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 cover letter makes must trace to visible evidence in the manuscript. This skill plugs that contract into generation and optimization by default.

Capability Summary

• Generate a cover letter draft from a manuscript .tex source, filling the five-segment scaffold with title, abstract, contributions, authors extracted deterministically.
• Optimize an existing draft against tier strategy and the active journal template; return LaTeX-comment diff suggestions instead of editing the file.
• Align-check claims in the cover letter against the manuscript, flagging overclaim, missing evidence, and unsupported numeric tokens. This runs as a default capability across generate and optimize.
• Journal-fit score the letter 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, paragraph shape.
• Unified deterministic CLI (scripts/cover_letter.py) with --mode generate|optimize|align-check|journal-fit|presubmission; legacy single-purpose scripts remain supported.

Triggering

Use this skill when the user has a LaTeX manuscript and wants: