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publication-proposal
Build book proposal, sample-chapter positioning, comp-title logic, and public-facing pitch materials.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Build book proposal, sample-chapter positioning, comp-title logic, and public-facing pitch materials.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Audit the book-level callback graph that tracks setup → payoff edges across chapters. Verifies every planted anchor has its declared downstream payoff and every payoff still has its upstream plant. Catches orphaned plants and orphaned payoffs. Reads `book/registries/callback-graph.yml`; runs book-wide when any graph chapter is rewritten. Cousin of fair-clue-audit (which covers chapter-internal recognition).
Build a structured responsibility-laundering case file from a historical, political, legal, corporate, war, or AI event.
Turn approved case files into chapter architecture for serious trade nonfiction.
Diagnose a chapter against the 10 reader-experience values in rule 12 (5 core + 5 craft) and build a defect map naming which values fail, with cited prose evidence, and recommending one of the five treatment classes from rule 08. Output is `process/defect-map/<n>-<slug>.md` plus a treatment-class row appended to `book/registries/treatment-classes.yml`.
Prepare citation-ready notes for drafting, fact-checking, and legal review.
Audit chapter prose for verbose inline `[CITE:]` markers that violate rule-13's slug-only invariant. Inline `[CITE:]` brackets must carry only card slugs (separated by `;` for multi-source claims); full citation apparatus lives in the source-ledger card and is compile-generated as Chicago NB endnote. Catches the failure mode where citation metadata leaks from card to prose. Cousin to `scan-cite-density.py` (the cheap pattern-level pass); this skill is the deeper read.
| name | publication-proposal |
| description | Build book proposal, sample-chapter positioning, comp-title logic, and public-facing pitch materials. |
| version | 1.0.0 |
Usable output:
Weak output:
Proposal components:
Preserve the thesis. Do not flatten the book into partisan outrage, AI panic, or generic accountability commentary.
Context: Blair is positioning the book against three comps for a US trade-nonfiction editor. input: comps=["The Big Short", "On Tyranny", "Weapons of Math Destruction"] output: Returns a positioning matrix — point-of-difference vs. each comp (broader taxonomy than Big Short, less polemical than On Tyranny, more historical depth than WoMD), 3 title/subtitle pairs ranked by directness, 800-word overview, 4-segment audience (general nonfiction readers, policy/governance professionals, journalism programs, AI-governance practitioners), sample-chapter recommendation, query-letter draft, handoff to Blair for audience cross-check. Context: Boundary case: the agent wants a celebrity foreword that would distort the thesis. output: Refuses to alter the thesis for endorsement convenience, proposes two alternative endorsements aligned with the book's evidence discipline, and writes Blair a note about the strategic risk of mis-signalled positioning.