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Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布. Skill: tech-content-review-panel Owner: haiyangchenbj Summary: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, d","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. 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Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布.\n\nTags: latest:1.2.1\n\nVersion history:\n\nv1.2.1 | 2026-10-08T04:04:58.404Z | user\n\nAdd title-anchor gate: editor role must verify headline carries a concrete anchor (who/scenario/problem); abstract or filename-style titles are must-fix\n\nv1.2.0 | 2026-09-08T02:39:50.109Z | user\n\nAdd Step R post-draft reader-fit test (R0-R5: positioning lock, role selection, feedback triage, conflict resolution); soften contrast-structure red line to a density-based rule; add Hard Rules #4-8 and Pitfalls section\n\nv1.1.2 | 2026-08-24T06:05:39.887Z | user\n\nAdd not_for routing field (agent-consumption-first design per skill-design-guide Principle Two)\n\nv1.1.1 | 2026-08-07T06:38:59.934Z | auto\n\n- Added a concise Chinese description field (`description_zh`) for improved discoverability and accessibility.\n- Minor documentation structure changes; content and workflow remain unchanged.\n- Added the internal metadata file `_meta.json`; removed obsolete `skill-card.md`.\n\nv1.1.0 | 2026-07-31T08:45:12.037Z | user\n\nEnglish-translate SKILL.md body (was 91% Chinese); add Chinese summary tail; preserve README/skill-card; bump 1.0.0 to 1.1.0\n\nv1.0.0 | 2026-07-15T03:12:05.434Z | user\n\nInitial release: eight-role expert review panel for tech/AI deep-analysis articles\n\nArchive index:\n\nArchive v1.2.1: 6 files, 15215 bytes\n\nFiles: README_zh.md (1834b), README.md (2124b), references/depth-playbook.md (2379b), skill-card.md (2124b), SKILL.md (20331b), _meta.json (144b)\n\nFile v1.2.1:SKILL.md\n\n---\r\nname: tech-content-review-panel\r\ndescription: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布.\r\nversion: \"1.2.1\"\r\nagent_created: true\r\nread_when:\r\n  - \"会审 / 评审 / review this article\"\r\n  - \"多视角挑刺 / 让内容接近完美 / 可发布质量\"\r\n  - \"tech/AI/行业深度稿成稿后的质量把关\"\r\nslug: tech-content-review-panel\r\ndisplayName: Tech Content Review Panel\r\ndescription_zh: \"技术内容评审委员会：发布前用固定八角色专家小组（目标读者代表、质量门禁含事实与原创检查、分发门禁）以先评估后优化循环，评审技术/AI/行业研究深度长文。\"\r\nnot_for:\r\n  - Fact-checking a single claim in isolation (use a claim-audit skill instead)\r\n  - Rewriting or restructuring the article itself (review only; revision guidance is advisory)\r\n  - News, marketing copy, documentation, tutorials, or short opinion posts\r\n  - Reviewing content that has no finished draft yet\r\n---\r\n\r\n# Tech Content Review Panel\r\n\r\nA 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.\r\n\r\n**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.\r\n\r\n## When to use\r\n\r\n**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).\r\n**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.\r\n\r\n## Review workflow\r\n\r\n### Step 1 [Deterministic] Confirm input and applicability\r\n- 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.\r\n- Judge whether the content type applies (see above). If not, stop and explain.\r\n\r\n### Step 2 [LLM] G1 Fact & originality check (gate first — reject if it fails)\r\n- **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).\r\n- **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.\r\n\r\n### Step 3 [Deterministic] G2 Style red-line scan (reject if not cleared)\r\n- Grep the full text for red-line phrasing (aligned with the user's long-term writing profile `negative_rules`):\r\n  - Contrast structures are no longer an absolute zero-tolerance gate. Allow natural, low-density contrast at judgment points (roughly <=3 occurrences per 6,500 Chinese characters), but reject formulaic repetition, contrast used as filler, and any sentence with an AI-bridge pattern. If a contrast sentence can be stated more directly without losing meaning, prefer the direct version. Never replace one contrast pattern with another during revision.\r\n  - For English, screen `not X but Y`, `rather than`, `instead of`, and similar forms for density and rhetorical function; do not mechanically delete natural comparative language. Also screen translated-syntax English (Chinese-to-English calques: nominalized predicate chains, stacked passives, literal idiom translations, sentence rhythm that reads like a translation rather than originally-written English). The author is not a reliable judge of EN native-ness — this check must happen in review, never left to author self-assessment.\r\n  - Register scan for Chinese (2026-09-16 calibration, mirrors tech-writing-pipeline section 4 register discipline): reject colloquial scenario/dialogue sentences (\"没人帮我们画\" / \"你的下一个同事\" — first/second-person conversational staging; subject should be enterprise/org/buyer), reject English-calque nominal predicates (\"组织问题没有供应商\" = \"X has no vendor\" translated literally; rewrite as full subject-predicate clause, e.g. \"没有供应商能替买方回答组织问题\"), reject entertaining hooks and playful endings. Approved baseline: full subject-predicate + abstract verb (翻译/到来/回答) + precise qualification; short sentences allowed, colloquial words not. Rule applies to titles, section headers, and bolded key lines, not just body text.\r\n  - marketing jargon (empower / closed-loop / end-to-end / powerful / significant / substantial / build)\r\n  - self-aggrandizing / inspirational-influencer tone\r\n  - preacher / instructing tone (you should… / I suggest you… / here's what to do)\r\n  - putting down others' arguments (most analyses… / many articles… / everyone assumes…)\r\n  - writing-process meta-info (one-line wrap-up / follow-up question / this piece will… / conclusion first)\r\n  - explicit commercial intent (researcher posture, no pitching)\r\n- Then read through to confirm no AI tone, no judgment-first, no written deflection.\r\n\r\n### Step 4 [LLM] R1–R4 Target-reader representatives\r\n- **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.\r\n- **R2 Cross-domain senior expert**: understands both the local and the reader's market. Picks on: assumed simplifications, inaccurate technical details, arrogant perspective.\r\n- **R3 Investor / strategy analyst**: understands business logic but not details. Picks on: hanging judgments without data, logic jumps, absolute conclusions without boundaries.\r\n- **R4 Blunt veteran critic**: zero tolerance for marketing / AI / influencer tone. Picks on: empty clichés, grandstanding, preacher posture, correct-but-useless platitudes.\r\n\r\n### Step 5 [LLM] G3 Structure & professional depth assessment\r\n- 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.\r\n- Check structure: judgment-first, no isomorphic template, has a collectable comparison table / framework.\r\n\r\n### Step 6 [LLM] T1 Distribution assessment\r\n- **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).\r\n- Surface the tension with G2 / R4 (hook vs restraint) explicitly; do not force unification.\r\n- **Mandatory native-speaker language pass (2026-09-16 calibration, not optional)**: after the distribution assessment, re-read the full draft as a native speaker of the writing language (Chinese draft → native Chinese reader; English draft → native English reader). Flag every word, collocation, sentence rhythm, or metaphor that reads translated, calqued, or unnatural; run the pipeline's calque lexicon and back-translation test here as a review-stage filter. Findings route to **must-fix**, never optional — this pass exists because the author is not a reliable judge of native-ness (EN native-ness is an author blind spot; EN→ZH calques are author zero-tolerance but still slip through). Language verdict reported separately from distribution verdict.\r\n\r\n### Step 7 [LLM] Consolidate and revise\r\n- Grade feedback: must-fix / suggested / optional / for-author-decision (tension items).\r\n- Handle all must-fix and suggested; list options for tension items for the author to decide.\r\n\r\n### Step 8 [Deterministic] Re-check\r\n- 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).\r\n\r\n### Step R [LLM] Post-draft reader-fit test (separate from the eight-role panel)\r\n\r\nRun this after a complete draft exists, preferably after the panel revision and before final release. This is a **reading/comprehension test**, not a ninth review role and not a second eight-role panel.\r\n\r\n#### R0. Lock the article positioning before testing\r\n\r\nRecord five items before collecting reader feedback:\r\n\r\n- article type and platform;\r\n- primary reader and intended reading task;\r\n- thesis / research question;\r\n- deliberate scope and exclusions;\r\n- depth, length and technicality target.\r\n\r\nReader feedback cannot silently rewrite these locked decisions. If the positioning is still undecided, resolve it first; do not let reader reactions decide the article's subject by accident.\r\n\r\n#### R1. Select representative readers, do not always use all roles\r\n\r\nChoose 3–4 roles that match the article. Typical options:\r\n\r\n- **Domain specialist**: tests terminology, mechanism and technical credibility;\r\n- **Runtime / infrastructure specialist**: tests component ownership, state flow, recovery and implementation boundaries;\r\n- **Enterprise decision-maker**: tests whether the judgment helps architecture, procurement, governance or prioritization;\r\n- **Cross-domain industry reader**: tests whether a non-specialist can understand the core message without flattening the technical content.\r\n\r\nSelection is based on the article's positioning. A research report does not automatically need an investor, media editor or general reader; a highly technical piece may prioritize the first two roles and use the fourth only as a comprehension check.\r\n\r\n#### R2. Ask each reader the same five questions\r\n\r\n1. What is the article's central message in one or two sentences?\r\n2. What technical distinction or mechanism did you take away?\r\n3. Which paragraph or term could make you infer the wrong scope or conclusion?\r\n4. What is missing for the article's stated reading task, not for a different article?\r\n5. After reading, would you trust the article's positioning and continue to the evidence?\r\n\r\nEach reader must separate **misunderstanding**, **missing bridge**, **disagreement**, and **different preference**. Do not treat all negative feedback as a defect.\r\n\r\n#### R3. Triage feedback by positioning compatibility\r\n\r\nDo not average all reader opinions or adopt every suggestion. Classify each item:\r\n\r\n- **Must-fix**: the core thesis is misunderstood; a factual/technical statement is misread; scope or subject is wrongly inferred; a necessary transition blocks comprehension; a term is used inconsistently.\r\n- **Suggested**: at least two relevant readers identify the same comprehension barrier, and the fix strengthens the locked reading task without changing thesis, scope or depth target.\r\n- **Optional**: a single reader's preference, a nice-to-have example, an alternate metaphor or a distribution improvement that does not repair misunderstanding.\r\n- **Reject / park**: feedback that pulls the article toward another genre, adds an unsupported framework, demands a product comparison, turns a research report into a media hook, expands the article into a survey, or materially increases length without new evidence.\r\n\r\nPriority order: **factual correctness → positioning integrity → core comprehension → technical depth → elegance → distribution preference**.\r\n\r\n#### R4. Resolve conflicts explicitly\r\n\r\nWhen readers disagree, preserve the article's positioning. Record:\r\n\r\n- which role raised the issue;\r\n- whether it is a comprehension failure or a preference difference;\r\n- whether the proposed change changes the article's subject;\r\n- the chosen action and why;\r\n- what is intentionally not adopted.\r\n\r\nTwo readers agreeing is not sufficient if the suggestion violates the locked scope. One specialist's objection is sufficient to fix a factual or technical error even if other readers did not notice it.\r\n\r\n#### R5. Limit revision scope\r\n\r\nApply must-fixes and compatible suggested changes in one focused pass. Do not keep adding examples, tables, definitions or sections until every role is satisfied. Re-run only the affected reader test after revision; stop when the core message, intended scope and technical level are understood by the selected roles.\r\n\r\n#### Reader-fit output\r\n\r\n```text\r\nPositioning lock: article type / primary reader / reading task / thesis / exclusions / depth target\r\nSelected roles: why these roles fit\r\nRole findings: core message / confusion / wrong inference / missing bridge\r\nConsensus barriers: issues raised by 2+ relevant roles\r\nDecision table: keep / fix / optional / reject-or-park + reason\r\nPositioning drift check: pass / fail\r\nPost-fix targeted re-read: pass / remaining issue\r\n```\r\n\r\n### Step 9 Finalize\r\n\r\nFinalize only after the selected reader-fit test passes its positioning-drift check and all unresolved items are either fixed or explicitly parked.\r\n\r\n## Hard Rules\r\n\r\n> Cannot be violated.\r\n\r\n1. **Panelists critique hard, never self-praise** — finding problems is more valuable than confirming none.\r\n2. **G1 has highest priority** — foundational facts and originality must be verified online; do not rely on existing material or memory alone.\r\n3. **Red-line clearance is a hard gate** — both Step 3 and Step 8 must scan the full text. Marketing jargon, writing-process meta-info, commercial intent, and AI-bridge patterns must be cleared. Contrast structures are reviewed for density and function, not mechanically forced to zero.\r\n4. **Never replace one contrast pattern with another** — when reducing a formulaic contrast sentence, use direct statements or two complete sentences; preserve a natural contrast when it carries the judgment.\r\n5. **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.\r\n6. **Reader-fit feedback cannot override the positioning lock** — the reading test diagnoses misunderstanding; it does not grant every role editorial authority to expand scope, change genre, add unsupported material or flatten technical depth.\r\n7. **Do not average roles or chase unanimity** — fix factual/technical misreadings even if one role reports them; adopt comprehension fixes only when compatible with the intended reading task; park preference conflicts explicitly.\r\n8. **Scope and length are quality constraints** — do not add examples, tables, definitions or sections merely because a reader requested them. Every addition must repair a named comprehension barrier without creating a new center of gravity.\r\n9. **Native-speaker review is a mandatory gate** — T1 must re-read the draft as a native speaker of the writing language and flag translated / calqued / unidiomatic expression; such findings are must-fix. Writing-time discipline (pipeline section 4 register rules) and review-time filtering are two independent defenses, never merged or skipped together.\r\n\r\n## Pitfalls\r\n\r\n- 把阅读测试当成第二轮八角色会审——它只测目标读者能否正确理解，不重新评判所有事实、深度和传播问题。\r\n- 看到普通读者读不懂就自动降技术密度——先判断是必要术语缺少桥梁，还是文章本来就面向专业读者；不为追求人人读懂而改变文章定位。\r\n- 把四个角色的意见全部采纳——不同角色代表不同阅读任务，意见冲突是正常的；先看是否触及核心误解，再看是否符合定位锁。\r\n- 用“多数人都这样反馈”替代判断——两人重复指出也不代表可以扩篇、加新中心或越过事实证据门槛。\r\n- 为满足阅读测试加入未经研究支撑的案例、市场数据、成本模型或产品横评——这属于定位漂移，应拒绝或停放。\r\n- 阅读测试后不断加表、加例子、加定义，导致文章面面俱到却没有一条线讲透——一次集中修订后只复测受影响段落，达到“核心信息、范围、技术层级可正确理解”即停止。\r\n\r\n## Failure Handling\r\n\r\n| Scenario | Handling |\r\n|----------|----------|\r\n| No finished draft (only topic/outline) | Stop, ask to finish first draft before review |\r\n| Content type does not apply (news/marketing/docs etc.) | Stop, explain this skill only reviews deep research pieces |\r\n| Foundational fact cannot be verified | Mark \"to-verify\", reject and ask for evidence or revised judgment; do not pass |\r\n| Core argument collides (plagiarism risk) | Judge independent-derivation vs verbatim-similar; if similar, reject and ask to rewrite that part |\r\n| Revision introduces new red-line phrasing | Step 8 re-check intercepts, revise again |\r\n\r\n## Output Format\r\n\r\n```\r\n【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\r\n【G2 Style red-line】pass/reject: specific sentence + line number\r\n【R1】value judgment + what it picked on + pass or not\r\n【R2】【R3】【R4】same as above\r\n【G3 Depth】how many moves hit + what's missing\r\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\r\n【Summary】must-fix N / suggested N / optional N / for-decision N\r\n```\r\n\r\n## Notes\r\n\r\nRole 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`.\r\n\r\n## 中文摘要\r\n\r\n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）。\r\n\r\n- **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。\r\n- **流程**：八角色会审负责事实、原创、红线、结构、专业度和传播张力；会审修订后另跑一次 **reader-fit 阅读测试**，再定稿。阅读测试先锁文章定位，按文章需要选择 3–4 个代表角色，不默认全选。\r\n- **阅读测试硬规则**：只诊断核心信息是否被正确理解、范围是否被误读和必要桥梁是否缺失；不把所有角色意见一概采用，不为迎合读者改题、扩篇、降技术密度或加入未经研究支撑的内容。\r\n- **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。\r\n- **母语审核强制门（2026-09-16 科里增设）**：T1 编辑角色须以写作语言的母语使用者视角重读全文，过滤翻译腔与不地道表达，命中即 must-fix；与写作阶段语域纪律构成两道独立防线。\r\n- **标题锚点门（2026-10-08 增设）**：T1 编辑角色须单独核标题——必须含具体锚点（谁/场景/问题三类至少一类），抽象短标题、文件名式标题、读者导向空泛标题（如\"关于 X 的思考\"）命中即 must-fix；标题是分发第一入口，不随正文修订自动改善，须单独过一遍。\r\n- 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`。\n\nFile v1.2.1:README.md\n\n# Tech Content Review Panel\n\nAn eight-role expert review panel for tech/AI/industry deep-analysis articles. It critiques a finished draft from multiple angles and pushes it toward publish-ready quality — built for content that aims to establish a professional personal brand with an industry audience.\n\n## What it does\n\nRuns a fixed panel over a finished draft in an **evaluate → revise → re-check** loop:\n\n- **4 target-reader representatives** — a tech decision-maker, a cross-domain senior expert, an investor/strategist, and a blunt veteran. They judge whether the piece is valuable, expert, and free of empty prose.\n- **3 quality gatekeepers** — a fact-and-originality checker (web-verifies foundational facts, searches for idea collisions/plagiarism risk), a style red-line scanner, and a structure-and-depth reviewer.\n- **1 distribution gatekeeper** — a tech media editor who checks title hooks, opening retention, memorable points, and multi-platform fit.\n\n## When to use\n\n**Applies to**: tech / AI / data industry deep-dives, sector judgment articles, research pieces for an industry readership.\n\n**Does not apply to**: news, marketing copy, product docs, tutorials, or short opinion posts.\n\n## How to use\n\nTrigger it after finishing a deep-analysis draft:\n\n> \"Review this article with the panel.\"\n\nAfter the panel revision, run a separate **reader-fit test**. It is not a ninth panelist or a second review round: it tests whether selected target readers correctly understand the core message, scope and technical level. Lock the article positioning first, select 3–4 roles that fit the piece, and triage feedback as must-fix / suggested / optional / reject-or-park. Do not adopt every role's opinion.\n\n## Design\n\n- **Pattern**: Evaluator-Optimizer.\n- **Hard gate**: style red-lines must be grep-clean before finalizing.\n- **Tension handling**: the professional-vs-distribution tension (hook vs restraint) is surfaced explicitly and left for the author to decide, never forced into agreement.\n\nSee `references/depth-playbook.md` for the six depth moves used by the structure reviewer.\n\n## License\n\nMIT\n\nFile v1.2.1:_meta.json\n\n{\n  \"ownerId\": \"kn70yg6zwmkftx4939qrs89awx82rr9a\",\n  \"slug\": \"tech-content-review-panel\",\n  \"version\": \"1.2.1\",\n  \"publishedAt\": 1791432298404\n}\n\nFile v1.2.1:references/depth-playbook.md\n\n# 专业纵深六套路（G3 评估依据）\n\n判断一篇研究/深度文章有没有\"专家级纵深\"、能不能碾压编译稿，对照这六个套路。至少命中 3 个才算有纵深。每个套路附一个来自\"欧洲 AI·开源即主权\"篇的实例。\n\n## ① 点破想当然的因果\n\n找出读者会默认、但其实不成立的因果链，戳破它。\n- 实例：多数人默认\"权重可下载=拿到主权\"。点破：自托管 675B 模型需要 8×H200 节点+运维团队，多数企业最后还是跑在美国云上——权重是自由的，跑权重的底座不是。\n\n## ② 给分层框架拆解被滥用的概念\n\n把一个被当成单一概念、其实被营销滥用的词，拆成清晰的层次。\n- 实例：\"主权\"不是一个开关，拆成数据主权/模型主权/运营主权/算力主权四层，指出开源只给足\"模型主权\"那一层。\n\n## ③ 找出立论对象自身的矛盾\n\n在被分析的对象内部，找一个它自己没解决或自相矛盾的点。这是最显专业的一招。\n- 实例：Mistral 靠开源拿 AI Act 合规豁免，但它的旗舰因训练算力超 10²⁵ FLOPs 落进系统性风险区，恰恰不能享开源豁免——真正吃满豁免的是它的小模型。\n\n## ④ 放进历史脉络\n\n把一个看似新的现象，接到一条已有的历史线索上，显出它不是孤立事件。\n- 实例：Mistral\"开源引流、服务变现\"是 Red Hat/MongoDB/Elastic 走了二十年的开源商业化老路，区别是多打了一张欧洲主权牌。\n\n## ⑤ 给横向参照系\n\n用同类对象的对比，凸显分析对象的独特性。\n- 实例：同样是开源，Meta 为打 OpenAI 商业模式、中国为突破生态围堵、欧洲为主权合规——同一动作三种战略。\n\n## ⑥ 点破判断的边界\n\n给出核心判断后，主动指出它的适用边界和失效条件，拒绝绝对化。\n- 实例：规则主权是防御性的，守得住本地市场但抢不了全球；且压在\"算力差距还没大到不可接受\"的前提上，一旦前沿能力鸿沟拉开，控制权的溢价会失效。\n\n## 使用提示\n\n- 这六招不必全用，但优质深度稿通常命中 3-4 个。\n- ③（自身矛盾）和 ①（想当然因果）最能拉开与编译稿的差距，优先找。\n- 每一招都要落到具体事实和数据，不能空转成\"看似深刻的正确废话\"。\n\nFile v1.2.1:README_zh.md\n\n# 科技内容多角色会审\n\n针对科技/AI/产业深度稿的八角色专家评审团。对成稿从多视角挑刺，逼近可发布质量——面向\"用内容建立专业个人 IP、读者是行业人\"的场景。\n\n## 它做什么\n\n对成稿跑一套固定的评审团，走\"评估 → 修订 → 复审\"循环：\n\n- **4 位目标读者代表** —— 技术决策者、跨域资深专家、投资人/战略分析师、专业毒舌老兵。判断内容有没有价值、够不够专业、有没有空话。\n- **3 位质量守门人** —— 事实与原创核查（联网核实立论基石、检索比对防撞车洗稿）、风格红线扫描、结构与专业纵深评估。\n- **1 位传播守门人** —— 技术媒体编辑，看标题钩子、开头留人、可传播记忆点、多平台适配。\n\n## 何时使用\n\n**适用**：科技/AI/数据产业的深度解读、产业判断、研究稿，面向行业读者。\n\n**不适用**：新闻资讯、营销文案、产品文档、技术教程、纯观点短评。\n\n## 怎么用\n\n在深度稿成稿后触发：\n\n> \"用评审团会审这篇文章。\"\n\n八角色会审完成后，另跑一次 **reader-fit 阅读测试**：它不是第九个角色，也不是第二轮会审，而是测试目标读者能否正确理解文章的核心信息、范围和技术层级。先锁文章定位，再按文章需要选择 3–4 个代表角色；意见分为必改 / 建议改 / 可选 / 拒绝或停放，不能一概采用。\n\n## 设计\n\n- **模式**：Evaluator-Optimizer（评估-优化）。\n- **硬门槛**：定稿前风格红线必须 grep 扫描清零。\n- **张力处理**：专业与传播的张力（钩子 vs 克制）显性列出、交给作者拍板，不强行统一。\n\n结构评审用到的\"六个纵深套路\"见 `references/depth-playbook.md`。\n\n## 许可\n\nMIT\n\nFile v1.2.1:skill-card.md\n\n## Description:\n\nReviews finished tech, AI, and industry deep-analysis articles through an eight-role panel covering facts, originality, style, depth, reader value, and distribution readiness.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[haiyangchenbj](https://clawhub.ai/user/haiyangchenbj)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nWriters and editors use this skill to assess a finished long-form tech or AI analysis for publication, identify must-fix issues, and check whether revisions preserve the intended audience and scope. It is not intended for news, marketing, documentation, tutorials, or unfinished drafts.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Web checks of draft content may disclose unpublished ideas or sensitive material to external search services.\n\nMitigation: Obtain permission before searching unpublished text, and avoid sending confidential passages or distinctive phrasing to external services.\n\nRisk: Strict editorial preferences or incomplete fact and originality searches may lead to misleading publication-readiness judgments.\n\nMitigation: Have the author review cited evidence, unresolved claims, and proposed style changes before publishing.\n\n## Reference(s):\n\n- [Tech Content Review Panel on ClawHub](https://clawhub.ai/haiyangchenbj/skills/tech-content-review-panel)\n- [Depth playbook](references/depth-playbook.md)\n\n## Skill Output:\n\n**Output Type(s):** [Analysis, Guidance]\n\n**Output Format:** [Markdown review with per-role findings and prioritized editorial recommendations]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Includes fact and originality checks, style and depth assessments, distribution feedback, and a separate reader-fit test.]\n\n## Skill Version(s):\n\n1.2.1 (source: frontmatter and server release)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.2.0: 6 files, 13801 bytes\n\nFiles: README_zh.md (1834b), README.md (2124b), references/depth-playbook.md (2379b), skill-card.md (2133b), SKILL.md (17247b), _meta.json (144b)\n\nFile v1.2.0:SKILL.md\n\n---\nname: tech-content-review-panel\ndescription: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布.\nversion: 1.2.0\nagent_created: true\nread_when:\n  - \"会审 / 评审 / review this article\"\n  - \"多视角挑刺 / 让内容接近完美 / 可发布质量\"\n  - \"tech/AI/行业深度稿成稿后的质量把关\"\nslug: tech-content-review-panel\ndisplayName: Tech Content Review Panel\ndescription_zh: \"技术内容评审委员会：发布前用固定八角色专家小组（目标读者代表、质量门禁含事实与原创检查、分发门禁）以先评估后优化循环，评审技术/AI/行业研究深度长文。\"\nnot_for:\n  - Fact-checking a single claim in isolation (use a claim-audit skill instead)\n  - Rewriting or restructuring the article itself (review only; revision guidance is advisory)\n  - News, marketing copy, documentation, tutorials, or short opinion posts\n  - Reviewing content that has no finished draft yet\n---\n\n# Tech Content Review Panel\n\nA 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.\n\n**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.\n\n## When to use\n\n**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).\n**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.\n\n## Review workflow\n\n### Step 1 [Deterministic] Confirm input and applicability\n- 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.\n- Judge whether the content type applies (see above). If not, stop and explain.\n\n### Step 2 [LLM] G1 Fact & originality check (gate first — reject if it fails)\n- **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).\n- **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.\n\n### Step 3 [Deterministic] G2 Style red-line scan (reject if not cleared)\n- Grep the full text for red-line phrasing (aligned with the user's long-term writing profile `negative_rules`):\n  - Contrast structures are no longer an absolute zero-tolerance gate. Allow natural, low-density contrast at judgment points (roughly <=3 occurrences per 6,500 Chinese characters), but reject formulaic repetition, contrast used as filler, and any sentence with an AI-bridge pattern. If a contrast sentence can be stated more directly without losing meaning, prefer the direct version. Never replace one contrast pattern with another during revision.\n  - For English, screen `not X but Y`, `rather than`, `instead of`, and similar forms for density and rhetorical function; do not mechanically delete natural comparative language.\n  - marketing jargon (empower / closed-loop / end-to-end / powerful / significant / substantial / build)\n  - self-aggrandizing / inspirational-influencer tone\n  - preacher / instructing tone (you should… / I suggest you… / here's what to do)\n  - putting down others' arguments (most analyses… / many articles… / everyone assumes…)\n  - writing-process meta-info (one-line wrap-up / follow-up question / this piece will… / conclusion first)\n  - explicit commercial intent (researcher posture, no pitching)\n- Then read through to confirm no AI tone, no judgment-first, no written deflection.\n\n### Step 4 [LLM] R1–R4 Target-reader representatives\n- **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.\n- **R2 Cross-domain senior expert**: understands both the local and the reader's market. Picks on: assumed simplifications, inaccurate technical details, arrogant perspective.\n- **R3 Investor / strategy analyst**: understands business logic but not details. Picks on: hanging judgments without data, logic jumps, absolute conclusions without boundaries.\n- **R4 Blunt veteran critic**: zero tolerance for marketing / AI / influencer tone. Picks on: empty clichés, grandstanding, preacher posture, correct-but-useless platitudes.\n\n### Step 5 [LLM] G3 Structure & professional depth assessment\n- 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.\n- Check structure: judgment-first, no isomorphic template, has a collectable comparison table / framework.\n\n### Step 6 [LLM] T1 Distribution assessment\n- **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).\n- Surface the tension with G2 / R4 (hook vs restraint) explicitly; do not force unification.\n\n### Step 7 [LLM] Consolidate and revise\n- Grade feedback: must-fix / suggested / optional / for-author-decision (tension items).\n- Handle all must-fix and suggested; list options for tension items for the author to decide.\n\n### Step 8 [Deterministic] Re-check\n- 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).\n\n### Step R [LLM] Post-draft reader-fit test (separate from the eight-role panel)\n\nRun this after a complete draft exists, preferably after the panel revision and before final release. This is a **reading/comprehension test**, not a ninth review role and not a second eight-role panel.\n\n#### R0. Lock the article positioning before testing\n\nRecord five items before collecting reader feedback:\n\n- article type and platform;\n- primary reader and intended reading task;\n- thesis / research question;\n- deliberate scope and exclusions;\n- depth, length and technicality target.\n\nReader feedback cannot silently rewrite these locked decisions. If the positioning is still undecided, resolve it first; do not let reader reactions decide the article's subject by accident.\n\n#### R1. Select representative readers, do not always use all roles\n\nChoose 3–4 roles that match the article. Typical options:\n\n- **Domain specialist**: tests terminology, mechanism and technical credibility;\n- **Runtime / infrastructure specialist**: tests component ownership, state flow, recovery and implementation boundaries;\n- **Enterprise decision-maker**: tests whether the judgment helps architecture, procurement, governance or prioritization;\n- **Cross-domain industry reader**: tests whether a non-specialist can understand the core message without flattening the technical content.\n\nSelection is based on the article's positioning. A research report does not automatically need an investor, media editor or general reader; a highly technical piece may prioritize the first two roles and use the fourth only as a comprehension check.\n\n#### R2. Ask each reader the same five questions\n\n1. What is the article's central message in one or two sentences?\n2. What technical distinction or mechanism did you take away?\n3. Which paragraph or term could make you infer the wrong scope or conclusion?\n4. What is missing for the article's stated reading task, not for a different article?\n5. After reading, would you trust the article's positioning and continue to the evidence?\n\nEach reader must separate **misunderstanding**, **missing bridge**, **disagreement**, and **different preference**. Do not treat all negative feedback as a defect.\n\n#### R3. Triage feedback by positioning compatibility\n\nDo not average all reader opinions or adopt every suggestion. Classify each item:\n\n- **Must-fix**: the core thesis is misunderstood; a factual/technical statement is misread; scope or subject is wrongly inferred; a necessary transition blocks comprehension; a term is used inconsistently.\n- **Suggested**: at least two relevant readers identify the same comprehension barrier, and the fix strengthens the locked reading task without changing thesis, scope or depth target.\n- **Optional**: a single reader's preference, a nice-to-have example, an alternate metaphor or a distribution improvement that does not repair misunderstanding.\n- **Reject / park**: feedback that pulls the article toward another genre, adds an unsupported framework, demands a product comparison, turns a research report into a media hook, expands the article into a survey, or materially increases length without new evidence.\n\nPriority order: **factual correctness → positioning integrity → core comprehension → technical depth → elegance → distribution preference**.\n\n#### R4. Resolve conflicts explicitly\n\nWhen readers disagree, preserve the article's positioning. Record:\n\n- which role raised the issue;\n- whether it is a comprehension failure or a preference difference;\n- whether the proposed change changes the article's subject;\n- the chosen action and why;\n- what is intentionally not adopted.\n\nTwo readers agreeing is not sufficient if the suggestion violates the locked scope. One specialist's objection is sufficient to fix a factual or technical error even if other readers did not notice it.\n\n#### R5. Limit revision scope\n\nApply must-fixes and compatible suggested changes in one focused pass. Do not keep adding examples, tables, definitions or sections until every role is satisfied. Re-run only the affected reader test after revision; stop when the core message, intended scope and technical level are understood by the selected roles.\n\n#### Reader-fit output\n\n```text\nPositioning lock: article type / primary reader / reading task / thesis / exclusions / depth target\nSelected roles: why these roles fit\nRole findings: core message / confusion / wrong inference / missing bridge\nConsensus barriers: issues raised by 2+ relevant roles\nDecision table: keep / fix / optional / reject-or-park + reason\nPositioning drift check: pass / fail\nPost-fix targeted re-read: pass / remaining issue\n```\n\n### Step 9 Finalize\n\nFinalize only after the selected reader-fit test passes its positioning-drift check and all unresolved items are either fixed or explicitly parked.\n\n## Hard Rules\n\n> Cannot be violated.\n\n1. **Panelists critique hard, never self-praise** — finding problems is more valuable than confirming none.\n2. **G1 has highest priority** — foundational facts and originality must be verified online; do not rely on existing material or memory alone.\n3. **Red-line clearance is a hard gate** — both Step 3 and Step 8 must scan the full text. Marketing jargon, writing-process meta-info, commercial intent, and AI-bridge patterns must be cleared. Contrast structures are reviewed for density and function, not mechanically forced to zero.\n4. **Never replace one contrast pattern with another** — when reducing a formulaic contrast sentence, use direct statements or two complete sentences; preserve a natural contrast when it carries the judgment.\n5. **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.\n6. **Reader-fit feedback cannot override the positioning lock** — the reading test diagnoses misunderstanding; it does not grant every role editorial authority to expand scope, change genre, add unsupported material or flatten technical depth.\n7. **Do not average roles or chase unanimity** — fix factual/technical misreadings even if one role reports them; adopt comprehension fixes only when compatible with the intended reading task; park preference conflicts explicitly.\n8. **Scope and length are quality constraints** — do not add examples, tables, definitions or sections merely because a reader requested them. Every addition must repair a named comprehension barrier without creating a new center of gravity.\n\n## Pitfalls\n\n- 把阅读测试当成第二轮八角色会审——它只测目标读者能否正确理解，不重新评判所有事实、深度和传播问题。\n- 看到普通读者读不懂就自动降技术密度——先判断是必要术语缺少桥梁，还是文章本来就面向专业读者；不为追求人人读懂而改变文章定位。\n- 把四个角色的意见全部采纳——不同角色代表不同阅读任务，意见冲突是正常的；先看是否触及核心误解，再看是否符合定位锁。\n- 用“多数人都这样反馈”替代判断——两人重复指出也不代表可以扩篇、加新中心或越过事实证据门槛。\n- 为满足阅读测试加入未经研究支撑的案例、市场数据、成本模型或产品横评——这属于定位漂移，应拒绝或停放。\n- 阅读测试后不断加表、加例子、加定义，导致文章面面俱到却没有一条线讲透——一次集中修订后只复测受影响段落，达到“核心信息、范围、技术层级可正确理解”即停止。\n\n## Failure Handling\n\n| Scenario | Handling |\n|----------|----------|\n| No finished draft (only topic/outline) | Stop, ask to finish first draft before review |\n| Content type does not apply (news/marketing/docs etc.) | Stop, explain this skill only reviews deep research pieces |\n| Foundational fact cannot be verified | Mark \"to-verify\", reject and ask for evidence or revised judgment; do not pass |\n| Core argument collides (plagiarism risk) | Judge independent-derivation vs verbatim-similar; if similar, reject and ask to rewrite that part |\n| Revision introduces new red-line phrasing | Step 8 re-check intercepts, revise again |\n\n## Output Format\n\n```\n【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\n【G2 Style red-line】pass/reject: specific sentence + line number\n【R1】value judgment + what it picked on + pass or not\n【R2】【R3】【R4】same as above\n【G3 Depth】how many moves hit + what's missing\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\n【Summary】must-fix N / suggested N / optional N / for-decision N\n```\n\n## Notes\n\nRole 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`.\n\n## 中文摘要\n\n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）。\n\n- **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。\n- **流程**：八角色会审负责事实、原创、红线、结构、专业度和传播张力；会审修订后另跑一次 **reader-fit 阅读测试**，再定稿。阅读测试先锁文章定位，按文章需要选择 3–4 个代表角色，不默认全选。\n- **阅读测试硬规则**：只诊断核心信息是否被正确理解、范围是否被误读和必要桥梁是否缺失；不把所有角色意见一概采用，不为迎合读者改题、扩篇、降技术密度或加入未经研究支撑的内容。\n- **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。\n- 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`。\n\nFile v1.2.0:README.md\n\n# Tech Content Review Panel\n\nAn eight-role expert review panel for tech/AI/industry deep-analysis articles. It critiques a finished draft from multiple angles and pushes it toward publish-ready quality — built for content that aims to establish a professional personal brand with an industry audience.\n\n## What it does\n\nRuns a fixed panel over a finished draft in an **evaluate → revise → re-check** loop:\n\n- **4 target-reader representatives** — a tech decision-maker, a cross-domain senior expert, an investor/strategist, and a blunt veteran. They judge whether the piece is valuable, expert, and free of empty prose.\n- **3 quality gatekeepers** — a fact-and-originality checker (web-verifies foundational facts, searches for idea collisions/plagiarism risk), a style red-line scanner, and a structure-and-depth reviewer.\n- **1 distribution gatekeeper** — a tech media editor who checks title hooks, opening retention, memorable points, and multi-platform fit.\n\n## When to use\n\n**Applies to**: tech / AI / data industry deep-dives, sector judgment articles, research pieces for an industry readership.\n\n**Does not apply to**: news, marketing copy, product docs, tutorials, or short opinion posts.\n\n## How to use\n\nTrigger it after finishing a deep-analysis draft:\n\n> \"Review this article with the panel.\"\n\nAfter the panel revision, run a separate **reader-fit test**. It is not a ninth panelist or a second review round: it tests whether selected target readers correctly understand the core message, scope and technical level. Lock the article positioning first, select 3–4 roles that fit the piece, and triage feedback as must-fix / suggested / optional / reject-or-park. Do not adopt every role's opinion.\n\n## Design\n\n- **Pattern**: Evaluator-Optimizer.\n- **Hard gate**: style red-lines must be grep-clean before finalizing.\n- **Tension handling**: the professional-vs-distribution tension (hook vs restraint) is surfaced explicitly and left for the author to decide, never forced into agreement.\n\nSee `references/depth-playbook.md` for the six depth moves used by the structure reviewer.\n\n## License\n\nMIT\n\nFile v1.2.0:_meta.json\n\n{\n  \"ownerId\": \"kn70yg6zwmkftx4939qrs89awx82rr9a\",\n  \"slug\": \"tech-content-review-panel\",\n  \"version\": \"1.2.0\",\n  \"publishedAt\": 1788835190109\n}\n\nFile v1.2.0:references/depth-playbook.md\n\n# 专业纵深六套路（G3 评估依据）\n\n判断一篇研究/深度文章有没有\"专家级纵深\"、能不能碾压编译稿，对照这六个套路。至少命中 3 个才算有纵深。每个套路附一个来自\"欧洲 AI·开源即主权\"篇的实例。\n\n## ① 点破想当然的因果\n\n找出读者会默认、但其实不成立的因果链，戳破它。\n- 实例：多数人默认\"权重可下载=拿到主权\"。点破：自托管 675B 模型需要 8×H200 节点+运维团队，多数企业最后还是跑在美国云上——权重是自由的，跑权重的底座不是。\n\n## ② 给分层框架拆解被滥用的概念\n\n把一个被当成单一概念、其实被营销滥用的词，拆成清晰的层次。\n- 实例：\"主权\"不是一个开关，拆成数据主权/模型主权/运营主权/算力主权四层，指出开源只给足\"模型主权\"那一层。\n\n## ③ 找出立论对象自身的矛盾\n\n在被分析的对象内部，找一个它自己没解决或自相矛盾的点。这是最显专业的一招。\n- 实例：Mistral 靠开源拿 AI Act 合规豁免，但它的旗舰因训练算力超 10²⁵ FLOPs 落进系统性风险区，恰恰不能享开源豁免——真正吃满豁免的是它的小模型。\n\n## ④ 放进历史脉络\n\n把一个看似新的现象，接到一条已有的历史线索上，显出它不是孤立事件。\n- 实例：Mistral\"开源引流、服务变现\"是 Red Hat/MongoDB/Elastic 走了二十年的开源商业化老路，区别是多打了一张欧洲主权牌。\n\n## ⑤ 给横向参照系\n\n用同类对象的对比，凸显分析对象的独特性。\n- 实例：同样是开源，Meta 为打 OpenAI 商业模式、中国为突破生态围堵、欧洲为主权合规——同一动作三种战略。\n\n## ⑥ 点破判断的边界\n\n给出核心判断后，主动指出它的适用边界和失效条件，拒绝绝对化。\n- 实例：规则主权是防御性的，守得住本地市场但抢不了全球；且压在\"算力差距还没大到不可接受\"的前提上，一旦前沿能力鸿沟拉开，控制权的溢价会失效。\n\n## 使用提示\n\n- 这六招不必全用，但优质深度稿通常命中 3-4 个。\n- ③（自身矛盾）和 ①（想当然因果）最能拉开与编译稿的差距，优先找。\n- 每一招都要落到具体事实和数据，不能空转成\"看似深刻的正确废话\"。\n\nFile v1.2.0:README_zh.md\n\n# 科技内容多角色会审\n\n针对科技/AI/产业深度稿的八角色专家评审团。对成稿从多视角挑刺，逼近可发布质量——面向\"用内容建立专业个人 IP、读者是行业人\"的场景。\n\n## 它做什么\n\n对成稿跑一套固定的评审团，走\"评估 → 修订 → 复审\"循环：\n\n- **4 位目标读者代表** —— 技术决策者、跨域资深专家、投资人/战略分析师、专业毒舌老兵。判断内容有没有价值、够不够专业、有没有空话。\n- **3 位质量守门人** —— 事实与原创核查（联网核实立论基石、检索比对防撞车洗稿）、风格红线扫描、结构与专业纵深评估。\n- **1 位传播守门人** —— 技术媒体编辑，看标题钩子、开头留人、可传播记忆点、多平台适配。\n\n## 何时使用\n\n**适用**：科技/AI/数据产业的深度解读、产业判断、研究稿，面向行业读者。\n\n**不适用**：新闻资讯、营销文案、产品文档、技术教程、纯观点短评。\n\n## 怎么用\n\n在深度稿成稿后触发：\n\n> \"用评审团会审这篇文章。\"\n\n八角色会审完成后，另跑一次 **reader-fit 阅读测试**：它不是第九个角色，也不是第二轮会审，而是测试目标读者能否正确理解文章的核心信息、范围和技术层级。先锁文章定位，再按文章需要选择 3–4 个代表角色；意见分为必改 / 建议改 / 可选 / 拒绝或停放，不能一概采用。\n\n## 设计\n\n- **模式**：Evaluator-Optimizer（评估-优化）。\n- **硬门槛**：定稿前风格红线必须 grep 扫描清零。\n- **张力处理**：专业与传播的张力（钩子 vs 克制）显性列出、交给作者拍板，不强行统一。\n\n结构评审用到的\"六个纵深套路\"见 `references/depth-playbook.md`。\n\n## 许可\n\nMIT\n\nFile v1.2.0:skill-card.md\n\n## Description:\n\nReviews finished tech, AI, and industry deep-analysis drafts with an eight-role expert panel covering fact and originality checks, style red lines, structure, depth, reader fit, and distribution readiness.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[haiyangchenbj](https://clawhub.ai/user/haiyangchenbj)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal authors, editors, and content teams use this skill to review finished long-form tech, AI, or industry analysis drafts before publication. It identifies must-fix and optional issues across factual accuracy, originality, style, structure, professional depth, reader comprehension, and distribution fit.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The workflow may use web searches on draft content for fact verification and originality checks, which can expose confidential unpublished material to external search tools.\n\nMitigation: Use it only with drafts suitable for those checks, or redact sensitive material and perform verification in an approved environment.\n\nRisk: Editorial review findings can be incorrect or overstate factual, originality, or distribution issues.\n\nMitigation: Treat findings as review guidance and confirm important claims against traceable sources before publication.\n\n## Reference(s):\n\n- [Professional Depth Playbook](references/depth-playbook.md)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Guidance]\n\n**Output Format:** [Markdown review report with role-by-role findings and prioritized revision guidance]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include source-backed fact and originality findings plus must-fix, suggested, optional, and author-decision items.]\n\n## Skill Version(s):\n\n1.2.0 (source: frontmatter and server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.1.2: 3 files, 6397 bytes\n\nFiles: skill-card.md (2099b), SKILL.md (9643b), _meta.json (144b)\n\nFile v1.1.2:SKILL.md\n\n---\nslug: tech-content-review-panel\ndisplayName: Tech Content Review Panel\nname: tech-content-review-panel\ndescription: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布.\ndescription_zh: \"技术内容评审委员会：发布前用固定八角色专家小组（目标读者代表、质量门禁含事实与原创检查、分发门禁）以先评估后优化循环，评审技术/AI/行业研究深度长文。\"\nversion: 1.1.2\nagent_created: true\nnot_for:\n  - Fact-checking a single claim in isolation (use a claim-audit skill instead)\n  - Rewriting or restructuring the article itself (review only; revision guidance is advisory)\n  - News, marketing copy, documentation, tutorials, or short opinion posts\n  - Reviewing content that has no finished draft yet\nread_when:\n  - \"会审 / 评审 / review this article\"\n  - \"多视角挑刺 / 让内容接近完美 / 可发布质量\"\n  - \"tech/AI/行业深度稿成稿后的质量把关\"\n---\n\n# Tech Content Review Panel\n\nA 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.\n\n**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.\n\n## When to use\n\n**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).\n**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.\n\n## Review workflow\n\n### Step 1 [Deterministic] Confirm input and applicability\n- 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.\n- Judge whether the content type applies (see above). If not, stop and explain.\n\n### Step 2 [LLM] G1 Fact & originality check (gate first — reject if it fails)\n- **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).\n- **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.\n\n### Step 3 [Deterministic] G2 Style red-line scan (reject if not cleared)\n- Grep the full text for red-line phrasing (aligned with the user's long-term writing profile `negative_rules`):\n  - \"not A but B\" and all contrast variants (is X not Y / not…but / not…is / rather than)\n  - marketing jargon (empower / closed-loop / end-to-end / powerful / significant / substantial / build)\n  - self-aggrandizing / inspirational-influencer tone\n  - preacher / instructing tone (you should… / I suggest you… / here's what to do)\n  - putting down others' arguments (most analyses… / many articles… / everyone assumes…)\n  - writing-process meta-info (one-line wrap-up / follow-up question / this piece will… / conclusion first)\n  - explicit commercial intent (researcher posture, no pitching)\n- Then read through to confirm no AI tone, no judgment-first, no written deflection.\n\n### Step 4 [LLM] R1–R4 Target-reader representatives\n- **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.\n- **R2 Cross-domain senior expert**: understands both the local and the reader's market. Picks on: assumed simplifications, inaccurate technical details, arrogant perspective.\n- **R3 Investor / strategy analyst**: understands business logic but not details. Picks on: hanging judgments without data, logic jumps, absolute conclusions without boundaries.\n- **R4 Blunt veteran critic**: zero tolerance for marketing / AI / influencer tone. Picks on: empty clichés, grandstanding, preacher posture, correct-but-useless platitudes.\n\n### Step 5 [LLM] G3 Structure & professional depth assessment\n- 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.\n- Check structure: judgment-first, no isomorphic template, has a collectable comparison table / framework.\n\n### Step 6 [LLM] T1 Distribution assessment\n- **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).\n- Surface the tension with G2 / R4 (hook vs restraint) explicitly; do not force unification.\n\n### Step 7 [LLM] Consolidate and revise\n- Grade feedback: must-fix / suggested / optional / for-author-decision (tension items).\n- Handle all must-fix and suggested; list options for tension items for the author to decide.\n\n### Step 8 [Deterministic] Re-check\n- 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).\n\n### Step 9 Finalize\n\n## Hard Rules\n\n> Cannot be violated.\n\n1. **Panelists critique hard, never self-praise** — finding problems is more valuable than confirming none.\n2. **G1 has highest priority** — foundational facts and originality must be verified online; do not rely on existing material or memory alone.\n3. **Red-line clearance is a hard gate** — both Step 3 and Step 8 must grep the full text to confirm; cannot finalize until cleared.\n4. **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.\n\n## Failure Handling\n\n| Scenario | Handling |\n|----------|----------|\n| No finished draft (only topic/outline) | Stop, ask to finish first draft before review |\n| Content type does not apply (news/marketing/docs etc.) | Stop, explain this skill only reviews deep research pieces |\n| Foundational fact cannot be verified | Mark \"to-verify\", reject and ask for evidence or revised judgment; do not pass |\n| Core argument collides (plagiarism risk) | Judge independent-derivation vs verbatim-similar; if similar, reject and ask to rewrite that part |\n| Revision introduces new red-line phrasing | Step 8 re-check intercepts, revise again |\n\n## Output Format\n\n```\n【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\n【G2 Style red-line】pass/reject: specific sentence + line number\n【R1】value judgment + what it picked on + pass or not\n【R2】【R3】【R4】same as above\n【G3 Depth】how many moves hit + what's missing\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\n【Summary】must-fix N / suggested N / optional N / for-decision N\n```\n\n## Notes\n\nRole 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`.\n\n## 中文摘要\n\n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）。\n\n- **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。\n- **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。\n- **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。\n- 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`。\n\nFile v1.1.2:_meta.json\n\n{\n  \"ownerId\": \"kn70yg6zwmkftx4939qrs89awx82rr9a\",\n  \"slug\": \"tech-content-review-panel\",\n  \"version\": \"1.1.2\",\n  \"publishedAt\": 1787551539887\n}\n\nFile v1.1.2:skill-card.md\n\n## Description:\n\nReviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel covering target-reader fit, factuality, originality, professional depth, style red lines, and distribution readiness.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[haiyangchenbj](https://clawhub.ai/user/haiyangchenbj)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal authors and content reviewers use this skill to assess finished tech, AI, data, or industry deep-analysis drafts before publication. It helps identify factual, originality, style, depth, reader-value, and distribution issues before the author revises and re-checks the article.