skills/portfolio-roadmapping-bets/SKILL.md
Creates strategic portfolio roadmaps that size and sequence initiative bets across time horizons (H1/H2/H3), balance risk profiles (core/adjacent/transformational), and set clear exit/scale criteria for disciplined resource allocation. Use when managing multiple initiatives across time horizons, balancing risk vs return across portfolio, sizing and sequencing bets with dependencies, setting exit/scale criteria for experiments, allocating resources across innovation types, or when user mentions portfolio planning, roadmap horizons, betting framework, initiative prioritization, innovation portfolio, or resource allocation across horizons.
npx skillsauth add lyndonkl/claude portfolio-roadmapping-betsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
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.
Product Portfolio (multiple features/products):
Technology Portfolio (platform, infrastructure, tech debt):
Innovation Portfolio (R&D, experiments, ventures):
Marketing Portfolio (campaigns, channels, experiments):
Small Bets (1-2 weeks, 1-2 people):
Medium Bets (1-3 months, 3-5 people):
Large Bets (3-6 months, 5-10 people):
X-Large Bets (6-12+ months, 10+ people):
Core Bets (Low Risk, Incremental Return):
Adjacent Bets (Medium Risk, Substantial Return):
Transformational Bets (High Risk, Breakthrough Return):
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:
Resource Constraints:
Portfolio Balance Targets:
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 (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%
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
Problem Framing:
Bet Sizing:
Sequencing:
Exit & Scale Criteria:
Portfolio Balance:
Resources:
Success Criteria:
Common Mistakes:
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.