theory-analysis-product-positioning
About theory-analysis-product-positioning
A marketing skill that applies product positioning theory to evidence from interviews, support tickets, social posts, and other sources. It codes four positioning dimensions and produces readable reports plus structured results, with optional `review-mining-stp` integration.
Key features
- 01Codes four positioning dimensions
- 02Checks required inputs and evidence sufficiency
- 03Preserves source wording in quotations
- 04Outputs JSON and Markdown
- 05Supports optional `review-mining-stp` integration
Use cases
- 01Analyze product positioning across research notes
- 02Organize evidence from support tickets and interviews
- 03Pass annotations to `review-mining-stp` for further analysis
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ timlai666/ skills --skill theory-analysis-product-positioningAfter installing, type/theory-analysis-product-positioningin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
agents/openai.yamlreferences/00-theory-foundation.md01-input-and-gate.md02-dimension-coding-rules.md03-output-contract-json-markdown.md04-worked-example-turbocharger.mdDetails
From the project READMETheory Analysis Product Positioning
Overview
用產品定位理論(Product Positioning Theory)分析跨來源證據,輸出可讀報告與可機器串接的結構化結果。
可用來源包含訪談摘要、客服工單、社群貼文、研究筆記、觀察紀錄等,不限於評論探勘。
本 skill 含完整理論章節,可在分析前先校準理論概念與構面判準。
此 skill 可獨立使用,也可選擇把 theory_annotations 與 stp_mapping 交給 review-mining-stp。
Input Contract
至少提供:
analysis_goal: stringevidence_items: arrayitem_id: stringcontent: stringcontent_type: string
建議可選:
source_type: stringsource_ref: stringcontext_tags: string[]
Data Sufficiency Gate
若缺少必要欄位或沒有可用證據內容,回傳 MissingDataOutput,不要硬做推論。
{
"missing_fields": [],
"why_needed": {},
"questions_to_user": [],
"next_step_rule": "Provide analysis_goal and at least one evidence item with item_id, content, and content_type, then rerun coding."
}
Workflow
- 先讀
references/00-theory-foundation.md,校準理論概念、核心構面與研究應用語境。 - 讀
references/01-input-and-gate.md,做輸入檢查與缺資料判定。 - 讀
references/02-dimension-coding-rules.md,依四個構面做證據標註:attributesfunctionsbenefitsusage_context_service_experience
- 讀
references/03-output-contract-json-markdown.md,輸出 JSON 與 Markdown。 - 需要示範格式時,參考
references/04-worked-example-turbocharger.md。
STP Integration
theory_annotations.family固定為product_positioningtheory_annotations.subtheory只可使用:attributesfunctionsbenefitsusage_context_service_experience
可搭配 review-mining-stp 的 dimension_catalog 與 stat_roles 進一步分析。
review-mining-stp 屬於 optional downstream,不是本 skill 的必經流程。
Quality Rules
- 引文必須保留原句,不可改寫成看似直接引述。
- 每個構面摘要至少要有一則證據或明確標示
insufficient。 - 不可輸出 taxonomy 之外的 subtheory。
Suggested Prompts
Use $theory-analysis-product-positioning to code mixed evidence items and output JSON + Markdown in Traditional Chinese.請用 $theory-analysis-product-positioning 分析多來源證據,並提供可選串接 review-mining-stp 的 theory_annotations。