skills/prioritization-effort-impact/SKILL.md
Transforms overwhelming backlogs into clear, actionable priorities by mapping items on a 2x2 effort-vs-impact matrix, identifying quick wins (high impact, low effort), big bets, time sinks, and fill-ins. Use when ranking backlogs, deciding what to do first, prioritizing feature roadmaps, triaging bugs or technical debt, allocating resources across initiatives, identifying low-hanging fruit, evaluating strategic options, or when user mentions prioritization, quick wins, effort-impact matrix, high-impact low-effort, big bets, or "what should we do first?".
npx skillsauth add lyndonkl/claude prioritization-effort-impactInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Copy this checklist and track your progress:
Prioritization Progress:
- [ ] Step 1: Gather items and clarify scoring
- [ ] Step 2: Score effort and impact
- [ ] Step 3: Plot matrix and identify quadrants
- [ ] Step 4: Create prioritized roadmap
- [ ] Step 5: Validate and communicate decisions
Step 1: Gather items and clarify scoring
Collect all items to prioritize (features, bugs, initiatives, etc.) and define scoring scales for effort and impact. See Scoring Frameworks for effort and impact definitions. Use resources/template.md for structure.
Step 2: Score effort and impact
Rate each item on effort (1-5: trivial to massive) and impact (1-5: negligible to transformative). Involve subject matter experts for accuracy. See resources/methodology.md for advanced scoring techniques like Fibonacci, T-shirt sizes, or RICE.
Step 3: Plot matrix and identify quadrants
Place items on 2x2 matrix and categorize into Quick Wins (high impact, low effort), Big Bets (high impact, high effort), Fill-Ins (low impact, low effort), and Time Sinks (low impact, high effort). See Common Patterns for typical quadrant distributions.
Step 4: Create prioritized roadmap
Sequence items: Quick Wins first, Big Bets second (after quick wins build momentum), Fill-Ins during downtime, avoid Time Sinks unless required. See resources/template.md for roadmap structure.
Step 5: Validate and communicate decisions
Self-check using resources/evaluators/rubric_prioritization_effort_impact.json. Ensure scoring is defensible, stakeholder perspectives included, and decisions clearly explained with rationale.
By domain:
By stakeholder priority:
Typical quadrant distribution:
Red flags:
Effort dimensions (choose relevant ones):
Impact dimensions (choose relevant ones):
Composite scoring:
Example scoring (feature: "Add dark mode"):
Ensure quality:
Include diverse perspectives: Don't let one person score alone (eng overestimates effort, sales overestimates impact)
Differentiate scores: If everything is scored 3, you haven't prioritized
Question extreme scores: High-impact low-effort items are rare (if you have 10, something's wrong)
Make scoring transparent: Document why each score was assigned
Revisit scores periodically: Effort/impact change as context evolves
Don't ignore dependencies: Low-effort items blocked by high-effort prerequisites aren't quick wins
Beware of "strategic" override: Execs calling everything "high impact" defeats prioritization
Resources:
Success criteria:
Common mistakes:
When to use alternatives:
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.