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The review depends on web search for fact and originality checks, so incomplete or unavailable source verification can leave claims unresolved.\n\nMitigation: Treat unresolved foundational facts as to-verify and do not pass the draft until evidence or revised wording is supplied.\n\nRisk: The skill references depth-playbook guidance that is not included in the provided artifact, which can make the depth assessment less complete.\n\nMitigation: Supply the missing depth-playbook reference or have the reviewer explicitly document which depth criteria were applied.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [Analysis, Markdown, Guidance]\n\n**Output Format:** [Markdown review report with role-by-role findings and prioritized revision guidance]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires a finished draft; foundational facts and originality claims must be checked with traceable web sources.]\n\n## Skill Version(s):\n\n1.1.2 (source: frontmatter and server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.1.1: 6 files, 10259 bytes\n\nFiles: _meta.json (144b), README_zh.md (1693b), README.md (1982b), references/depth-playbook.md (2379b), skill-card.md (2138b), SKILL.md (9370b)\n\nFile v1.1.1:SKILL.md\n\n---\r\nname: tech-content-review-panel\r\ndescription: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布.\r\ndescription_zh: \"技术内容评审委员会：发布前用固定八角色专家小组（目标读者代表、质量门禁含事实与原创检查、分发门禁）以先评估后优化循环，评审技术/AI/行业研究深度长文。\"\r\nversion: 1.1.1\r\nagent_created: true\r\nread_when:\r\n  - \"会审 / 评审 / review this article\"\r\n  - \"多视角挑刺 / 让内容接近完美 / 可发布质量\"\r\n  - \"tech/AI/行业深度稿成稿后的质量把关\"\r\n---\r\n\r\n# Tech Content Review Panel\r\n\r\nA 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.\r\n\r\n**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.\r\n\r\n## When to use\r\n\r\n**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).\r\n**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.\r\n\r\n## Review workflow\r\n\r\n### Step 1 [Deterministic] Confirm input and applicability\r\n- 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.\r\n- Judge whether the content type applies (see above). If not, stop and explain.\r\n\r\n### Step 2 [LLM] G1 Fact & originality check (gate first — reject if it fails)\r\n- **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).\r\n- **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.\r\n\r\n### Step 3 [Deterministic] G2 Style red-line scan (reject if not cleared)\r\n- Grep the full text for red-line phrasing (aligned with the user's long-term writing profile `negative_rules`):\r\n  - \"not A but B\" and all contrast variants (is X not Y / not…but / not…is / rather than)\r\n  - marketing jargon (empower / closed-loop / end-to-end / powerful / significant / substantial / build)\r\n  - self-aggrandizing / inspirational-influencer tone\r\n  - preacher / instructing tone (you should… / I suggest you… / here's what to do)\r\n  - putting down others' arguments (most analyses… / many articles… / everyone assumes…)\r\n  - writing-process meta-info (one-line wrap-up / follow-up question / this piece will… / conclusion first)\r\n  - explicit commercial intent (researcher posture, no pitching)\r\n- Then read through to confirm no AI tone, no judgment-first, no written deflection.\r\n\r\n### Step 4 [LLM] R1–R4 Target-reader representatives\r\n- **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.\r\n- **R2 Cross-domain senior expert**: understands both the local and the reader's market. Picks on: assumed simplifications, inaccurate technical details, arrogant perspective.\r\n- **R3 Investor / strategy analyst**: understands business logic but not details. Picks on: hanging judgments without data, logic jumps, absolute conclusions without boundaries.\r\n- **R4 Blunt veteran critic**: zero tolerance for marketing / AI / influencer tone. Picks on: empty clichés, grandstanding, preacher posture, correct-but-useless platitudes.\r\n\r\n### Step 5 [LLM] G3 Structure & professional depth assessment\r\n- 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.\r\n- Check structure: judgment-first, no isomorphic template, has a collectable comparison table / framework.\r\n\r\n### Step 6 [LLM] T1 Distribution assessment\r\n- **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).\r\n- Surface the tension with G2 / R4 (hook vs restraint) explicitly; do not force unification.\r\n\r\n### Step 7 [LLM] Consolidate and revise\r\n- Grade feedback: must-fix / suggested / optional / for-author-decision (tension items).\r\n- Handle all must-fix and suggested; list options for tension items for the author to decide.\r\n\r\n### Step 8 [Deterministic] Re-check\r\n- 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).\r\n\r\n### Step 9 Finalize\r\n\r\n## Hard Rules\r\n\r\n> Cannot be violated.\r\n\r\n1. **Panelists critique hard, never self-praise** — finding problems is more valuable than confirming none.\r\n2. **G1 has highest priority** — foundational facts and originality must be verified online; do not rely on existing material or memory alone.\r\n3. **Red-line clearance is a hard gate** — both Step 3 and Step 8 must grep the full text to confirm; cannot finalize until cleared.\r\n4. **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.\r\n\r\n## Failure Handling\r\n\r\n| Scenario | Handling |\r\n|----------|----------|\r\n| No finished draft (only topic/outline) | Stop, ask to finish first draft before review |\r\n| Content type does not apply (news/marketing/docs etc.) | Stop, explain this skill only reviews deep research pieces |\r\n| Foundational fact cannot be verified | Mark \"to-verify\", reject and ask for evidence or revised judgment; do not pass |\r\n| Core argument collides (plagiarism risk) | Judge independent-derivation vs verbatim-similar; if similar, reject and ask to rewrite that part |\r\n| Revision introduces new red-line phrasing | Step 8 re-check intercepts, revise again |\r\n\r\n## Output Format\r\n\r\n```\r\n【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\r\n【G2 Style red-line】pass/reject: specific sentence + line number\r\n【R1】value judgment + what it picked on + pass or not\r\n【R2】【R3】【R4】same as above\r\n【G3 Depth】how many moves hit + what's missing\r\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\r\n【Summary】must-fix N / suggested N / optional N / for-decision N\r\n```\r\n\r\n## Notes\r\n\r\nRole 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`.\r\n\r\n## 中文摘要\r\n\r\n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）。\r\n\r\n- **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。\r\n- **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。\r\n- **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。\r\n- 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`。\n\nFile v1.1.1:README.md\n\n# Tech Content Review Panel\n\nAn eight-role expert review panel for tech/AI/industry deep-analysis articles. It critiques a finished draft from multiple angles and pushes it toward publish-ready quality — built for content that aims to establish a professional personal brand with an industry audience.\n\n## What it does\n\nRuns a fixed panel over a finished draft in an **evaluate → revise → re-check** loop:\n\n- **4 target-reader representatives** — a tech decision-maker, a cross-domain senior expert, an investor/strategist, and a blunt veteran. They judge whether the piece is valuable, expert, and free of empty prose.\n- **3 quality gatekeepers** — a fact-and-originality checker (web-verifies foundational facts, searches for idea collisions/plagiarism risk), a style red-line scanner, and a structure-and-depth reviewer.\n- **1 distribution gatekeeper** — a tech media editor who checks title hooks, opening retention, memorable points, and multi-platform fit.\n\n## When to use\n\n**Applies to**: tech / AI / data industry deep-dives, sector judgment articles, research pieces for an industry readership.\n\n**Does not apply to**: news, marketing copy, product docs, tutorials, or short opinion posts.\n\n## How to use\n\nTrigger it after finishing a deep-analysis draft:\n\n> \"Review this article with the panel.\"\n\nThe panel runs the nine-step workflow (fact & originality check → red-line scan → reader reps → depth → distribution → consolidate → re-check → finalize) and outputs per-role feedback graded as must-fix / suggested / optional / for-author-decision.\n\n## Design\n\n- **Pattern**: Evaluator-Optimizer.\n- **Hard gate**: style red-lines must be grep-clean before finalizing.\n- **Tension handling**: the professional-vs-distribution tension (hook vs restraint) is surfaced explicitly and left for the author to decide, never forced into agreement.\n\nSee `references/depth-playbook.md` for the six depth moves used by the structure reviewer.\n\n## License\n\nMIT\n\nFile v1.1.1:_meta.json\n\n{\n  \"ownerId\": \"kn70yg6zwmkftx4939qrs89awx82rr9a\",\n  \"slug\": \"tech-content-review-panel\",\n  \"version\": \"1.1.1\",\n  \"publishedAt\": 1786084739934\n}\n\nFile v1.1.1:references/depth-playbook.md\n\n# 专业纵深六套路（G3 评估依据）\n\n判断一篇研究/深度文章有没有\"专家级纵深\"、能不能碾压编译稿，对照这六个套路。至少命中 3 个才算有纵深。每个套路附一个来自\"欧洲 AI·开源即主权\"篇的实例。\n\n## ① 点破想当然的因果\n\n找出读者会默认、但其实不成立的因果链，戳破它。\n- 实例：多数人默认\"权重可下载=拿到主权\"。点破：自托管 675B 模型需要 8×H200 节点+运维团队，多数企业最后还是跑在美国云上——权重是自由的，跑权重的底座不是。\n\n## ② 给分层框架拆解被滥用的概念\n\n把一个被当成单一概念、其实被营销滥用的词，拆成清晰的层次。\n- 实例：\"主权\"不是一个开关，拆成数据主权/模型主权/运营主权/算力主权四层，指出开源只给足\"模型主权\"那一层。\n\n## ③ 找出立论对象自身的矛盾\n\n在被分析的对象内部，找一个它自己没解决或自相矛盾的点。这是最显专业的一招。\n- 实例：Mistral 靠开源拿 AI Act 合规豁免，但它的旗舰因训练算力超 10²⁵ FLOPs 落进系统性风险区，恰恰不能享开源豁免——真正吃满豁免的是它的小模型。\n\n## ④ 放进历史脉络\n\n把一个看似新的现象，接到一条已有的历史线索上，显出它不是孤立事件。\n- 实例：Mistral\"开源引流、服务变现\"是 Red Hat/MongoDB/Elastic 走了二十年的开源商业化老路，区别是多打了一张欧洲主权牌。\n\n## ⑤ 给横向参照系\n\n用同类对象的对比，凸显分析对象的独特性。\n- 实例：同样是开源，Meta 为打 OpenAI 商业模式、中国为突破生态围堵、欧洲为主权合规——同一动作三种战略。\n\n## ⑥ 点破判断的边界\n\n给出核心判断后，主动指出它的适用边界和失效条件，拒绝绝对化。\n- 实例：规则主权是防御性的，守得住本地市场但抢不了全球；且压在\"算力差距还没大到不可接受\"的前提上，一旦前沿能力鸿沟拉开，控制权的溢价会失效。\n\n## 使用提示\n\n- 这六招不必全用，但优质深度稿通常命中 3-4 个。\n- ③（自身矛盾）和 ①（想当然因果）最能拉开与编译稿的差距，优先找。\n- 每一招都要落到具体事实和数据，不能空转成\"看似深刻的正确废话\"。\n\nFile v1.1.1:README_zh.md\n\n# 科技内容多角色会审\n\n针对科技/AI/产业深度稿的八角色专家评审团。对成稿从多视角挑刺，逼近可发布质量——面向\"用内容建立专业个人 IP、读者是行业人\"的场景。\n\n## 它做什么\n\n对成稿跑一套固定的评审团，走\"评估 → 修订 → 复审\"循环：\n\n- **4 位目标读者代表** —— 技术决策者、跨域资深专家、投资人/战略分析师、专业毒舌老兵。判断内容有没有价值、够不够专业、有没有空话。\n- **3 位质量守门人** —— 事实与原创核查（联网核实立论基石、检索比对防撞车洗稿）、风格红线扫描、结构与专业纵深评估。\n- **1 位传播守门人** —— 技术媒体编辑，看标题钩子、开头留人、可传播记忆点、多平台适配。\n\n## 何时使用\n\n**适用**：科技/AI/数据产业的深度解读、产业判断、研究稿，面向行业读者。\n\n**不适用**：新闻资讯、营销文案、产品文档、技术教程、纯观点短评。\n\n## 怎么用\n\n在深度稿成稿后触发：\n\n> \"用评审团会审这篇文章。\"\n\n评审团跑九步流程（事实与原创核查 → 红线扫描 → 读者代表 → 纵深 → 传播 → 汇总 → 复审 → 定稿），按角色输出意见，分级为必改 / 建议改 / 可选 / 待作者拍板。\n\n## 设计\n\n- **模式**：Evaluator-Optimizer（评估-优化）。\n- **硬门槛**：定稿前风格红线必须 grep 扫描清零。\n- **张力处理**：专业与传播的张力（钩子 vs 克制）显性列出、交给作者拍板，不强行统一。\n\n结构评审用到的\"六个纵深套路\"见 `references/depth-playbook.md`。\n\n## 许可\n\nMIT\n\nFile v1.1.1:skill-card.md\n\n## Description:\n\nReviews finished long-form tech, AI, and industry analysis before publication using a fixed eight-role expert panel with fact, originality, style, depth, reader, and distribution checks.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[haiyangchenbj](https://clawhub.ai/user/haiyangchenbj)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nAuthors, editors, and content strategists use this skill to review finished tech, AI, and industry deep-analysis drafts before publication, then revise must-fix and suggested issues before finalizing.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Using the skill on confidential drafts can expose facts, arguments, or distinctive phrasing through web searches required for fact and originality checks.\n\nMitigation: Use it only on drafts whose contents can be included in search queries, or remove confidential details before review.\n\nRisk: The workflow rejects content outside long-form tech, AI, or industry analysis, so it can give mismatched guidance for news, marketing copy, documentation, tutorials, or short opinion posts.\n\nMitigation: Confirm the draft type before invoking the panel and use a purpose-built review process for unsupported content.\n\n## Reference(s):\n\n- [Professional Depth Playbook](references/depth-playbook.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance]\n\n**Output Format:** [Markdown review report with per-gate and per-role findings graded as must-fix, suggested, optional, or for author decision.