SKILL.md
Cover Letter Skill (Academic Submission)
Generate, optimize, align-check, journal-fit-check, and pre-submission-check a submission cover letter using the user's existing LaTeX manuscript as the evidence source. The core differentiating capability is align-check: every claim the letter makes must trace to visible manuscript evidence; generation and optimization plug into that contract by default.
Capability Summary
- Generate a draft from a manuscript .tex (five-segment scaffold; title/abstract/contributions/authors extracted deterministically).
- Optimize an existing draft against tier strategy and the active journal template; return LaTeX-comment diff suggestions, never file edits.
- Align-check letter claims against the manuscript (overclaim, missing evidence, unsupported numeric tokens, AI-disclosure inconsistency between letter and manuscript). Runs by default inside
generateandoptimize. - Journal-fit score on four sub-axes (scope_fit, novelty_framing, evidence_density, format_compliance) → HIGH / MEDIUM / LOW.
- Pre-submission mechanical checks: required declarations, length, opener clichés, banned phrases, AI-tone term frequency, structural AI-trace signals, paragraph shape.
- Unified deterministic CLI (
scripts/cover_letter.py) with--mode generate|optimize|align-check|journal-fit|presubmission; legacy scripts remain supported.
Triggering
Use when the user has a LaTeX manuscript and wants a cover letter generated, an existing letter polished/reviewed, claims verified against the manuscript, a journal-fit assessment, or pre-submission declaration/length/phrasing checks. Prefer this skill over generic prose tools whenever the request mentions "cover letter," "submission letter," "投稿信," or "editor letter" with a paper / journal / conference context.