cheat-on-content
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
Essential tools and frameworks for modern product management, from discovery to delivery.
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill product-manager-toolkit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/product-manager-toolkitContext preview
The summary Claude sees to decide when to auto-load this skill.
Essential tools and frameworks for modern product management, from discovery to delivery.
name: product-manager-toolkit description: "Essential tools and frameworks for modern product management, from discovery to delivery." risk: unknown source: community date_added: "2026-02-27"
Essential tools and frameworks for modern product management, from discovery to delivery.
python scripts/rice_prioritizer.py sample # Create sample CSV python scripts/rice_prioritizer.py sample_features.csv --capacity 15
python scripts/customer_interview_analyzer.py interview_transcript.txt
1. Choose template from `references/prd_templates.md` 2. Fill in sections based on discovery work 3. Review with stakeholders 4. Version control in your PM tool
1. **Gather Feature Requests**
2. **Score with RICE**
# Create CSV with: name,reach,impact,confidence,effort python scripts/rice_prioritizer.py features.csv
3. **Analyze Portfolio**
4. **Generate Roadmap**
1. **Conduct Interviews**
2. **Analyze Insights**
python scripts/customer_interview_analyzer.py transcript.txt
Extracts:
3. **Synthesize Findings**
4. **Validate Solutions**
1. **Choose Template**
2. **Structure Content**
3. **Collaborate**
Advanced RICE framework implementation with portfolio analysis.
**Features**:
**Usage Examples**:
# Basic prioritization python scripts/rice_prioritizer.py features.csv # With custom team capacity (person-months per quarter) python scripts/rice_prioritizer.py features.csv --capacity 20 # Output as JSON for integration python scripts/rice_prioritizer.py features.csv --output json
NLP-based interview analysis for extracting actionable insights.
**Capabilities**:
**Usage Examples**:
# Analyze single interview python scripts/customer_interview_analyzer.py interview.txt # Output as JSON for aggregation python scripts/customer_interview_analyzer.py interview.txt json
Multiple PRD formats for different contexts:
1. **Standard PRD Template**
2. **One-Page PRD**
3. **Agile Epic Template**
4. **Feature Brief**
Score = (Reach × Impact × Confidence) / Effort Reach: # of users/quarter Impact: - Massive = 3x - High = 2x - Medium = 1x - Low = 0.5x - Minimal = 0.25x Confidence: - High = 100% - Medium = 80% - Low = 50% Effort: Person-months
Low Effort High Effort
High QUICK WINS BIG BETS
Value [Prioritize] [Strategic]
Low FILL-INS TIME SINKS
Value [Maybe] [Avoid]1. Context Questions (5 min) - Role and responsibilities - Current workflow - Tools used 2. Problem Exploration (15 min) - Pain points - Frequency and impact - Current workarounds 3. Solution Validation (10 min) - Reaction to concepts - Value perception - Willingness to pay 4. Wrap-up (5 min) - Other thoughts - Referrals - Follow-up permission
We believe that [building this feature] For [these users] Will [achieve this outcome] We'll know we're right when [metric]
Outcome
├── Opportunity 1
│ ├── Solution A
│ └── Solution B
└── Opportunity 2
├── Solution C
└── Solution D1. **Identif
MUNDO - THE EMPEROR. Complete AI orchestration system with 1208 skills, 25 capability modules, self-evolving, collective consciousness. GitHub Actions 24/7 automation.
Repo: LiHongwei-cn/lihongwei-cn
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
提议并执行 rubric 或 bucket 升级。两种模式:**完整 rubric bump**(最高风险动作,5 步强制 + 跨模型审核)和 **--bucket-only 轻量重校**(只换 bucket 边界,不动 rubric 公式)。**Phase 2 强制走 cheat-score-blind…
cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环,唯一区别是"发过视频的人"会在 init 时多一步:抓取已有视频建立历史 context(用于后续 cheat-seed 给更贴合的选题、更准的…
从对标账号导入 script + 数据 → 拆 pattern + 派生 base rubric 信号 → 写到 benchmark.md / script_patterns.md / rubric_notes.md。**这是工具最早期信号的来源**——cold-start…
把老用户的 .cheat-state.json 升级到当前 schema_version。读 migrations/registry.md 算迁移链,按顺序应用每一步迁移文件。幂等:跑两次结果一样。失败停在中间版本不前进。触发词:"迁移"/"升级 state"/"migrate"/"我的 state…
从复盘评论数据派生 / 刷新账号的受众画像,写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分",persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md 含实绩信号,cheat-score-blind…