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Includes pass or reject gates for fact and originality checks, style red-line scanning, depth assessment, distribution assessment, and re-check guidance.]\n\n## Skill Version(s):\n\n1.1.1 (source: frontmatter and server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.1.0: 6 files, 10301 bytes\n\nFiles: README_zh.md (1693b), README.md (1982b), references/depth-playbook.md (2379b), skill-card.md (2663b), SKILL.md (9021b), _meta.json (144b)\n\nFile v1.1.0:SKILL.md\n\n---\nname: tech-content-review-panel\ndescription: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布.\nversion: 1.1.0\nagent_created: true\nread_when:\n  - \"会审 / 评审 / review this article\"\n  - \"多视角挑刺 / 让内容接近完美 / 可发布质量\"\n  - \"tech/AI/行业深度稿成稿后的质量把关\"\n---\n\n# Tech Content Review Panel\n\nA 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.\n\n**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.\n\n## When to use\n\n**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).\n**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.\n\n## Review workflow\n\n### Step 1 [Deterministic] Confirm input and applicability\n- 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.\n- Judge whether the content type applies (see above). If not, stop and explain.\n\n### Step 2 [LLM] G1 Fact & originality check (gate first — reject if it fails)\n- **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).\n- **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.\n\n### Step 3 [Deterministic] G2 Style red-line scan (reject if not cleared)\n- Grep the full text for red-line phrasing (aligned with the user's long-term writing profile `negative_rules`):\n  - \"not A but B\" and all contrast variants (is X not Y / not…but / not…is / rather than)\n  - marketing jargon (empower / closed-loop / end-to-end / powerful / significant / substantial / build)\n  - self-aggrandizing / inspirational-influencer tone\n  - preacher / instructing tone (you should… / I suggest you… / here's what to do)\n  - putting down others' arguments (most analyses… / many articles… / everyone assumes…)\n  - writing-process meta-info (one-line wrap-up / follow-up question / this piece will… / conclusion first)\n  - explicit commercial intent (researcher posture, no pitching)\n- Then read through to confirm no AI tone, no judgment-first, no written deflection.\n\n### Step 4 [LLM] R1–R4 Target-reader representatives\n- **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.\n- **R2 Cross-domain senior expert**: understands both the local and the reader's market. Picks on: assumed simplifications, inaccurate technical details, arrogant perspective.\n- **R3 Investor / strategy analyst**: understands business logic but not details. Picks on: hanging judgments without data, logic jumps, absolute conclusions without boundaries.\n- **R4 Blunt veteran critic**: zero tolerance for marketing / AI / influencer tone. Picks on: empty clichés, grandstanding, preacher posture, correct-but-useless platitudes.\n\n### Step 5 [LLM] G3 Structure & professional depth assessment\n- 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.\n- Check structure: judgment-first, no isomorphic template, has a collectable comparison table / framework.\n\n### Step 6 [LLM] T1 Distribution assessment\n- **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).\n- Surface the tension with G2 / R4 (hook vs restraint) explicitly; do not force unification.\n\n### Step 7 [LLM] Consolidate and revise\n- Grade feedback: must-fix / suggested / optional / for-author-decision (tension items).\n- Handle all must-fix and suggested; list options for tension items for the author to decide.\n\n### Step 8 [Deterministic] Re-check\n- 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).\n\n### Step 9 Finalize\n\n## Hard Rules\n\n> Cannot be violated.\n\n1. **Panelists critique hard, never self-praise** — finding problems is more valuable than confirming none.\n2. **G1 has highest priority** — foundational facts and originality must be verified online; do not rely on existing material or memory alone.\n3. **Red-line clearance is a hard gate** — both Step 3 and Step 8 must grep the full text to confirm; cannot finalize until cleared.\n4. **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.\n\n## Failure Handling\n\n| Scenario | Handling |\n|----------|----------|\n| No finished draft (only topic/outline) | Stop, ask to finish first draft before review |\n| Content type does not apply (news/marketing/docs etc.) | Stop, explain this skill only reviews deep research pieces |\n| Foundational fact cannot be verified | Mark \"to-verify\", reject and ask for evidence or revised judgment; do not pass |\n| Core argument collides (plagiarism risk) | Judge independent-derivation vs verbatim-similar; if similar, reject and ask to rewrite that part |\n| Revision introduces new red-line phrasing | Step 8 re-check intercepts, revise again |\n\n## Output Format\n\n```\n【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\n【G2 Style red-line】pass/reject: specific sentence + line number\n【R1】value judgment + what it picked on + pass or not\n【R2】【R3】【R4】same as above\n【G3 Depth】how many moves hit + what's missing\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\n【Summary】must-fix N / suggested N / optional N / for-decision N\n```\n\n## Notes\n\nRole 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`.\n\n## 中文摘要\n\n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）。\n\n- **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。\n- **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。\n- **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。\n- 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`。\n\nFile v1.1.0:README.md\n\n# Tech Content Review Panel\n\nAn eight-role expert review panel for tech/AI/industry deep-analysis articles. It critiques a finished draft from multiple angles and pushes it toward publish-ready quality — built for content that aims to establish a professional personal brand with an industry audience.\n\n## What it does\n\nRuns a fixed panel over a finished draft in an **evaluate → revise → re-check** loop:\n\n- **4 target-reader representatives** — a tech decision-maker, a cross-domain senior expert, an investor/strategist, and a blunt veteran. They judge whether the piece is valuable, expert, and free of empty prose.\n- **3 quality gatekeepers** — a fact-and-originality checker (web-verifies foundational facts, searches for idea collisions/plagiarism risk), a style red-line scanner, and a structure-and-depth reviewer.\n- **1 distribution gatekeeper** — a tech media editor who checks title hooks, opening retention, memorable points, and multi-platform fit.\n\n## When to use\n\n**Applies to**: tech / AI / data industry deep-dives, sector judgment articles, research pieces for an industry readership.\n\n**Does not apply to**: news, marketing copy, product docs, tutorials, or short opinion posts.\n\n## How to use\n\nTrigger it after finishing a deep-analysis draft:\n\n> \"Review this article with the panel.\"\n\nThe panel runs the nine-step workflow (fact & originality check → red-line scan → reader reps → depth → distribution → consolidate → re-check → finalize) and outputs per-role feedback graded as must-fix / suggested / optional / for-author-decision.\n\n## Design\n\n- **Pattern**: Evaluator-Optimizer.\n- **Hard gate**: style red-lines must be grep-clean before finalizing.\n- **Tension handling**: the professional-vs-distribution tension (hook vs restraint) is surfaced explicitly and left for the author to decide, never forced into agreement.\n\nSee `references/depth-playbook.md` for the six depth moves used by the structure reviewer.\n\n## License\n\nMIT\n\nFile v1.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn70yg6zwmkftx4939qrs89awx82rr9a\",\n  \"slug\": \"tech-content-review-panel\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1785487512037\n}\n\nFile v1.1.0:references/depth-playbook.md\n\n# 专业纵深六套路（G3 评估依据）\n\n判断一篇研究/深度文章有没有\"专家级纵深\"、能不能碾压编译稿，对照这六个套路。至少命中 3 个才算有纵深。每个套路附一个来自\"欧洲 AI·开源即主权\"篇的实例。\n\n## ① 点破想当然的因果\n\n找出读者会默认、但其实不成立的因果链，戳破它。\n- 实例：多数人默认\"权重可下载=拿到主权\"。点破：自托管 675B 模型需要 8×H200 节点+运维团队，多数企业最后还是跑在美国云上——权重是自由的，跑权重的底座不是。\n\n## ② 给分层框架拆解被滥用的概念\n\n把一个被当成单一概念、其实被营销滥用的词，拆成清晰的层次。\n- 实例：\"主权\"不是一个开关，拆成数据主权/模型主权/运营主权/算力主权四层，指出开源只给足\"模型主权\"那一层。\n\n## ③ 找出立论对象自身的矛盾\n\n在被分析的对象内部，找一个它自己没解决或自相矛盾的点。这是最显专业的一招。\n- 实例：Mistral 靠开源拿 AI Act 合规豁免，但它的旗舰因训练算力超 10²⁵ FLOPs 落进系统性风险区，恰恰不能享开源豁免——真正吃满豁免的是它的小模型。\n\n## ④ 放进历史脉络\n\n把一个看似新的现象，接到一条已有的历史线索上，显出它不是孤立事件。\n- 实例：Mistral\"开源引流、服务变现\"是 Red Hat/MongoDB/Elastic 走了二十年的开源商业化老路，区别是多打了一张欧洲主权牌。\n\n## ⑤ 给横向参照系\n\n用同类对象的对比，凸显分析对象的独特性。\n- 实例：同样是开源，Meta 为打 OpenAI 商业模式、中国为突破生态围堵、欧洲为主权合规——同一动作三种战略。\n\n## ⑥ 点破判断的边界\n\n给出核心判断后，主动指出它的适用边界和失效条件，拒绝绝对化。\n- 实例：规则主权是防御性的，守得住本地市场但抢不了全球；且压在\"算力差距还没大到不可接受\"的前提上，一旦前沿能力鸿沟拉开，控制权的溢价会失效。\n\n## 使用提示\n\n- 这六招不必全用，但优质深度稿通常命中 3-4 个。\n- ③（自身矛盾）和 ①（想当然因果）最能拉开与编译稿的差距，优先找。\n- 每一招都要落到具体事实和数据，不能空转成\"看似深刻的正确废话\"。\n\nFile v1.1.0:README_zh.md\n\n# 科技内容多角色会审\n\n针对科技/AI/产业深度稿的八角色专家评审团。对成稿从多视角挑刺，逼近可发布质量——面向\"用内容建立专业个人 IP、读者是行业人\"的场景。\n\n## 它做什么\n\n对成稿跑一套固定的评审团，走\"评估 → 修订 → 复审\"循环：\n\n- **4 位目标读者代表** —— 技术决策者、跨域资深专家、投资人/战略分析师、专业毒舌老兵。判断内容有没有价值、够不够专业、有没有空话。\n- **3 位质量守门人** —— 事实与原创核查（联网核实立论基石、检索比对防撞车洗稿）、风格红线扫描、结构与专业纵深评估。\n- **1 位传播守门人** —— 技术媒体编辑，看标题钩子、开头留人、可传播记忆点、多平台适配。\n\n## 何时使用\n\n**适用**：科技/AI/数据产业的深度解读、产业判断、研究稿，面向行业读者。\n\n**不适用**：新闻资讯、营销文案、产品文档、技术教程、纯观点短评。\n\n## 怎么用\n\n在深度稿成稿后触发：\n\n> \"用评审团会审这篇文章。\"\n\n评审团跑九步流程（事实与原创核查 → 红线扫描 → 读者代表 → 纵深 → 传播 → 汇总 → 复审 → 定稿），按角色输出意见，分级为必改 / 建议改 / 可选 / 待作者拍板。\n\n## 设计\n\n- **模式**：Evaluator-Optimizer（评估-优化）。\n- **硬门槛**：定稿前风格红线必须 grep 扫描清零。\n- **张力处理**：专业与传播的张力（钩子 vs 克制）显性列出、交给作者拍板，不强行统一。\n\n结构评审用到的\"六个纵深套路\"见 `references/depth-playbook.md`。\n\n## 许可\n\nMIT\n\nFile v1.1.0:skill-card.md\n\n## Description: <br>\nReviews finished tech, AI, or industry deep-analysis drafts with an eight-role expert panel that checks facts, originality, style red lines, structure, professional depth, and distribution fit before publication. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[haiyangchenbj](https://clawhub.ai/user/haiyangchenbj) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nExternal authors, editors, and content strategists use this skill to review finished tech, AI, or industry research drafts before publication. It provides fact and originality checks, reader-role critique, professional-depth assessment, and distribution feedback in an evaluate-revise-recheck loop. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Fact claims in a draft may be stale, unverifiable, or unsupported. <br>\nMitigation: Verify foundational numbers, company names, dates, policies, and events against traceable online sources; mark unresolved claims as to-verify and do not pass the draft until they are resolved. <br>\nRisk: Originality checks may send draft arguments or distinctive phrasing through web search, which can be inappropriate for confidential drafts. <br>\nMitigation: Use the skill only for drafts that are suitable for web-assisted review, or remove confidential details before running fact and originality checks. <br>\nRisk: Distribution recommendations may conflict with professional restraint and can over-optimize for hooks. <br>\nMitigation: Surface hook-versus-credibility tradeoffs as explicit author-decision items instead of forcing a single recommendation. <br>\n\n\n## Reference(s): <br>\n- [Professional Depth Playbook](references/depth-playbook.md) <br>\n- [ClawHub skill page](https://clawhub.ai/haiyangchenbj/skills/tech-content-review-panel) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Text, Markdown, Guidance] <br>\n**Output Format:** [Markdown-style structured review with pass or reject gates, per-role critique, and prioritized revision items] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Includes must-fix, suggested, optional, and author-decision categories; may include source-backed fact and originality findings.] <br>\n\n## Skill Version(s): <br>\n1.1.0 (source: frontmatter and server release evidence) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.0.0: 6 files, 9528 bytes\n\nFiles: README_zh.md (1693b), README.md (1982b), references/depth-playbook.md (2990b), skill-card.md (2416b), SKILL.md (7632b), _meta.json (144b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: tech-content-review-panel\ndescription: Reviews a tech, AI, or industry research/in-depth analysis long-form article before publishing, using a fixed eight-role expert panel in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces, not news, marketing, docs, tutorials, or short opinion posts. Trigger when a user asks to review a finished deep-analysis draft or wants tech content to reach publish-ready quality.