Individual skill

journal-cover-letter-skill-trainer

Improve, extend, or benchmark the journal-cover-letter-skill from paired manuscript materials and expert-authored cover letters. Use whenever a user wants to train or iterate the Cover Letter Skill, compare an AI letter with an expert letter, strengthen the Research, Review, or Bibliometrics route, add a new writing route, process a batch of manuscript-letter pairs, control when an expert reference enters model context, or decide whether a candidate Skill version is genuinely better. Do not use merely to write one submission letter.

skills/journal-cover-letter-skill-trainer/SKILL.md

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Last checked: Nov 5, 2026

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Repository stars (not skill ratings): 42
Repository last updated: Oct 1, 2026
License: MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

academic-writing Repository topic
agent-skills Repository topic
codex-plugin Repository topic
cover-letter Repository topic
journal-submission Repository topic
bibliometrics Repository topic
science-mapping Repository topic
skill-development Repository topic
operations Derived category
writing Derived category
research Derived category

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SKILL.mdcore · 15,332 bytes
SKILL.mdcore · 15,332 bytes
SKILL.mdcore · 15,332 bytes
agents/openai.yamlasset · 304 bytes
assets/context-ledger.example.jsonasset · 882 bytes
assets/training-manifest.example.jsonasset · 2,150 bytes
references/blind-evaluation-rubric.mddoc · 3,151 bytes
references/input-and-case-schema.mddoc · 4,164 bytes
references/portability-and-isolation.mddoc · 4,515 bytes
references/promotion-and-versioning.mddoc · 3,234 bytes
references/worker-prompts.mddoc · 3,550 bytes
scripts/evaluate_promotion.pyruns · 4,831 bytes
scripts/prepare_blind_packet.pyruns · 3,162 bytes
scripts/prepare_generator_packet.pyruns · 4,204 bytes
scripts/validate_context_ledger.pyruns · 4,493 bytes
scripts/validate_training_manifest.pyruns · 6,519 bytes

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# Journal Cover Letter Skill Trainer v0.2.0

Improve the `journal-cover-letter-skill` through controlled experiments. Learn transferable editorial reasoning from expert letters without copying their wording, treating them as factual authorities, or overfitting one manuscript.

## Non-negotiable principles

- Treat the manuscript and author-confirmed case materials as the factual source of truth. An expert letter is a benchmark for editorial decisions, not a gold standard.
- Generate the baseline before exposing its generator to the expert letter.
- After the expert is revealed, never reuse that evaluator context for a claimed-blind candidate generation. Start a fresh generator context for every round.
- Change the target route for a stated reason. Do not patch only the current letter.
- A single case may create an experimental candidate, but cannot by itself prove a general improvement.
- Protect every route that was not targeted. A Research improvement must not silently weaken Review or Bibliometrics, and vice versa.
- Preserve the target Skill's name. Version the candidate and release; do not rename the Skill to encode a version.
- Keep private manuscripts and real letters outside public source trees and release packages.