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):

   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:

   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:

   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>.