consulting-writing
Management-consulting writing craft — McKinsey SCR (Situation·Complication·Resolution), Minto Pyramid/MECE, BCG bold-bullet…
Journalism and argumentation writing craft — inverted pyramid, lede, nut graph, kicker, explainer framing, PAGE frames, Toulmin argument (claim/rebuttal/qualifier), Hegelian dialectic, BBC due impartiality. Use when writing or reviewing news/explainer pieces, landscape
Claude Code
$ npx -y skills add alfadur7/llm-wiki-newsroom --skill journalism-writing --agent claude-code
Codex
$ npx skills add alfadur7/llm-wiki-newsroom --skill journalism-writing -a codex -y
Cursor
$ npx skills add alfadur7/llm-wiki-newsroom --skill journalism-writing -a cursor -y
Gemini CLI
$ npx skills add alfadur7/llm-wiki-newsroom --skill journalism-writing -a gemini-cli -y
GitHub Copilot
$ npx skills add alfadur7/llm-wiki-newsroom --skill journalism-writing -a github-copilot -y
OpenCode
$ npx skills add alfadur7/llm-wiki-newsroom --skill journalism-writing -a opencode -y
Windsurf
$ npx skills add alfadur7/llm-wiki-newsroom --skill journalism-writing -a windsurf -y
name: journalism-writing description: Journalism and argumentation writing craft — inverted pyramid, lede, nut graph, kicker, explainer framing, PAGE frames, Toulmin argument (claim/rebuttal/qualifier), Hegelian dialectic, BBC due impartiality. Use when writing or reviewing news/explainer pieces, landscape overviews, or issue analyses that fairly juxtapose opposing views, or when a strong lede, sound argument structure, or balanced conclusion is needed.
Writing craft drawn from news/explanatory journalism and argumentation traditions — narrative lead (Lede→Nut graph→Kicker), dialectical structure (thesis·antithesis·synthesis), argument quality (Toulmin), and fairness (BBC due impartiality). `criteria.json` is the SoT for each criterion's definition, comparator, and source. The shared parsing and wiki-global state that the deterministic checks (judge=A) rely on are injected by the orchestrator (the skill is content-type-agnostic). Examples are illustrative of the target English prose.
Develop an issue as Hegelian thesis → antithesis → synthesis. State thesis and antithesis with explicit **Position A / Position B** bold labels (ko rendering: **A 입장 / B 입장**); add a **C — Mediation** label (ko: **C 중재**) only when a genuine convergence exists. The C paragraph must not run longer than the shorter of A and B, so the convergence is not mistaken for the main clash. If C is not a synthesis but a meta-critique (weakening both sides at once, flagging interest bias), move it out of the dialectic frame — a meta-critique in the C slot breaks the three-part structure.
Synthesis does not pick a winner. In Hegel's terms it *sublates* — cancels and preserves — identifying what each side correctly grasps. Concretely, place each side's **monitoring point** (what one would observe if that side were right) symmetrically (jrn.monitoring-balance); a monitor skewed to one side hides an editorial verdict under hedged wording (combine with BBC due impartiality). e.g. ✅ "If tighter regulation is right, we would observe reduced consumer harm; if looser regulation is right, increased new entry" (winning conditions symmetric on both sides) / ❌ "Regulation blocks innovation, so abolishing it is right" (one-sided verdict).
Check each side's support structure with the Toulmin model (claim · grounds/data · warrant · qualifier · rebuttal · backing).
When aggregating many topics/sides into one piece, keep length and references from skewing to one side — BBC due impartiality is proportionate to weight, not a mechanical 50:50, and privileges no side. The deterministic check signals via a max/min reference-ratio ceiling (default 3.0, see criteria.json), but a hub that is intrinsically more referenced can be normal, so human review accompanies it.
News/explanatory journalism front-loads the point and descends into detail — in the inverted pyramid, "the most important information (or what might even be considered the conclusion) is presented first." These resist deterministic measurement (judge=M, qualitative review); the source techniques shared by author and reviewer:
A multi-agent AI knowledge base run by a five-role "newsroom" — open-source, local-first, no vendor lock-in.
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