Tech Content Review Panel

A tech/AI/industry deep-analysis piece aimed at an industry readership and built to establish a professional personal brand is easy to miss with a single perspective. This skill provides a fixed eight-role expert panel that reviews a finished draft from multiple angles, giving blunt per-role feedback to push it toward publish-ready quality.

Design pattern: Evaluator-Optimizer — the panel evaluates, you revise per the feedback, then re-check to confirm no new problems before finalizing. It is a generate → evaluate → revise loop.

When to use

Applies to: tech / AI / industry research or in-depth analysis long-form articles aimed at an industry readership (industry deep-dives, sector judgments, research pieces). Does not apply to: news, marketing copy, product docs, tutorials, or short opinion posts — different goals and criteria mean this panel would mismatch. When such content triggers, tell the user this skill does not apply.

Review workflow

Step 1 [Deterministic] Confirm input and applicability

• Confirm there is a finished deep-analysis draft (file path or full text). If none, stop and ask the user to finish a first draft first.
• Judge whether the content type applies (see above). If not, stop and explain.

Step 2 [LLM] G1 Fact & originality check (gate first — reject if it fails)

• Facts: verify every number / company name / date / policy / event. Foundational facts must be verified online with traceable sources. Distinguish confirmed / to-verify / possibly-stale (watch timeliness — do not present old news as new).
• Originality: search (WebSearch) the core argument, framework, and signature phrasing to judge whether it is "independently derived / deepened from public views" (keep, add a clarifying line if needed) or "verbatim-similar and needs rewrite" (plagiarism risk). Every "original / first / exclusive" claim must be verified — never assert originality from memory.

Step 3 [Deterministic] G2 Style red-line scan (reject if not cleared)

• Grep the full text for red-line phrasing (aligned with the user's long-term writing profile negative_rules):
  • "not A but B" and all contrast variants (is X not Y / not…but / not…is / rather than)
  • marketing jargon (empower / closed-loop / end-to-end / powerful / significant / substantial / build)
  • self-aggrandizing / inspirational-influencer tone
  • preacher / instructing tone (you should… / I suggest you… / here's what to do)
  • putting down others' arguments (most analyses… / many articles… / everyone assumes…)
  • writing-process meta-info (one-line wrap-up / follow-up question / this piece will… / conclusion first)
  • explicit commercial intent (researcher posture, no pitching)
• Then read through to confirm no AI tone, no judgment-first, no written deflection.

Step 4 [LLM] R1–R4 Target-reader representatives

• R1 Technical decision-maker: decision layer with a tech background in the industry. Picks on: vague generalities, phenomenon without depth, correct conclusions with no information gain.
• R2 Cross-domain senior expert: understands both the local and the reader's market. Picks on: assumed simplifications, inaccurate technical details, arrogant perspective.
• R3 Investor / strategy analyst: understands business logic but not details. Picks on: hanging judgments without data, logic jumps, absolute conclusions without boundaries.
• R4 Blunt veteran critic: zero tolerance for marketing / AI / influencer tone. Picks on: empty clichés, grandstanding, preacher posture, correct-but-useless platitudes.

Step 5 [LLM] G3 Structure & professional depth assessment

• Against the six depth moves (see references/depth-playbook.md), hit at least 3: ① expose assumed causality ② decompose an overused concept with a layered framework ③ find contradictions within the argued object itself ④ place it in historical context ⑤ give a horizontal reference frame ⑥ expose the boundary of the judgment.
• Check structure: judgment-first, no isomorphic template, has a collectable comparison table / framework.

Step 6 [LLM] T1 Distribution assessment

• T1 Tech media editor: understands platform distribution. Rates title hook (has a hook without losing professionalism), opening retention and search-crawlability, screenshot-shareable memorable points, multi-platform fit (WeChat / LinkedIn / Substack each have their own logic).
• Surface the tension with G2 / R4 (hook vs restraint) explicitly; do not force unification.

Step 7 [LLM] Consolidate and revise

• Grade feedback: must-fix / suggested / optional / for-author-decision (tension items).
• Handle all must-fix and suggested; list options for tension items for the author to decide.

Step 8 [Deterministic] Re-check

• After revision, re-run Step 2 and Step 3 (facts and red-lines must not introduce new problems from the changes; re-grep red-lines to confirm cleared).

Step 9 Finalize

Hard Rules

Cannot be violated.

1. Panelists critique hard, never self-praise — finding problems is more valuable than confirming none.
2. G1 has highest priority — foundational facts and originality must be verified online; do not rely on existing material or memory alone.
3. Red-line clearance is a hard gate — both Step 3 and Step 8 must grep the full text to confirm; cannot finalize until cleared.
4. Professional-vs-distribution tension is not forced into agreement — list options for the author to decide. Principle: a hook must not sacrifice professional credibility, but must not be so professional that no one clicks.

Failure Handling

Output Format

【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)
【G2 Style red-line】pass/reject: specific sentence + line number
【R1】value judgment + what it picked on + pass or not
【R2】【R3】【R4】same as above
【G3 Depth】how many moves hit + what's missing
【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4
【Summary】must-fix N / suggested N / optional N / for-decision N

Notes

Role profiles can be fine-tuned per the specific project's reader composition and writing norms (e.g., align with the user's long-term writing profile). Detailed role definitions and depth moves are in references/depth-playbook.md.

中文摘要

本 Skill 提供固定的八角色专家评审团，在科技/AI/产业深度长文成稿后做多视角会审，逼近可发布质量。设计模式为 Evaluator-Optimizer（评估→修订→复审）。

• 适用：面向行业读者、建专业 IP 的科技/AI/产业研究型或深度分析型长文；不适用新闻、营销、文档、教程、短评。
• 九步流程：①确认输入与适用性 ②G1 事实与原创核查（立论基石须联网核实，原创性须检索验证，不过关打回）③G2 风格红线 grep 扫描（不清零打回）④R1–R4 目标读者代表会审 ⑤G3 结构与纵深评估（六套路至少命中 3）⑥T1 传播评估（钩子 vs 克制张力显性列出）⑦汇总分级修订 ⑧复审重跑 G1/G2 ⑨定稿。
• 硬规则：评审挑得狠不自我表扬；G1 优先级最高；红线清零是硬门槛（Step3/Step8 双 grep）；专业 vs 传播张力不强行统一，列选项由作者拍板。
• 角色画像可按项目读者构成与写作规范微调；详细定义见 references/depth-playbook.md。