Select Venue

Select Venue

Turn "where should I submit this paper?" into a ranked shortlist of venue-track pairs with scores, deadlines, page budgets, and an explicit verification status — grounded in the repo's venues/ profiles, not vibes.

When to use

• The user asks where to submit a paper, for target-venue recommendations, or to compare candidate venues/tracks.
• The user asks "does my paper fit venue X?" or "research track or industry track?"
• The user mentions CORE rank, h5-index, CCF class, acceptance rates, or upcoming CS conference deadlines in a submission-planning context.

Not for: parsing one specific CFP into a profile (parse-cfp), adapting a draft to an already-chosen venue (tailor-to-venue), or pre-submission compliance checks (preflight-check).

Inputs

• Topic — one or two sentences on what the paper is about.
• Contribution type — classify using references/track-fit.md; ask the user if ambiguous.
• Constraints — earliest realistic submission date, prestige goals (and whether CCF class matters), page-budget reality of the current draft, blind-level conflicts (e.g. an already-public preprint), travel/registration constraints.
• Venue data — venues/conferences/*.yml profiles (schema in venues/schema.yml), surfaced by scripts/list_venues.py.

Process

1. Elicit the inputs. Confirm topic, contribution type, and constraints in one round of questions. The contribution type changes the shortlist more than the topic — pin it down first (references/track-fit.md, "Contribution taxonomy").

2. Survey profiled venues. Run:

   python3 scripts/list_venues.py
   

   This lists every profile with deadline proximity, blind level, submission system, and per-track page budgets, sorted by next upcoming submission deadline. Useful flags: --upcoming-only, --family <name>, --track <name>, --json (full data incl. track notes), --today YYYY-MM-DD (reproducible runs). Never compute date arithmetic by hand. Caveat: some venues (e.g. SIGSPATIAL) have per-track deadlines that differ from the top-level ones — they live in each track's notes field; read them in the --json output.

3. Build the candidate set. Combine (a) profiled venues that plausibly match the topic, and (b) venues you know fit the topic but have no profile yet. For unprofiled candidates, resolve identity via DBLP:

   CONTACT_EMAIL=you@example.org python3 scripts/dblp_venue_lookup.py search "<venue name>"
   

   Optionally gauge venue size with dblp_venue_lookup.py toc-count <dblp_key> <year>. Mark every unprofiled candidate clearly and suggest parse-cfp to profile it. Aim for 5-10 candidate venue-track pairs before scoring.

4. Gather prestige signals. For each candidate, look up CORE rank, h5-index, CCF class (if the user cares), and acceptance rate following references/ranking-sources.md. Hard rule: every number carries a source + edition/year, or is labeled "unverified — indicative" / "unknown". Never state a rank or acceptance rate from memory as fact, and never invent one.

5. Score and rank. Apply the 0-10 rubric in references/track-fit.md (topic fit 0-3, track fit 0-3, prestige fit 0-2, deadline feasibility 0-2), then apply its tie-breakers and red-flag vetoes (embargoes, dual-submission bans, attendance mandates).

6. Re-verify the top picks against live CFPs — mandatory. For the top 3 venues, fetch each profile's cfp_url and confirm the facts the user will act on: deadlines (and their timezone — AoE vs PT matters), page limits and what they exclude, blind level, submission system. Profiles are a starting point, never ground truth; a stale page limit causes a desk reject. Record what was verified and when in the output. If a CFP cannot be reached, mark that venue "NOT re-verified" — do not silently present profile data as confirmed.

7. Deliver the shortlist (format below) and offer next steps: parse-cfp for unprofiled picks, tailor-to-venue once a target is chosen, plan-submission for the timeline.

Output

A ranked shortlist in chat (offer to save as venue-shortlist.md):

Below the table, per venue: 2-4 lines of rationale (the four rubric scores with one-line justifications), the red flags checked, and — for any venue whose deadline has passed — the next expected cycle. Close with the explicit disclaimer that the user must confirm all critical facts on the venue's own CFP page before planning around them.

Bundled resources

• references/ranking-sources.md — CORE, h5-index, acceptance rates, CCF, deadline aggregators: where each lives, how to interpret, how to cite.
• references/track-fit.md — contribution taxonomy, track archetypes, the scoring rubric, tie-breakers, red flags.
• scripts/list_venues.py — stdlib-only profile lister + deadline math.
• scripts/dblp_venue_lookup.py — polite DBLP venue resolver (rate-limited ≤1 req/s, cached under .cache/, needs CONTACT_EMAIL).

Guardrails

• Profiles and this skill's tables are never ground truth — re-verify deadlines, page limits, and policies against the live cfp_url (step 6) and say so in the output.
• Never fabricate ranks, h5-indexes, acceptance rates, or citations; route any bibliographic citation through verify-citations.
• Never submit to, register with, or create accounts on any submission system on the user's behalf.
• Fetch venue/CFP pages on demand and process them transiently; do not copy paper abstracts or full text into the repo (metadata is fine).
• Prestige caps at 2 of 10 points by design — do not let "it's A*" override fit, and do not present prestige as publication advice ("aim lower/higher") unless the user asked for that judgment.