add-conference

name	add-conference
description	Add a conference-year to AI-Trend from a single URL (OpenReview group/venue URL, or an openaccess.thecvf.com CVPR/ICCV URL). Runs the whole pipeline: download paper metadata, extract + curate topics, assign labels, optionally fetch citation counts, regenerate trends and the GitHub Pages site, and open a PR. Use when the user pastes a conference URL and asks to add/track it.

Add Conference (URL → full pipeline)

The natural-language front door. The user gives a URL; you run the pipeline by calling ai-trend subcommands and doing the topic-curation reasoning yourself (the same judgement as the curate-topics skill).

All commands run from the repo root, isolated from user site-packages:

PYTHONNOUSERSITE=1 ./env/bin/ai-trend ...

Inputs

• URL — e.g. https://openreview.net/group?id=ICML.cc/2025/Conference or https://openaccess.thecvf.com/CVPR2024?day=all.
• Optionally whether to fetch citations (slow; default ask the user).

Procedure

1. Download metadata

PYTHONNOUSERSITE=1 ./env/bin/ai-trend ingest-url "$URL"

This detects the source (OpenReview vs thecvf), downloads, and writes data/<year>/<month>_<key>.csv. Note the printed path as $CSV. (CVPR/ICCV have no abstracts — that's expected.)

2. Extract + curate topics (your reasoning — like curate-topics)

PYTHONNOUSERSITE=1 ./env/bin/ai-trend candidates "$CSV" \
  --conference "$CONF" --year "$YEAR" -o /tmp/candidates.json

Read /tmp/candidates.json; for every candidate decide existing / new / noise / other (anchor on existing topics, prefer existing, lowercase keywords, be conservative with new). Write /tmp/decision.json, then:

PYTHONNOUSERSITE=1 ./env/bin/ai-trend curate --dry-run /tmp/decision.json
PYTHONNOUSERSITE=1 ./env/bin/ai-trend curate /tmp/decision.json

(See the curate-topics skill for the full decision rubric.)

3. Assign topics

PYTHONNOUSERSITE=1 ./env/bin/ai-trend assign "$CSV"

For cross-year trend consistency after a taxonomy change, also re-assign the other years (re-run assign on each *_topics.csv, or re-migrate + re-assign as in AUTOMATION.md) — mention this to the user; it re-labels existing years.

4. Citations (optional, slow — ask first)

Semantic Scholar is unauthenticated and rate-limited (~1 paper/sec, frequent 429s), so this is bounded to the conference-year's top/emerging-topic papers and cached:

PYTHONNOUSERSITE=1 ./env/bin/ai-trend citations "${CSV}_topics.csv"

Resumable — safe to re-run. Skip if the user doesn't want to wait.

5. Trends + site

PYTHONNOUSERSITE=1 ./env/bin/ai-trend trends --include-counts -o data/trends/trends.json
PYTHONNOUSERSITE=1 ./env/bin/ai-trend export-site   # picks up citations sidecars

Optionally regenerate the README (ai-trend trends --format markdown).

6. Verify + PR

Smoke-check the new conference-year appears (manifest shard + browsable), then open a PR (/pr) summarising the added venue, new topics, and any citations. Merging republishes the site via the Pages workflow.

Notes

• Source detection + venue/year come from the URL via ai_trend/sources.py and the conference registry (config/conferences.json, with a source field).
• New conferences must exist in the registry; if the URL's venue isn't there, add it to config/conferences.json first (key/label/tokens/month/source).
• Do not hand-edit config/*.json topics — always go through ai-trend curate.