Tailor to Venue

Diff a draft against a target venue's requirements and produce a concrete tailoring plan: what to reposition, what to cut, what to reformat, and what to anonymize — before any edit is made. This skill plans; the user (or a follow-up request) executes the edits.

When to use

• Retargeting a paper (new submission, rejection, or venue switch) to a

different conference, journal, track, or page limit.

• Converting between template families (acmart / IEEEtran / NeurIPS-style /

llncs) or between blind levels (single ↔ double ↔ triple).

• Deciding which track at one venue fits the work best.

Related skills: parse-cfp (build a missing venue profile), select-venue (choose the venue first), preflight-check (final desk-reject lint before submission), prepare-camera-ready (after acceptance).

Inputs

• The draft: main .tex file (the scripts resolve \input/\include);

optionally the compiled PDF and .bib files.

• Target venue profile: venues/conferences/<venue-id>.yml (schema:

venues/schema.yml; family defaults merge automatically from venues/families/).

• Target track name, if the user has chosen one.

Process

1. Resolve the venue profile

Find the profile in venues/conferences/. If none exists, do NOT invent requirements — run the parse-cfp skill against the venue's CFP URL to create one, or proceed with only facts quoted live from the CFP.

2. Re-verify against the live CFP (mandatory)

Profiles go stale and a wrong page limit causes a desk reject. Fetch the profile's cfp_url and re-verify before relying on anything: page limits and exclusions for the chosen track, deadlines and timezone, blind level, template/documentclass invocation, and required sections. Note in the plan what was verified and when; if the live CFP contradicts the profile, the CFP wins — flag the profile for update. If the CFP cannot be fetched, mark every profile-derived fact "UNVERIFIED — confirm on CFP" in the plan.

3. Pick the track with the user

List the profile's tracks with their page limits (the venue diff report includes this table). If the user has not chosen, recommend one based on the work's strongest claim — see [references/contribution-reframing.md](references/contribution-reframing.md) for what each track rewards — and confirm before planning.

4. Run the deterministic diff

Run from the repo root (or pass absolute paths):

python3 skills/tailor-to-venue/scripts/venue_diff.py <main.tex> --venue venues/conferences/<id>.yml --track <Track>

This reports template/option gaps, required-section gaps, abstract length, author-block vs blind level, page-limit context, and the venue facts (submission system, deadlines, LLM policy) to carry into the plan.

Then size the page budget:

python3 skills/tailor-to-venue/scripts/page_budget.py <main.tex> --venue venues/conferences/<id>.yml --track <Track>

And scan anonymization at the venue's blind level (add --pdf <paper.pdf> and --bib <refs.bib> when available):

python3 skills/tailor-to-venue/scripts/anon_sweep.py <main.tex> --venue venues/conferences/<id>.yml

All three print Markdown and exit 2 with a clear message on bad input. Treat script outputs as signals: the compiled PDF and the live CFP are ground truth.

5. Build the four-part plan

Turn the script reports into prose plans using the references:

1. Contribution reframing — retitle, re-abstract, rewrite contribution

bullets and evaluation emphasis for the chosen track: [references/contribution-reframing.md](references/contribution-reframing.md)

1. Page-budget cutting plan — ordered ladder from structural exports to

line-level compression, with estimated savings per step and the forbidden moves listed: [references/page-budget-cutting.md](references/page-budget-cutting.md)

1. Template switch plan — only if venue_diff reports a class/option gap;

per-direction breakage list and bibliography mapping: [references/template-switching.md](references/template-switching.md)

1. Anonymization sweep plan — fix strategy per leak category, plus the

beyond-the-PDF checklist and a camera-ready-restore.md for removed content: [references/anonymization-sweep.md](references/anonymization-sweep.md)

If the draft needs new citations from the target community, route every one through the verify-citations skill — never add an unverified reference.

6. Deliver the plan

Write tailoring-plan-<venue-id>-<track>.md next to the draft with sections:

1. Verification record — what was checked against the live CFP, when, and

any profile contradictions found.

1. Track fit and contribution reframing (with rewritten title/abstract/

contribution-bullet drafts marked as proposals).

1. Page-budget cutting plan (current estimate → target, ordered cuts with

estimated savings, restore list).

1. Template switch plan (or "no switch needed").
2. Anonymization sweep plan (findings with fixes, beyond-the-PDF checklist).
3. Submission logistics — system, URL, deadlines with timezone, rebuttal

format, LLM-policy disclosure needs.

Offer to execute the plan step by step; after edits, recommend preflight-check as the final gate.

Worked example

A double-blind KDD draft was rejected; the user wants to retarget it to SIGSPATIAL's Research track. Both venues use acmart sigconf, so this case exercises three of the four plan parts (no template switch) and shows the de-anonymization direction.

python3 skills/tailor-to-venue/scripts/venue_diff.py paper/main.tex \
    --venue venues/conferences/sigspatial-2026.yml --track Research

Reading the reports against the live SIGSPATIAL CFP, the plan comes out:

• Verification. SIGSPATIAL Research is **single-blind, 10 pages excl.

references + appendix** (KDD was double-blind, 8 pages) — both confirmed on the live CFP on the date of the diff.

• Track fit / reframing. Same Research track, so light reframing: re-aim

vocabulary and baselines at the spatial-data community and add the community-standard baseline KDD reviewers did not expect (references/contribution-reframing.md, "Cross-venue repositioning").

• Page budget. The limit grew 8→10 pages, so this is a de-compression,

not a cut: restore the ablation moved to the appendix for KDD and the examples trimmed for space. page_budget.py confirms headroom.

• Template switch. None — venue_diff reports the class matches.
• Anonymization (reverse sweep). The hard part. The draft is anonymized;

SIGSPATIAL requires author names. Run anon_sweep.py paper/main.tex --venue venues/conferences/sigspatial-2026.yml — at single-blind it flags the leftover "Anonymous Author(s)" placeholder. Restore the real \author/\affiliation/\email block, drop the anonymous/review class options, re-link the real repository, and restore first-person framing. Keep funding out until camera-ready unless the CFP asks for it (references/anonymization-sweep.md, "De-anonymizing").

The deliverable is tailoring-plan-sigspatial-2026-Research.md; no draft file is touched until the user asks to execute a step.

Output

A reviewed, verification-stamped tailoring-plan-<venue-id>-<track>.md, plus the three raw script reports on request. No draft files are modified by this skill unless the user asks for execution of specific plan items.

Guardrails

• Never submit to any submission system on the user's behalf; stop at the plan.
• Never state venue requirements from memory — profile + live-CFP

verification only; unverifiable facts are labeled UNVERIFIED.

• Never fabricate or hand-type citations; new references go through

verify-citations.

• Never plan template tampering (negative \vspace, margin/font tricks) to

meet a page limit — these are desk-reject triggers.

• Do not paste text from other authors' papers into the draft; exemplar

study happens transiently via the study-exemplars skill.