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Ccf Pipeline Orchestrator

CommunityPopular
mikubaka88
ccf-pipeline-orchestrator

Plan or coordinate CCF research stages, goals, gates, artifacts, and ccfa.yaml state. Use for 任务拆解, 流程规划, project status, and explicitly requested end-to-end coordination. Specialist skills own research outputs; ccf-project-scaffolder owns folder/template creation.

Overview

Publishermikubaka88
RepositoryCCFA-Skills
Skill nameccf-pipeline-orchestrator
Stars
2.6K
Forks
116
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mikubaka88 on GitHub. Read the source before you install it.

Installation

Install the Ccf Pipeline Orchestrator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mikubaka88/CCFA-Skills.git /tmp/CCFA-Skills
mkdir -p .claude/skills
cp -r /tmp/CCFA-Skills/ccf-pipeline-orchestrator .claude/skills/ccf-pipeline-orchestrator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccf Pipeline Orchestrator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ccf Pipeline Orchestrator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ccf Pipeline Orchestrator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CCF Pipeline Orchestrator

Family File Contract

Before writing, resolve the canonical output and one stable working directory per task/artifact. Reuse explicit or established task paths; otherwise use project-root ccfa-workfiles/<purpose>/<artifact-id>/, with source/, assets/, cache/, and build/ only as needed. Update current files in place; do not scatter intermediates or create iteration copies. Preserve inputs and required evidence; clean only verified disposable files created by this task. Use UTF-8 text I/O and check Chinese text after saving or rendering. For file work, apply artifact-contracts.md and reuse the same paths across skill transitions.

Collaboration Contract

Before specialist execution, read and apply ccf-humanization first, then ccf-common. At every handoff, reuse their applicable active rules or refresh missing/changed ones. Both preflights are required even without prose; detailed editing, experiment, and maintenance modes run only when relevant.

Keep one integrating owner and actively use other skills to resolve missing prerequisites or check material findings. Reuse applicable evidence; do not skip necessary groundwork to save tokens. Before finalizing, integrate contributions and verify affected results. Follow the conditional cooperation routes; avoid unrelated stages and duplicate reports.

Core Rule

Operate as the project coordinator and workflow planner. Clarify the goal, map the current stage, update or read ccfa.yaml, define gates, and name the next owner skill. Specialist skills own downstream outputs. For a plan-only request, return the plan. For explicitly requested end-to-end execution, coordinate the authorized owners through completion rather than stopping after naming the next skill. Follow ../ccf-common/references/task-modes.md: if the user asks for a short plan, checklist, YAML update, table, or narrative roadmap, use that visible shape instead of forcing a fixed report.

Ensure Humanization and Common are active before planning and every contributing skill. Reuse their applicable rules at handoffs. Apply Humanization's detailed prose modes within authorized writing stages; raw planning and rendering still receive its baseline without a manuscript-edit workflow.

Follow ../ccf-common/references/handoff-modes.md and ../ccf-common/references/artifact-contracts.md. Later user corrections normally steer the active project: preserve valid completed work, update affected requirements, and continue. Do not invent completed stages or automatic background jobs.

Workflow

  1. Identify target venue, current stage, available artifacts, constraints, deadline pressure, and the user's immediate goal.
  2. Read ccfa.yaml when available; if absent, continue with supplied artifacts. Do not require setup or create project state merely to route work; disclose its absence only when it limits requested tracking.
  3. For unclear projects, use references/workflow-planning/intake-protocol.md, approach-options.md, and design-brief-template.md.
  4. Assign integrating owners using ../ccf-common/references/routing.md. Work backward from the requested outcome to missing prerequisites and material checks, including upstream work not named by the user. Distinguish bounded contributions from next-artifact ownership; carry both active preflights throughout and apply detailed Humanization modes when relevant. Preserve explicit scope limits.
  5. Define each applicable gate through its required evidence, output, pass condition, blocker, and responsible skill. Mark prerequisites satisfied, missing, conflicting, or outside scope; reuse valid ones and resolve missing/conflicting ones before advancing dependent conclusions. Tool completion alone does not pass a gate.
  6. Use the continuity/return contract in ../ccf-common/references/handoff-modes.md: carry current scope, evidence/version, canonical paths, edit ownership, and the exact next action. Update existing ccfa.yaml stage/gate fields only when state maintenance is authorized; preserve unrelated fields and schema. A planning-only request proposes changes without writing.
  7. Integrate specialist results, resolve conflicts against source evidence, and check affected downstream conclusions. Advance gates from actual evidence; preserve valid completed work and continue independent stages around a blocker. Reopen only changed dependencies or unresolved findings. Finish when requested outputs and applicable prerequisites/checks are complete, or identify the precise dependent result that remains incomplete.

Adaptive Output Contract

Put the requested artifact first: roadmap, next-step decision, task list, handoff packet, or ccfa.yaml patch instructions. Use the full structure below only for standard planning, ambiguous multi-stage projects, or when the user asks for a complete coordination report.

text
Project goal:
Current stage:
Known artifacts:
Missing artifacts:
Gate decision:
Next owner skill:
Handoff packet:
ccfa.yaml update:
Risks / blockers:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ccf Pipeline Orchestrator AI skill do?

Plan or coordinate CCF research stages, goals, gates, artifacts, and ccfa.yaml state. Use for 任务拆解, 流程规划, project status, and explicitly requested end-to-end coordination. Specialist skills own research outputs; ccf-project-scaffolder owns folder/template creation.

Why use Ccf Pipeline Orchestrator on TypingMind?

Because you install it once and use it with any model. Ccf Pipeline Orchestrator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ccf Pipeline Orchestrator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikubaka88/CCFA-Skills/tree/main/ccf-pipeline-orchestrator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ccf Pipeline Orchestrator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ccf Pipeline Orchestrator?

As many as you like. As long as a model supports skills, you can use Ccf Pipeline Orchestrator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ccf Pipeline Orchestrator AI skill free?

Yes. It is published on GitHub by mikubaka88 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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