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Ccf Literature Monitor

CommunityPopular
mikubaka88
ccf-literature-monitor

Scan recent arXiv/OpenReview papers, venue feeds, labs, and competitors for overlap. Use for 竞品监控, 新论文追踪, 论文跟踪, and recurring novelty watch. Give dated evidence and changes since the prior scan. Broad related-work retrieval belongs to ccf-literature-searcher.

Overview

Publishermikubaka88
RepositoryCCFA-Skills
Skill nameccf-literature-monitor
Stars
2.6K
Forks
116
Bundled files
3
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.

  • 3 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 Literature Monitor 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-literature-monitor .claude/skills/ccf-literature-monitor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccf Literature Monitor 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 Literature Monitor 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 Literature Monitor 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 Literature Monitor

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

Monitor arXiv, OpenReview, conference/proceedings feeds, project pages, labs, and named competitors for new papers that could overlap with the user's idea or paper. Report findings factually. Do not exaggerate novelty threats, dismiss real overlap, or infer priority from weak evidence. Provide actionable signals: RELAX, RESEARCH, FOLLOW-UP.

Modes

  • arxiv-watch: scan recent papers in target categories or keyword clusters.
  • venue-watch: scan OpenReview, proceedings, accepted-paper lists, or official venue pages.
  • novelty-check: compare a given idea with recent papers and flag overlap.
  • trend-scouting: summarize emerging directions, crowded areas, and under-tested gaps.
  • competitor-tracking: monitor named research groups, labs, repositories, datasets, benchmarks, or authors.
  • paper-alert-digest: produce a recurring watch report suitable for saving into a project folder.

Skill Linkage

  • @READS ccfa.yaml for idea state.
  • @SHARES findings with ccf-literature-searcher.
  • @SHARES novelty implications with ccf-idea-reviewer.
  • @SHARES rescue or differentiation opportunities with ccf-idea-optimizer.
  • @SHARES citation and positioning candidates with ccf-paper-writer.
  • @FLAGS conflicting papers for ccf-integrity-auditor.

Invocation Controls

CCFA Handoff Mode: PARTIAL (Recommended). Follow metadata.ccf_skill_controls.handoff_question_mode and ../ccf-common/references/handoff-modes.md.

Load ../ccf-common/references/task-modes.md before deciding exploratory, quick, or standard mode.

Treat unpublished ideas, draft abstracts, methods, and results as private material. Load ../ccf-common/references/privacy-and-evidence.md before using private text in queries. Use public-safe keywords, public method names, venue names, and user-approved query text.

Load ../ccf-common/references/review-output-standards.md when monitor results feed idea scoring, paper review, or score-risk language.

Workflow

  1. Load references/monitoring-workflow.md.
  2. If project state is available (ccfa.yaml), read the current idea, target venue, claims, and tracked competitors.
  3. Choose the mode and time window. If the user says "latest", "recent", "new", "this week", or "today", verify the current date and use an explicit date range.
  4. Execute the requested monitoring mode using public-safe queries and source-quality exclusions from the shared policy.
  5. Classify overlaps by problem, mechanism, evidence, benchmark, dataset, claim, and venue positioning.
  6. Report new or materially changed findings, with precise implications for the requested research decision. Use existing report history to avoid presenting the same paper as new.
  7. Execute already requested downstream work through its owner; otherwise offer optional handoff to ccf-literature-searcher for deep retrieval, ccf-idea-reviewer for score impact, ccf-idea-optimizer for differentiation, or ccf-paper-writer for related-work integration.

Output Contract

For each monitoring execution, report in this structure:

markdown
## Literature Monitoring Report: [Mode] - [Topic/Venue]

**As of:** [YYYY-MM-DD]
**Time range:** [from] to [to]
**Sources scanned:** [arXiv categories / venues / labs / APIs]
**Papers scanned:** [count]
**High-relevance papers:** [count]
**Overall signal:** RELAX / RESEARCH / FOLLOW-UP

| Title | Source | Overlap level | Overlap type | Evidence basis | Action |
|:---|:---|:---:|:---|:---|:---:|
| [title] | [arXiv/OpenReview/venue] | None/Low/Medium/High | Problem/Method/Evidence/Benchmark | [abstract/intro/result] | RELAX/RESEARCH/FOLLOW-UP |

**Actionable flags:**
- RELAX: no material overlap found in the scanned window; continue, but do not claim full novelty proof.
- RESEARCH: partial overlap or possible close work; deep retrieve via `ccf-literature-searcher`.
- FOLLOW-UP: significant overlap in problem, mechanism, and evidence; route to `ccf-idea-reviewer` or `ccf-idea-optimizer` before writing stronger novelty claims.

**Handoff signals:**
- `ccf-literature-searcher`: [papers or clusters to retrieve deeply]
- `ccf-idea-reviewer`: [novelty or score implications]
- `ccf-idea-optimizer`: [differentiation or rescue direction]
- `ccf-paper-writer`: [related-work or positioning update]
- `ccf-integrity-auditor`: [citation/attribution conflict]

**Output self-check:** [date range explicit, source basis stated, table valid, no unsupported novelty conclusion]

References

  • references/monitoring-workflow.md — Workflow, api-key-management, search strategy.
  • references/report-template.md — Output template and formatting.
  • ../ccf-common/references/source-registry.yaml — Competitor and arxiv links.

Execution And Persistence

Distinguish a single scan from recurring monitoring. A recurring report format is not a scheduler: claim ongoing monitoring only after an actual authorized scheduled job exists. Record the time window and coverage; an empty scan is not proof of novelty. Deduplicate by stable paper identity and version, separating first publication from revisions. Batch independent feeds when supported; preserve real monitoring history under ../ccf-common/references/artifact-contracts.md. Follow no-new-files and existing project-state authorization.

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 Literature Monitor AI skill do?

Scan recent arXiv/OpenReview papers, venue feeds, labs, and competitors for overlap. Use for 竞品监控, 新论文追踪, 论文跟踪, and recurring novelty watch. Give dated evidence and changes since the prior scan. Broad related-work retrieval belongs to ccf-literature-searcher.

Why use Ccf Literature Monitor on TypingMind?

Because you install it once and use it with any model. Ccf Literature Monitor 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 Literature Monitor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikubaka88/CCFA-Skills/tree/main/ccf-literature-monitor. 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 Literature Monitor?

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 Literature Monitor?

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

Is the Ccf Literature Monitor 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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