\nversion: 1.0.0\nmetadata:\n  openclaw:\n    tags:\n      - content-review\n      - editorial\n      - tech-writing\n      - quality-assurance\n      - fact-checking\n      - deep-analysis\n      - thought-leadership\n    requires:\n      bins:\n        - none\nread_when:\n  - review this article\n  - multi-perspective critique\n  - make content publish-ready\n  - editorial review for deep-analysis draft\n---\n\n# Tech Content Review Panel\n\nA single reviewer misses things. This skill runs a fixed **eight-role expert review panel** over a finished tech/AI/industry deep-analysis draft, gives blunt per-role feedback, and pushes the draft toward publish-ready quality — especially for content meant to build a professional personal brand for an industry audience.\n\n**Design pattern: Evaluator-Optimizer.** The panel evaluates, the draft is revised, a re-check confirms no new issues, then it ships. A generate → evaluate → revise loop.\n\n## When to use\n\n**Applies to**: tech / AI / data industry deep-dives, sector judgment articles, and research pieces aimed at an industry readership.\n**Does not apply to**: news, marketing copy, product docs, tutorials, or short opinion posts — their goals and criteria differ, and this panel would misfire. If triggered on such content, stop and tell the user the skill does not apply.\n\n## Workflow\n\n### Step 1 [Deterministic] Confirm input and applicability\n- Confirm a finished draft exists (file path or full text). If only a topic or outline exists, stop and ask for a complete draft first.\n- Confirm the content form is in scope (see above). If not, stop and explain.\n\n### Step 2 [LLM] G1 fact and originality check (must pass first)\n- **Facts**: verify every number, company name, date, policy, and event. Foundational claims must be verified against a reliable source (use web search). Mark items as confirmed / to-verify / possibly-stale (watch for stale news presented as current).\n- **Originality**: search the core arguments, frameworks, and signature phrasings. Decide whether each is an independent take/deepening of a public idea (keep, add a distinguishing line if needed) or too close to an existing piece (rewrite). Every \"original / first / exclusive\" claim must be verified — never assert originality from impression.\n\n### Step 3 [Deterministic] G2 style red-line scan (must be clean)\n- Grep the full text for banned patterns (align to the author's writing profile):\n  - \"not A but B\" and all paired-contrast variants\n  - marketing jargon (empower, closed-loop, end-to-end, powerful, significant, substantial)\n  - inflated/preachy/self-media tone\n  - lecturing/advice tone (you should / it is recommended / here is what to do)\n  - arguing by belittling others (most analyses / many articles / everyone assumes)\n  - writing-process meta text (in one line / further questions / this piece will)\n  - explicit commercial intent (keep a researcher stance, no selling)\n- Then read through: no AI tone, judgment-first, written and tightened.\n\n### Step 4 [LLM] R1-R4 target-reader representatives\n- **R1 tech decision-maker**: technically-grounded exec. Flags: vague generalities, phenomena without depth, correct-but-no-new-info conclusions.\n- **R2 cross-domain senior expert**: knows both the local and the subject's market. Flags: lazy simplifications, inaccurate technical detail, arrogant framing.\n- **R3 investor / strategy analyst**: knows business logic, not the code. Flags: floating judgments without data, logic gaps, absolute conclusions without boundaries.\n- **R4 blunt veteran**: zero tolerance for marketing/AI/self-media tone. Flags: filler, inflation, lecturing, correct-but-empty prose.\n\n### Step 5 [LLM] G3 structure and depth\n- Check against six depth moves (see references/depth-playbook.md); hit at least three: (1) puncture a taken-for-granted causal link; (2) give a layered framework that unpacks an overused concept; (3) surface a contradiction inside the subject; (4) place it in a historical lineage; (5) add a horizontal comparison; (6) name the boundary of the judgment.\n- Check structure: judgment-first, no cookie-cutter sections, has a keepable comparison table or framework.\n\n### Step 6 [LLM] T1 distribution check\n- **T1 tech media editor**: knows platform distribution. Assesses title hook (hooked yet still credible), opening retention and searchability, screenshot-worthy memorable points, multi-platform fit.\n- The tension with G2/R4 (hook vs restraint) is surfaced explicitly, not forced into agreement.\n\n### Step 7 [LLM] Consolidate and revise\n- Grade feedback: must-fix / suggested / optional / for-author-decision (tension items).\n- Apply all must-fix and suggested; list tension items as options for the author to decide.\n\n### Step 8 [Deterministic] Re-check\n- Re-run Step 2 and Step 3 after edits (facts and red-lines must not regress; re-grep red-lines to confirm clean).\n\n### Step 9 Finalize\n\n## Hard Rules\n\n> These cannot be violated.\n\n1. **Reviewers critique hard, no self-congratulation** — finding problems beats confirming none.\n2. **G1 has highest priority** — foundational facts and originality must be web-verified, not taken from existing material or impression.\n3. **Red-line clearance is a hard gate** — both Step 3 and Step 8 must grep-confirm clean before finalizing.\n4. **Do not force the professional-vs-distribution tension into agreement** — list options for the author. A hook must not cost credibility, but content too dry to open is also a failure.\n\n## Failure Handling\n\n| Scenario | Action |\n|----------|--------|\n| No draft (only topic/outline) | Stop, ask for a complete draft first |\n| Content form out of scope (news/marketing/docs) | Stop, explain the panel only reviews deep-analysis pieces |\n| Foundational fact cannot be verified | Mark as to-verify; send back for evidence or a softened claim; do not pass |\n| Core argument collides with existing work | Judge independent-take vs verbatim overlap; rewrite the overlapping part |\n| Revision introduces a new red-line pattern | Caught by Step 8 re-check; revise again |\n\n## Output Format\n\n```\n[G1 fact & originality] facts: pass/reject + originality: search result (original / independent take / collision - rewrite)\n[G2 style red-lines] pass/reject: specific sentence + line number\n[R1] value judgment + what it flags + pass or not\n[R2] [R3] [R4] same\n[G3 depth] how many moves hit + what is missing\n[T1 distribution] title hook + opening retention + memorable point + platform fit + tension with G2/R4\n[Summary] must-fix N / suggested N / optional N / for-author-decision N\n```\n\n## Notes\n\nRole profiles can be tuned to a project's specific readership and writing conventions (e.g. aligning to the author's long-term writing profile). Full role definitions and depth moves live in references/depth-playbook.md.\n\n---\n\n一份中文导读：本 skill 用固定的八角色评审团（4 位目标读者代表 + 3 位质量守门人，含事实与原创核查 + 1 位传播守门人），对科技/AI/产业深度稿做成稿后的多视角会审，逼近可发布质量。核心是\"评估→修订→复审\"循环，红线清零是硬门槛，专业与传播的张力交给作者拍板。\n\nFile v1.0.0:README.md\n\n# Tech Content Review Panel\n\nAn eight-role expert review panel for tech/AI/industry deep-analysis articles. It critiques a finished draft from multiple angles and pushes it toward publish-ready quality — built for content that aims to establish a professional personal brand with an industry audience.\n\n## What it does\n\nRuns a fixed panel over a finished draft in an **evaluate → revise → re-check** loop:\n\n- **4 target-reader representatives** — a tech decision-maker, a cross-domain senior expert, an investor/strategist, and a blunt veteran. They judge whether the piece is valuable, expert, and free of empty prose.\n- **3 quality gatekeepers** — a fact-and-originality checker (web-verifies foundational facts, searches for idea collisions/plagiarism risk), a style red-line scanner, and a structure-and-depth reviewer.\n- **1 distribution gatekeeper** — a tech media editor who checks title hooks, opening retention, memorable points, and multi-platform fit.\n\n## When to use\n\n**Applies to**: tech / AI / data industry deep-dives, sector judgment articles, research pieces for an industry readership.\n\n**Does not apply to**: news, marketing copy, product docs, tutorials, or short opinion posts.\n\n## How to use\n\nTrigger it after finishing a deep-analysis draft:\n\n> \"Review this article with the panel.\"\n\nThe panel runs the nine-step workflow (fact & originality check → red-line scan → reader reps → depth → distribution → consolidate → re-check → finalize) and outputs per-role feedback graded as must-fix / suggested / optional / for-author-decision.\n\n## Design\n\n- **Pattern**: Evaluator-Optimizer.\n- **Hard gate**: style red-lines must be grep-clean before finalizing.\n- **Tension handling**: the professional-vs-distribution tension (hook vs restraint) is surfaced explicitly and left for the author to decide, never forced into agreement.\n\nSee `references/depth-playbook.md` for the six depth moves used by the structure reviewer.\n\n## License\n\nMIT\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn70yg6zwmkftx4939qrs89awx82rr9a\",\n  \"slug\": \"tech-content-review-panel\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1784085125434\n}\n\nFile v1.0.0:references/depth-playbook.md\n\n# Six Depth Moves (G3 evaluation basis)\n\nTo judge whether a research/deep-analysis piece has expert-level depth — whether it beats a rewrite of secondary sources — check it against these six moves. Hitting at least three counts as having depth. Each move includes an example drawn from a \"Europe AI · open source as sovereignty\" piece.\n\n## 1. Puncture a taken-for-granted causal link\n\nFind a cause-effect the reader defaults to but that does not actually hold, and break it.\n- Example: most assume \"downloadable weights = sovereignty.\" Puncture: self-hosting a 675B model needs an 8xH200 node plus an ops team; most firms still run it on a US cloud. The weights are free; the substrate running them is not.\n\n## 2. Give a layered framework that unpacks an overused concept\n\nTake a word that is treated as a single concept but is actually abused by marketing, and split it into clear layers.\n- Example: \"sovereignty\" is not one switch. Split into data sovereignty / model sovereignty / operational sovereignty / compute sovereignty, and note that open source only fully delivers the model layer.\n\n## 3. Surface a contradiction inside the subject\n\nFind a point the analyzed subject has not resolved or that is self-contradictory. The most credibility-building move.\n- Example: Mistral uses open source to claim an AI Act compliance exemption, yet its flagship — training compute above 10^25 FLOPs — falls into the systemic-risk tier that open source does NOT exempt. The models that actually enjoy the exemption are its small ones.\n\n## 4. Place it in a historical lineage\n\nConnect a seemingly new phenomenon to an existing thread, showing it is not an isolated event.\n- Example: Mistral's \"open source for adoption, services for revenue\" is the same open-source commercialization path Red Hat / MongoDB / Elastic have walked for twenty years — the difference is an added European sovereignty card.\n\n## 5. Add a horizontal comparison\n\nUse a comparison with peers to highlight what is distinctive about the subject.\n- Example: same act, three strategies — Meta open-sources to break OpenAI's business model, China to break an ecosystem blockade, Europe for sovereignty and compliance.\n\n## 6. Name the boundary of the judgment\n\nAfter giving the core judgment, proactively state its scope and failure conditions. Refuse absolutes.\n- Example: rule-sovereignty is defensive — it protects the home market but cannot win globally; and it rests on the premise that the compute gap has not yet grown large enough to make \"controllable but behind\" unacceptable. Once the frontier capability gap widens, the sovereignty premium erodes.