Install
openclaw skills install @haiyangchenbj/tech-content-review-panelReviews 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.
openclaw skills install @haiyangchenbj/tech-content-review-panelA tech/AI/industry deep-analysis piece aimed at an industry readership and built to establish a professional personal brand is easy to miss with a single perspective. This skill provides a fixed eight-role expert panel that reviews a finished draft from multiple angles, giving blunt per-role feedback to push it toward publish-ready quality.
Design pattern: Evaluator-Optimizer — the panel evaluates, you revise per the feedback, then re-check to confirm no new problems before finalizing. It is a generate → evaluate → revise loop.
Applies to: tech / AI / industry research or in-depth analysis long-form articles aimed at an industry readership (industry deep-dives, sector judgments, research pieces). Does not apply to: news, marketing copy, product docs, tutorials, or short opinion posts — different goals and criteria mean this panel would mismatch. When such content triggers, tell the user this skill does not apply.
negative_rules):
not X but Y, rather than, instead of, and similar forms for density and rhetorical function; do not mechanically delete natural comparative language.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.Run 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.
Record five items before collecting reader feedback:
Reader 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.
Choose 3–4 roles that match the article. Typical options:
Selection 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.
Each reader must separate misunderstanding, missing bridge, disagreement, and different preference. Do not treat all negative feedback as a defect.
Do not average all reader opinions or adopt every suggestion. Classify each item:
Priority order: factual correctness → positioning integrity → core comprehension → technical depth → elegance → distribution preference.
When readers disagree, preserve the article's positioning. Record:
Two 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.
Apply 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.
Positioning lock: article type / primary reader / reading task / thesis / exclusions / depth target
Selected roles: why these roles fit
Role findings: core message / confusion / wrong inference / missing bridge
Consensus barriers: issues raised by 2+ relevant roles
Decision table: keep / fix / optional / reject-or-park + reason
Positioning drift check: pass / fail
Post-fix targeted re-read: pass / remaining issue
Finalize only after the selected reader-fit test passes its positioning-drift check and all unresolved items are either fixed or explicitly parked.
Cannot be violated.
| Scenario | Handling |
|---|---|
| No finished draft (only topic/outline) | Stop, ask to finish first draft before review |
| Content type does not apply (news/marketing/docs etc.) | Stop, explain this skill only reviews deep research pieces |
| Foundational fact cannot be verified | Mark "to-verify", reject and ask for evidence or revised judgment; do not pass |
| Core argument collides (plagiarism risk) | Judge independent-derivation vs verbatim-similar; if similar, reject and ask to rewrite that part |
| Revision introduces new red-line phrasing | Step 8 re-check intercepts, revise again |
【G1 Fact & Originality】Facts: pass/reject + Originality: core-argument search result (original / independently derived / collision-needs-fix)
【G2 Style red-line】pass/reject: specific sentence + line number
【R1】value judgment + what it picked on + pass or not
【R2】【R3】【R4】same as above
【G3 Depth】how many moves hit + what's missing
【T1 Distribution】title hook + opening retention + memorable point + platform fit + tension with G2/R4
【Summary】must-fix N / suggested N / optional N / for-decision N
Role profiles can be fine-tuned per the specific project's reader composition and writing norms (e.g., align with the user's long-term writing profile). Detailed role definitions and depth moves are in references/depth-playbook.md.
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)。
references/depth-playbook.md。