Lunheng (lunheng-article-pipeline) — a DeepSeek Harness bundle for multi-agent long-form writing

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版本：v19.27.13（DSH bundle：package.json + cordis.patch.yml + lib/index.js）

A DeepSeek Harness (DSH) bundle that registers two on-demand agent skills:

• lunheng-article-pipeline — the main 9-role pipeline (T1–T9, see below).
• lunheng-commands — a thin wrapper exposing 13 /lunheng-* slash commands (draft / resume / cite / audit / journal / ppt / history / rollback / status / stats / gate / abort / help). No new role, no new M-gate item; see skills/lunheng-commands/SKILL.md.

The main skill turns long-form production — academic papers, industry analysis, business commentary, and long-form articles — into a 9-role pipeline with a human in the loop.

What this is

Lunheng is a writing pipeline, not a text generator. It splits a long-form deliverable into 9 independent roles (T1–T9) across 6 phases, orchestrated with DSH subagent calls, and produces output with an evidence base, counter-argument review, independent audit, and human checkpoints.

The nine roles are independent and interchangeable with nothing else: T1 literature scout, T2 data scout, T3 case scout, T4 analyst, T5 writer, T6 critical companion, T7 auditor, T8 finalizer (executed by the coordinator itself), T9 peer reviewer.

When to use it

• You need a long-form piece (over 2000 characters) that has to hold up under scrutiny, and you can wait several hours (the pipeline deliberately gives no hour range — see skills/lunheng-article-pipeline/SKILL.md §边界).
• The topic involves facts, figures, or multiple viewpoints, so it needs an evidence base rather than opinion only.
• You want human checkpoints: confirm the outline before drafting, and review the final draft.

When not to use it

Lunheng actively collects published evidence and integrates evidence you supply. It cannot produce the following on its own; supply the material first, or use another tool:

• First-hand data collection — experiments, surveys, interviews, field work.
• Statistical analysis — it can cite results but does not run SPSS/R/Python.
• Raw chart data collection — it renders data visualizations; scraping, OCR, and speech-to-text need dedicated tools.
• Original images or video — DSH has no built-in text-to-image. Covers fall back to local SVG or supplied files.
• Code execution — the pipeline runs only whitelisted scripts; anything else needs your explicit approval.

Rule of thumb: ask whether the evidence is already published. If yes, Lunheng collects it. If not, supply it first.

What you get

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