Select Venue

venues/

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.

parse-cfp

tailor-to-venue

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

venue-shortlist.md

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.