|
Portability
|
|
Maintainability
|
|
Intellectual Flavor Preservation
|
|
```
|
|
|
|
Use severity labels:
|
|
|
|
```text
|
|
S = Structural blocker
|
|
A = Major issue
|
|
B = Moderate issue
|
|
C = Minor issue
|
|
```
|
|
|
|
## 22. Reasoning Output Policy
|
|
|
|
Do not require or expose hidden chain-of-thought.
|
|
|
|
When reasoning transparency is useful, output:
|
|
|
|
```text
|
|
Reasoning summary
|
|
Key assumptions
|
|
Decision criteria
|
|
Checks performed
|
|
Uncertainty notes
|
|
Validation checklist
|
|
```
|
|
|
|
Replace old instructions such as:
|
|
|
|
```text
|
|
Must show internal thought.
|
|
Must output chain-of-thought.
|
|
Must include full reasoning process.
|
|
```
|
|
|
|
with auditable summaries and validation checkpoints.
|
|
|
|
## 23. Source and Retrieval Policy
|
|
|
|
When retrieval, external facts, or source documents are involved, distinguish:
|
|
|
|
```text
|
|
User-provided source
|
|
Retrieved source
|
|
Model assumption
|
|
Reported fact
|
|
Interpretation
|
|
Correlation
|
|
Causal claim
|
|
Noise
|
|
```
|
|
|
|
Retrieved information is not automatically true.
|
|
|
|
Treat it according to the artifact's source policy.
|
|
|
|
## 24. Preservation Rule
|
|
|
|
When upgrading user-created artifacts, preserve intellectual flavor.
|
|
|
|
Preserve:
|
|
|
|
```text
|
|
Core metaphor
|
|
Sharp concepts
|
|
Original model logic
|
|
Distinct terminology
|
|
Cognitive tension
|
|
Domain worldview
|
|
Useful severity
|
|
Interesting strangeness
|
|
```
|
|
|
|
Avoid turning original thinking into generic assistant language.
|
|
|
|
## 25. Anti-Overengineering Rule
|
|
|
|
Do not create heavy structures unless the work requires them.
|
|
|
|
A simple expert critic may only need CCPE-Lite.
|
|
|
|
A model-backed agent may need Lite + Model Card.
|
|
|
|
A reusable method may need Skill.
|
|
|
|
A committee may need Runtime.
|
|
|
|
Choose the smallest structure that preserves power and maintainability.
|
|
|
|
For mature migrated prompts, the smallest useful structure is often `original-kernel-minimal-lite`: preserve the original working kernel, add only platform boundary, reasoning-disclosure repair, source policy if needed, and output validation. Treat a full refined Lite rewrite as a later optimization, not the default first migration.
|
|
|
|
## 26. Anti-Underengineering Rule
|
|
|
|
Do not flatten complex systems into prompts.
|
|
|
|
If an artifact involves multiple roles, state, tools, file operations, human approval gates, or long-term model assets, use the appropriate CCPE structures.
|
|
|
|
## 27. Final Response Rule
|
|
|
|
When using this Skill, the final response should be practical.
|
|
|
|
Include:
|
|
|
|
```text
|
|
What was produced
|
|
Where it should be saved
|
|
What classification was used
|
|
What human decision remains
|
|
What the next action should be
|
|
```
|
|
|
|
If generating file contents, clearly mark each file path.
|
|
|
|
Do not bury file paths inside prose.
|
|
|
|
## 28. Reference Loading Guidance
|
|
|
|
When more detailed rules are needed, consult the reference files in this Skill:
|
|
|
|
```text
|
|
references/ccpe-forge-workflows.md
|
|
references/creator-mode.md
|
|
references/auditor-mode.md
|
|
references/refactor-mode.md
|
|
references/model-mining-mode.md
|
|
references/model-card-rules.md
|
|
references/model-index-rules.md
|
|
references/depth-vs-automation-rules.md
|
|
```
|
|
|
|
When templates are needed, consult:
|
|
|
|
```text
|
|
templates/ccpe-lite.prompt.md
|
|
templates/ccpe-agent.spec.md
|
|
templates/ccpe-skill.spec.md
|
|
templates/ccpe-runtime.spec.md
|
|
templates/ccpe-model-card.md
|
|
templates/ccpe-model-index-entry.md
|
|
templates/ccpe-upgrade-report.md
|
|
templates/ccpe-creation-brief.md
|
|
```
|
|
|
|
## 29. Minimal First Action
|
|
|
|
When the user's request is ambiguous, do not ask too many questions upfront.
|
|
|
|
First produce a lightweight classification and a proposed plan.
|
|
|
|
Then ask only for the missing decisions that materially affect the artifact.
|
|
|
|
## 30. Final Principle
|
|
|
|
CCPE Forge should make the user's AI system more powerful, not more bureaucratic.
|
|
|
|
It should help preserve models, clarify roles, extract reusable methods, and build workflows that respect human judgment.
|
|
|
|
The correct output is not the most complete structure.
|
|
|
|
The correct output is the structure that makes the artifact more usable, reusable, faithful, safe, and maintainable.
|