# 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.

- Skill: `duruii/ccf-rank` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add duruii/ccf-rank`
- Raw SKILL.md: https://api.skillmd.com/api/skills/duruii/ccf-rank/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: duruii (https://skillmd.com/u/duruii)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/duruii/ccf-rank

---


# 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

1. 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>`.