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portfolio-roadmapping-bets投资组合路线图押注

Agent Skill

portfolio-roadmapping-bets 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:portfolio-roadmapping-bets(投资组合路线图押注)
来源仓库:https://github.com/lyndonkl/claude
仓库路径:skills/portfolio-roadmapping-bets
安装命令:
npx skills add https://github.com/lyndonkl/claude --skill portfolio-roadmapping-bets
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/lyndonkl/claude --skill portfolio-roadmapping-bets

简介

portfolio-roadmapping-bets 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定仓库安装并使用。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Portfolio Roadmapping Bets

When NOT to use: Single initiative with clear priority (use one-pager-prd or project-risk-register instead), purely operational prioritization without strategic horizons (use prioritization-effort-impact), or no resource constraints or trade-offs.

Workflow

Copy this checklist and track your progress:

Portfolio Roadmapping Bets Progress:
- [ ] Step 1: Define portfolio theme and constraints
- [ ] Step 2: Inventory and size all bets
- [ ] Step 3: Sequence bets across horizons
- [ ] Step 4: Set exit and scale criteria
- [ ] Step 5: Balance and validate portfolio

Step 1: Define portfolio theme and constraints

Clarify the strategic theme (north star), time horizons (H1/H2/H3 definitions), resource constraints (budget, people, time), and portfolio balance targets (e.g., 70/20/10 rule). See Portfolio Theme & Constraints for guidance.

Step 2: Inventory and size all bets

List all candidate initiatives, size each by effort (S/M/L/XL) and impact potential (1x/3x/10x), categorize by type (core/adjacent/transformational), and identify dependencies. For simple cases use resources/template.md. For complex cases with 15+ bets or multiple themes, study resources/methodology.md.

Step 3: Sequence bets across horizons

Assign each bet to H1 (now), H2 (next), or H3 (later) based on dependencies, strategic timing, learning sequencing, and capacity constraints. See Sequencing & Dependencies for sequencing heuristics.

Step 4: Set exit and scale criteria

For each bet, define what success looks like (scale criteria: double down, expand scope) and what failure looks like (exit criteria: kill, deprioritize, pivot). See Exit & Scale Criteria for examples.

Step 5: Balance and validate portfolio

Check portfolio balance (are we too conservative or too aggressive?), validate resource feasibility (can we actually staff this?), and self-assess using resources/evaluators/rubric_portfolio_roadmapping_bets.json. Minimum standard: ≥3.5 average score. See Portfolio Balance Checks.

Common Patterns

By Portfolio Type

Product Portfolio (multiple features/products):

  • H1: Ship quick wins and critical bugs
  • H2: Strategic features with cross-product dependencies
  • H3: Platform bets and R&D exploration
  • Balance: 60% incremental, 30% substantial, 10% breakthrough

Technology Portfolio (platform, infrastructure, tech debt):

  • H1: Stability, security, performance quick wins
  • H2: Major migrations and platform upgrades
  • H3: Next-gen architecture and tooling
  • Balance: 50% maintain, 30% improve, 20% transform

Innovation Portfolio (R&D, experiments, ventures):

  • H1: Validated experiments ready to scale
  • H2: Active experiments with checkpoints
  • H3: Early-stage exploration and research
  • Balance: 70% core business, 20% adjacent, 10% transformational (McKinsey Horizons)

Marketing Portfolio (campaigns, channels, experiments):

  • H1: Proven channels with optimization
  • H2: New channel experiments and tests
  • H3: Brand building and long-term positioning
  • Balance: 70% performance marketing, 20% growth experiments, 10% brand

By Bet Size

Small Bets (1-2 weeks, 1-2 people):

  • Low effort, low-to-medium impact
  • Use for quick wins, experiments, bug fixes
  • Example: A/B test new pricing page (2 weeks, 1.2x conversion potential)

Medium Bets (1-3 months, 3-5 people):

  • Moderate effort, moderate-to-high impact
  • Use for features, improvements, small initiatives
  • Example: Build seller dashboard (2 months, 1.8x seller retention)

Large Bets (3-6 months, 5-10 people):

  • High effort, high impact
  • Use for strategic initiatives, platform work, major features
  • Example: Marketplace trust & safety system (5 months, 3x GMV via reduced fraud)

X-Large Bets (6-12+ months, 10+ people):

  • Very high effort, transformational impact potential
  • Use for platform rewrites, new business lines, moonshots
  • Example: AI-powered recommendation engine (9 months, 10x engagement potential)

By Risk Profile

Core Bets (Low Risk, Incremental Return):

  • Optimize existing products/channels
  • Proven approaches with clear ROI
  • Example: Improve search relevance from 65% → 75% accuracy

Adjacent Bets (Medium Risk, Substantial Return):

  • Extend to new use cases, segments, or capabilities
  • Validated approach, new application
  • Example: Launch seller analytics (proven feature, new user segment)

Transformational Bets (High Risk, Breakthrough Return):

  • New business models, technologies, or markets
  • Unproven approach, high uncertainty
  • Example: Blockchain-based ownership system (unproven tech, could unlock new market)