\n\n## Usage notes\n\n- Not all six are required, but strong depth pieces usually hit three or four.\n- Move 3 (internal contradiction) and move 1 (taken-for-granted causality) create the biggest gap versus secondary-source rewrites — look for these first.\n- Every move must land on concrete facts and data, not spin into \"profound-sounding correct filler.\"\n\nFile v1.0.0:README_zh.md\n\n# 科技内容多角色会审\n\n针对科技/AI/产业深度稿的八角色专家评审团。对成稿从多视角挑刺，逼近可发布质量——面向\"用内容建立专业个人 IP、读者是行业人\"的场景。\n\n## 它做什么\n\n对成稿跑一套固定的评审团，走\"评估 → 修订 → 复审\"循环：\n\n- **4 位目标读者代表** —— 技术决策者、跨域资深专家、投资人/战略分析师、专业毒舌老兵。判断内容有没有价值、够不够专业、有没有空话。\n- **3 位质量守门人** —— 事实与原创核查（联网核实立论基石、检索比对防撞车洗稿）、风格红线扫描、结构与专业纵深评估。\n- **1 位传播守门人** —— 技术媒体编辑，看标题钩子、开头留人、可传播记忆点、多平台适配。\n\n## 何时使用\n\n**适用**：科技/AI/数据产业的深度解读、产业判断、研究稿，面向行业读者。\n\n**不适用**：新闻资讯、营销文案、产品文档、技术教程、纯观点短评。\n\n## 怎么用\n\n在深度稿成稿后触发：\n\n> \"用评审团会审这篇文章。\"\n\n评审团跑九步流程（事实与原创核查 → 红线扫描 → 读者代表 → 纵深 → 传播 → 汇总 → 复审 → 定稿），按角色输出意见，分级为必改 / 建议改 / 可选 / 待作者拍板。\n\n## 设计\n\n- **模式**：Evaluator-Optimizer（评估-优化）。\n- **硬门槛**：定稿前风格红线必须 grep 扫描清零。\n- **张力处理**：专业与传播的张力（钩子 vs 克制）显性列出、交给作者拍板，不强行统一。\n\n结构评审用到的\"六个纵深套路\"见 `references/depth-playbook.md`。\n\n## 许可\n\nMIT\n\nFile v1.0.0:skill-card.md\n\n## Description: <br>\nReviews finished tech, AI, or industry deep-analysis drafts with an eight-role expert panel that verifies facts and originality, checks style and depth, and produces publish-ready revision guidance. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[haiyangchenbj](https://clawhub.ai/user/haiyangchenbj) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nWriters, editors, analysts, and content strategists use this skill to review completed tech, AI, data, or industry deep-analysis drafts before publication. It provides a structured panel critique, fact and originality checks, style red-line review, depth assessment, distribution feedback, and prioritized revision guidance. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Fact and originality checks may use web searches with facts or distinctive phrasing from a draft. <br>\nMitigation: Avoid using the skill on confidential unpublished material unless external search exposure is acceptable, or remove sensitive details before review. <br>\nRisk: Editorial recommendations or fact-check results can be incomplete or mistaken if source evidence is weak. <br>\nMitigation: Treat to-verify items and must-fix recommendations as review inputs, and confirm important claims against reliable sources before publication. <br>\n\n\n## Reference(s): <br>\n- [Tech Content Review Panel on ClawHub](https://clawhub.ai/haiyangchenbj/skills/tech-content-review-panel) <br>\n- [Six Depth Moves](references/depth-playbook.md) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, guidance] <br>\n**Output Format:** [Structured Markdown review with role-labeled sections, pass or reject status, issue categories, and prioritized revision guidance.] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May include fact and originality verification notes, style red-line findings, depth-move coverage, distribution feedback, and author-decision tradeoffs.] <br>\n\n## Skill Version(s): <br>\n1.0.0 (source: frontmatter and release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>","readmeExcerpt":"Skill: tech-content-review-panel Owner: haiyangchenbj Summary: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, d","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"Positioning lock: article type / primary reader / reading task / thesis / exclusions / depth target\nSelected roles: why these roles fit\nRole findings: core message / confusion / wrong inference / missing bridge\nConsensus barriers: issues raised by 2+ relevant roles\nDecision table: keep / fix / optional / reject-or-park + reason\nPositioning drift check: pass / fail\nPost-fix targeted re-read: pass / remaining issue"},{"language":"text","snippet":"【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\n【G2 Style red-line】pass/reject: specific sentence + line number\n【R1】value judgment + what it picked on + pass or not\n【R2】【R3】【R4】same as above\n【G3 Depth】how many moves hit + what's missing\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\n【Summary】must-fix N / suggested N / optional N / for-decision N"},{"language":"text","snippet":"【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\n【G2 Style red-line】pass/reject: specific sentence + line number\n【R1】value judgment + what it picked on + pass or not\n【R2】【R3】【R4】same as above\n【G3 Depth】how many moves hit + what's missing\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\n【Summary】must-fix N / suggested N / optional N / for-decision N"},{"language":"text","snippet":"【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)\n【G2 Style red-line】pass/reject: specific sentence + line number\n【R1】value judgment + what it picked on + pass or not\n【R2】【R3】【R4】same as above\n【G3 Depth】how many moves hit + what's missing\n【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4\n【Summary】must-fix N / suggested N / optional N / for-decision N"},{"language":"text","snippet":"[G1 fact & originality] facts: pass/reject + originality: search result (original / independent take / collision - rewrite)\n[G2 style red-lines] pass/reject: specific sentence + line number\n[R1] value judgment + what it flags + pass or not\n[R2] [R3] [R4] same\n[G3 depth] how many moves hit + what is missing\n[T1 distribution] title hook + opening retention + memorable point + platform fit + tension with G2/R4\n[Summary] must-fix N / suggested N / optional N / for-author-decision N"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\r\nname: tech-content-review-panel\r\ndescription: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布.\r\nversion: \"1.2.1\"\r\nagent_created: true\r\nread_when:\r\n  - \"会审 / 评审 / review this article\"\r\n  - \"多视角挑刺 / 让内容接近完美 / 可发布质量\"\r\n  - \"tech/AI/行业深度稿成稿后的质量把关\"\r\nslug: tech-content-review-panel\r\ndisplayName: Tech Content Review Panel\r\ndescription_zh: \"技术内容评审委员会：发布前用固定八角色专家小组（目标读者代表、质量门禁含事实与原创检查、分发门禁）以先评估后优化循环，评审技术/AI/行业研究深度长文。\"\r\nnot_for:\r\n  - Fact-checking a single claim in isolation (use a claim-audit skill instead)\r\n  - Rewriting or restructuring the article itself (review only; revision guidance is advisory)\r\n  - News, marketing copy, documentation, tutorials, or short opinion posts\r\n  - Reviewing content that has no finished draft yet\r\n---\r\n\r\n# Tech Content Review Panel\r\n\r\nA 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.\r\n\r\n**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.\r\n\r\n## When to use\r\n\r\n**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).\r\n**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.\r\n\r\n## Review workflow\r\n\r\n### Step 1 [Deterministic] Confirm input and applicability\r\n- 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.\r\n- Judge whether the content type applies (see above). If not, stop and explain.\r\n\r\n### Step 2 [LLM] G1 Fact & originality check (gate first — reject if it fails)\r\n- **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).\r\n- **Originality**: search (WebSearch) the core argument, framework, and signature phrasing to judge whether it is \"independently derived / deepened from public views\" (k"},{"path":"README.md","content":"# Tech Content Review Panel\n\nAn eight-role expert review panel for tech/AI/industry deep-analysis articles. It critiques a finished draft from multiple angles and pushes it toward publish-ready quality — built for content that aims to establish a professional personal brand with an industry audience.\n\n## What it does\n\nRuns a fixed panel over a finished draft in an **evaluate → revise → re-check** loop:\n\n- **4 target-reader representatives** — a tech decision-maker, a cross-domain senior expert, an investor/strategist, and a blunt veteran. They judge whether the piece is valuable, expert, and free of empty prose.\n- **3 quality gatekeepers** — a fact-and-originality checker (web-verifies foundational facts, searches for idea collisions/plagiarism risk), a style red-line scanner, and a structure-and-depth reviewer.\n- **1 distribution gatekeeper** — a tech media editor who checks title hooks, opening retention, memorable points, and multi-platform fit.\n\n## When to use\n\n**Applies to**: tech / AI / data industry deep-dives, sector judgment articles, research pieces for an industry readership.\n\n**Does not apply to**: news, marketing copy, product docs, tutorials, or short opinion posts.\n\n## How to use\n\nTrigger it after finishing a deep-analysis draft:\n\n> \"Review this article with the panel.\"\n\nAfter the panel revision, run a separate **reader-fit test**. It is not a ninth panelist or a second review round: it tests whether selected target readers correctly understand the core message, scope and technical level. Lock the article positioning first, select 3–4 roles that fit the piece, and triage feedback as must-fix / suggested / optional / reject-or-park. Do not adopt every role's opinion.\n\n## Design\n\n- **Pattern**: Evaluator-Optimizer.\n- **Hard gate**: style red-lines must be grep-clean before finalizing.\n- **Tension handling**: the professional-vs-distribution tension (hook vs restraint) is surfaced explicitly and left for the author to decide, never forced into agreement.\n\nSee `references/depth-playbook.md` for the six depth moves used by the structure reviewer.\n\n## License\n\nMIT"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn70yg6zwmkftx4939qrs89awx82rr9a\",\n  \"slug\": \"tech-content-review-panel\",\n  \"version\": \"1.2.1\",\n  \"publishedAt\": 1791432298404\n}"},{"path":"references/depth-playbook.md","content":"# 专业纵深六套路（G3 评估依据）\n\n判断一篇研究/深度文章有没有\"专家级纵深\"、能不能碾压编译稿，对照这六个套路。至少命中 3 个才算有纵深。每个套路附一个来自\"欧洲 AI·开源即主权\"篇的实例。\n\n## ① 点破想当然的因果\n\n找出读者会默认、但其实不成立的因果链，戳破它。\n- 实例：多数人默认\"权重可下载=拿到主权\"。点破：自托管 675B 模型需要 8×H200 节点+运维团队，多数企业最后还是跑在美国云上——权重是自由的，跑权重的底座不是。\n\n## ② 给分层框架拆解被滥用的概念\n\n把一个被当成单一概念、其实被营销滥用的词，拆成清晰的层次。\n- 实例：\"主权\"不是一个开关，拆成数据主权/模型主权/运营主权/算力主权四层，指出开源只给足\"模型主权\"那一层。\n\n## ③ 找出立论对象自身的矛盾\n\n在被分析的对象内部，找一个它自己没解决或自相矛盾的点。这是最显专业的一招。\n- 实例：Mistral 靠开源拿 AI Act 合规豁免，但它的旗舰因训练算力超 10²⁵ FLOPs 落进系统性风险区，恰恰不能享开源豁免——真正吃满豁免的是它的小模型。\n\n## ④ 放进历史脉络\n\n把一个看似新的现象，接到一条已有的历史线索上，显出它不是孤立事件。\n- 实例：Mistral\"开源引流、服务变现\"是 Red Hat/MongoDB/Elastic 走了二十年的开源商业化老路，区别是多打了一张欧洲主权牌。\n\n## ⑤ 给横向参照系\n\n用同类对象的对比，凸显分析对象的独特性。\n- 实例：同样是开源，Meta 为打 OpenAI 商业模式、中国为突破生态围堵、欧洲为主权合规——同一动作三种战略。\n\n## ⑥ 点破判断的边界\n\n给出核心判断后，主动指出它的适用边界和失效条件，拒绝绝对化。\n- 实例：规则主权是防御性的，守得住本地市场但抢不了全球；且压在\"算力差距还没大到不可接受\"的前提上，一旦前沿能力鸿沟拉开，控制权的溢价会失效。\n\n## 使用提示\n\n- 这六招不必全用，但优质深度稿通常命中 3-4 个。\n- ③（自身矛盾）和 ①（想当然因果）最能拉开与编译稿的差距，优先找。\n- 每一招都要落到具体事实和数据，不能空转成\"看似深刻的正确废话\"。"},{"path":"README_zh.md","content":"# 科技内容多角色会审\n\n针对科技/AI/产业深度稿的八角色专家评审团。对成稿从多视角挑刺，逼近可发布质量——面向\"用内容建立专业个人 IP、读者是行业人\"的场景。\n\n## 它做什么\n\n对成稿跑一套固定的评审团，走\"评估 → 修订 → 复审\"循环：\n\n- **4 位目标读者代表** —— 技术决策者、跨域资深专家、投资人/战略分析师、专业毒舌老兵。判断内容有没有价值、够不够专业、有没有空话。\n- **3 位质量守门人** —— 事实与原创核查（联网核实立论基石、检索比对防撞车洗稿）、风格红线扫描、结构与专业纵深评估。\n- **1 位传播守门人** —— 技术媒体编辑，看标题钩子、开头留人、可传播记忆点、多平台适配。\n\n## 何时使用\n\n**适用**：科技/AI/数据产业的深度解读、产业判断、研究稿，面向行业读者。\n\n**不适用**：新闻资讯、营销文案、产品文档、技术教程、纯观点短评。\n\n## 怎么用\n\n在深度稿成稿后触发：\n\n> \"用评审团会审这篇文章。\"\n\n八角色会审完成后，另跑一次 **reader-fit 阅读测试**：它不是第九个角色，也不是第二轮会审，而是测试目标读者能否正确理解文章的核心信息、范围和技术层级。先锁文章定位，再按文章需要选择 3–4 个代表角色；意见分为必改 / 建议改 / 可选 / 拒绝或停放，不能一概采用。\n\n## 设计\n\n- **模式**：Evaluator-Optimizer（评估-优化）。\n- **硬门槛**：定稿前风格红线必须 grep 扫描清零。\n- **张力处理**：专业与传播的张力（钩子 vs 克制）显性列出、交给作者拍板，不强行统一。\n\n结构评审用到的\"六个纵深套路\"见 `references/depth-playbook.md`。\n\n## 许可\n\nMIT"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. Applies to tech/AI/data deep-dives, sector judgment and research pieces — not news, marketing, docs, tutorials, or short opinion posts. Trigger when the user asks to 会审/评审/review a finished deep-analysis draft or wants tech content 接近完美/可发布. Skill: tech-content-review-panel Owner: haiyangchenbj Summary: Reviews a tech/AI/industry research or in-depth analysis long-form article before publishing, via a fixed eight-role expert panel (target-reader reps, quality gatekeepers incl. fact+originality check, distribution gatekeeper) in an evaluate-then-optimize loop. 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