How do I install the Ccf Rank skill?

Run npx skillmds@latest add duruii/ccf-rank in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ccf Rank safe to use?

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Which AI agents work with Ccf Rank?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ccf Rank free to use?

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Who published Ccf Rank?

duruii (@duruii) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ccf Rank by duruii — Agent Skill

Name: Ccf Rank
Author: duruii

Ccf Rank

Query CCF (China Computer Federation) venue rankings for conferences and journals using year-partitioned reference data. Use when users ask for CCF level (A/B/C), category/domain, or rank verification for a venue abbreviation/full name (for example ICML, CVPR, TOCS), or request batch lookup/comparison across venues or years.

duruii e9e2377 8 files · 297.1 KB Updated Sep 17, 2026

File contents

ccf-rank

Use this skill to answer CCF ranking questions quickly and consistently.

Data source

Dataset index: references/manifest.json
Year data: references/<year>/rankings.json
Year exclusions: references/<year>/excluded_venues.json

Query workflow

Run the lookup script with Node.js (preferred for installable skill compatibility):
```
node scripts/query_ccf_rank.mjs "<venue name>"
```
It uses the latest available year by default. Add --year <YYYY> to query a specific year.

For ambiguous venue names, narrow by type/rank:

node scripts/query_ccf_rank.mjs "<query>" --year 2026 --type conference --rank A --top 20

If user asks for several venues, run the script per venue and return a compact table with: venue, type, rank, area, url.
If there are multiple high-score matches, show the top matches and explicitly ask user to disambiguate.

Output rules

Always include type (conference or journal) and rank (A/B/C).
Include the CCF area category when available.
Include the canonical venue name and DBLP URL from the dataset.
If no confident match is found, say so explicitly and list closest candidates.

Update workflow (new CCF version)

Replace the PDF file with the newer official CCF version.

Rebuild one year dataset:

python scripts/build_ccf_dataset.py "/absolute/path/to/new.pdf" --year 2028

Update references/<year>/excluded_venues.json if the PDF has deleted entries that text extraction cannot reliably detect from styling.
Spot-check representative venues (for example ICML, NeurIPS, CVPR, TOCS) using query_ccf_rank.mjs --year <year>.

---name: ccf-rankdescription: Query CCF (China Computer Federation) venue rankings for conferences and journals using year-partitioned reference data. Use when users ask for CCF level (A/B/C), category/domain, or rank verification for a venue abbreviation/full name (for example ICML, CVPR, TOCS), or request batch lookup/comparison across venues or years.---# ccf-rankUse this skill to answer CCF ranking questions quickly and consistently.## Data source- Dataset index: `references/manifest.json`- Year data: `references/<year>/rankings.json`- Year exclusions: `references/<year>/excluded_venues.json`## Query workflow1. Run the lookup script with Node.js (preferred for installable skill compatibility): ```bash node scripts/query_ccf_rank.mjs "<venue name>" ``` It uses the latest available year by default. Add `--year <YYYY>` to query a specific year.2. For ambiguous venue names, narrow by type/rank: ```bash node scripts/query_ccf_rank.mjs "<query>" --year 2026 --type conference --rank A --top 20 ```3. If user asks for several venues, run the script per venue and return a compact table with: `venue`, `type`, `rank`, `area`, `url`.4. If there are multiple high-score matches, show the top matches and explicitly ask user to disambiguate.## Output rules- Always include `type` (`conference` or `journal`) and `rank` (`A/B/C`).- Include the CCF `area` category when available.- Include the canonical venue name and DBLP URL from the dataset.- If no confident match is found, say so explicitly and list closest candidates.## Update workflow (new CCF version)1. Replace the PDF file with the newer official CCF version.2. Rebuild one year dataset: ```bash python scripts/build_ccf_dataset.py "/absolute/path/to/new.pdf" --year 2028 ```3. Update `references/<year>/excluded_venues.json` if the PDF has deleted entries that text extraction cannot reliably detect from styling.4. Spot-check representative venues (for example `ICML`, `NeurIPS`, `CVPR`, `TOCS`) using `query_ccf_rank.mjs --year <year>`.

Frequently asked questions