Portfolio Theme & Constraints

Define the strategic anchor for your portfolio:

Theme: The overarching goal (e.g., "Grow enterprise revenue 3x", "Achieve platform parity", "Launch in APAC")

Time Horizons:

  • H1 (Now): 0-6 months - High confidence, shipping soon
  • H2 (Next): 6-12 months - Medium confidence, in planning/development
  • H3 (Later): 12-24+ months - Lower confidence, exploration/research

Resource Constraints:

  • Budget: $X available across all initiatives
  • People: Y engineers, Z designers, etc. (capacity by function)
  • Time: When must key milestones be hit? (launch date, board meeting, fiscal year)

Portfolio Balance Targets:

  • Example: 70% core / 20% adjacent / 10% transformational (McKinsey Three Horizons)
  • Example: 60% product features / 30% platform / 10% R&D
  • Example: 50% H1 / 30% H2 / 20% H3 (de-risk near term while investing in future)

Sequencing & Dependencies

Types: Technical (infrastructure), Learning (insights), Strategic (validation), Resource (capacity)

Heuristics: Dependencies first, learn before scaling, quick wins early, long bets start early, hedge portfolio

Exit & Scale Criteria

Exit (kill): Time-based ("90 days"), Metric ("<5% adoption"), Cost (">$X"), Strategic ("market shifts") Scale (double-down): Adoption (">20%"), Engagement (">3x baseline"), Revenue (">1.5x target"), Efficiency ("<$X CAC")

Example: AI chatbot bet | Exit: Deflection <30% after 60d OR sentiment <-20% | Scale: Deflection >50% AND sentiment >70%

Portfolio Balance Checks

Risk: ✓ ~70% core, ~20% adjacent, ~10% transformational | ❌ >80% core (too safe) or >30% transformational (too risky) Horizon: ✓ ~50-60% H1, ~25-30% H2, ~15-20% H3 | ❌ >70% H1 (no future) or >40% H3 (no near-term) Capacity: Effort ≤ capacity × 0.8 (20% slack) | Example: 10 eng → 48 EM/6mo → max 38 EM in H1 Impact: Portfolio ladders to theme (risk-adjusted) | Example: "3x revenue" → bets sum to 4.7x potential → 50% fail = 2.35x expected → add more bets

Guardrails

Problem Framing:

  • ❌ Vague theme like "improve product" → ✓ Specific like "Reduce churn from 5% to 2% in 12 months"
  • ❌ No constraints (infinite resources) → ✓ Explicit budget, people, time limits
  • ❌ Missing portfolio balance targets → ✓ Define risk tolerance (e.g., 70/20/10)

Bet Sizing:

  • ❌ Effort in person-days without context → ✓ Use S/M/L/XL relative sizing
  • ❌ Impact as vague "high/medium/low" → ✓ Use multipliers (1x/3x/10x) or concrete metrics
  • ❌ All bets are "high priority" → ✓ Force-rank or categorize by type

Sequencing:

  • ❌ No dependencies identified → ✓ Map technical, learning, strategic dependencies
  • ❌ All bets in H1 (wish list) → ✓ Realistic capacity-constrained sequencing
  • ❌ No rationale for sequence → ✓ Explain why A before B (dependency, learning, quick win)

Exit & Scale Criteria:

  • ❌ No criteria (just "we'll see") → ✓ Specific metrics and timelines for kill/scale decisions
  • ❌ Only exit criteria (pessimistic) → ✓ Include scale criteria (what does wild success look like?)
  • ❌ Unmeasurable criteria → ✓ Use quantifiable metrics with baselines

Portfolio Balance:

  • ❌ All core (too safe) or all transformational (too risky) → ✓ Balanced risk distribution
  • ❌ Sum of efforts exceeds capacity → ✓ Effort ≤ capacity × 0.8 (20% slack for unknowns)
  • ❌ Expected impact below strategic goal → ✓ Portfolio ladders up to theme with risk adjustment

Quick Reference

Resources:

Success Criteria:

  • ✓ Strategic theme is clear and measurable
  • ✓ All bets sized by effort (S/M/L/XL) and impact (1x/3x/10x)
  • ✓ Bets sequenced across H1/H2/H3 with dependency rationale
  • ✓ Exit and scale criteria defined for each bet
  • ✓ Portfolio balanced across risk profiles and horizons
  • ✓ Total effort ≤ team capacity (with 20% slack)
  • ✓ Expected portfolio impact ≥ strategic goal (risk-adjusted)

Common Mistakes:

  • ❌ No strategic theme → roadmap becomes random wish list
  • ❌ All bets sized "Large" → no useful prioritization
  • ❌ No exit criteria → sunk cost fallacy, zombie projects
  • ❌ Portfolio imbalanced → all quick wins (no future) or all moonshots (no near-term value)
  • ❌ Dependencies ignored → H1 bets blocked by H2 infrastructure
  • ❌ Over-capacity → team burns out, quality suffers
  • ❌ Under-ambitious → portfolio impact below strategic goal even if everything succeeds

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