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Ccf Submission Checker

CommunityPopular
mikubaka88
ccf-submission-checker

Check CCF venue rules and submission packages: template, pages, anonymity, PDF build, metadata, and reproducibility. Use for 投稿检查, 会议格式, page limits, and artifact readiness. Verify current official rules for the exact venue/year/track. Manuscript polishing belongs to ccf-paper-writer.

Overview

Publishermikubaka88
RepositoryCCFA-Skills
Skill nameccf-submission-checker
Stars
2.6K
Forks
116
Bundled files
1
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.

  • 1 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 Submission Checker 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-submission-checker .claude/skills/ccf-submission-checker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccf Submission Checker 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 Submission Checker 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 Submission Checker 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 Submission Checker

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

Treat submission as a build, venue-policy, page-budget, and artifact-readiness gate. Use local venue guides for expected rules, then require official-policy freshness for final decisions. Do not rewrite paper content.

Modes

  • venue-format: template, page limit, anonymity, author block, supplementary and camera-ready rules.
  • package-check: LaTeX/PDF build, metadata, fonts, page count, file structure, and submission checklist.
  • artifact: code/data/model release plan, environment, seeds, hardware, licenses, artifact README, and reproducibility appendix.
  • full: venue + package + artifact.

Workflow

  1. Identify venue/year/track, submission mode, project directory, TeX/PDF files, supplementary/artifact files, and deadline pressure.
  2. Read ccfa.yaml when available. If absent, proceed with supplied files and state that project-state tracking is unavailable.
  3. For venue questions, read ../ccf-paper-writer/references/venue-guides/index.md and the specific venue guide before checking official freshness.
  4. For package checks, inspect the actual TeX/PDF/build output, page accounting for the exact venue/year/track, anonymity, fonts, metadata, references, and applicable required forms. For ICLR 2027, apply the matched guide's year-specific template, stage-specific page budget, and AI-use statement/form checks; unconfirmed usage facts remain not verified. Read logs and affected pages; do not infer a successful build from file presence.
  5. For artifact checks, build a reproducibility checklist: code, data, models, environment, seeds, hardware, license, access restrictions, and README.
  6. Hand off to ccf-paper-writer for text/page rewrites: compression when over limit, substantive expansion when actual explanation is missing, and normal polishing when within budget. Hand off to ccf-experiment-designer for missing reproducibility experiments, ccf-visual-composer for figure/table float order, caption placement, font, clipping, palette, or visual readability fixes, and ccf-rebuttal-writer for post-review response packaging.

Output Contract

text
Mode:
Venue and rule freshness:
Files checked:
Pass/fail checklist:
Build/package issues:
Anonymity/page/font/metadata issues:
Length budget status:
Artifact/reproducibility issues:
Required fixes:
Next CCFA owner:

Check Scope

Follow ../ccf-common/references/handoff-modes.md and ../ccf-common/references/task-modes.md. Run checks relevant to the requested mode and actual artifacts. Distinguish pass, fail, not applicable, and not verified; missing tools do not imply passing or failure. Record the official rule URL and the date checked. Reuse the project build configuration and place generated logs/previews in the established or shared task build directory. Update the canonical readiness report in place and distinguish old PDFs from successful current builds. Recheck only affected build surfaces after a fix. Underfilling a page budget alone is not a venue violation. Do not upload or submit files merely because a readiness check was requested.

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 Submission Checker AI skill do?

Check CCF venue rules and submission packages: template, pages, anonymity, PDF build, metadata, and reproducibility. Use for 投稿检查, 会议格式, page limits, and artifact readiness. Verify current official rules for the exact venue/year/track. Manuscript polishing belongs to ccf-paper-writer.

Why use Ccf Submission Checker on TypingMind?

Because you install it once and use it with any model. Ccf Submission Checker 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 Submission Checker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikubaka88/CCFA-Skills/tree/main/ccf-submission-checker. 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 Submission Checker?

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 Submission Checker?

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

Is the Ccf Submission Checker 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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