Feature Scope Slicer
Cut an approved but oversized feature into thin vertical slices, each shippable, with a success metric, guardrail, and keep-going bar.
Before & after
Same prompt, same agent — the only difference is the skill.
That depends on a lot of things - your current metrics, what customers are asking for, where your team is struggling. You'd probably want to look at ticket volume, resolution time, and customer satisfaction. I think prioritization usually comes down to impact and effort.
CS PRODUCT MANAGER Q3 PRIORITIES
STEP 1: BASELINE METRICS (Pull data from last 30 days)
• Avg. response time: 4.2 hours (target: <2 hours)
• Resolution rate first contact: 62% (target: 75%)
• CSAT score: 7.8/10 (target: 8.5/10)
• Ticket volume: 2,100/month (trending +12% MoM)
• Churn rate: 8% (customers citing "support responsiveness" as reason: 23% of churn)
STEP 2: ROOT CAUSE ANALYSIS
→ Response time gap: Team at 85% utilization, 6 open tickets per agent
→ First-contact resolution: Knowledge base outdated (30% of issues are RTFM)
→ CSAT dips: Repetitive escalations (same 5 issues account for 40% of volume)
PRIORITY RANKING (QUARTER GOALS)
1. KNOWLEDGE BASE REFRESH (Impact: 8/10, Effort: 4/10)
→ Audit 20 most-common issues
→ Rewrite 15 help articles (10 hours)
→ Link to tickets (automation, 5 hours)
→ Expected outcome: +8% first-contact resolution, -200 tickets/month
→ Timeline: Week 1-2, owner: support manager
2. HIRING + RAMP (Impact: 9/10, Effort: 6/10)
→ Hire 2 support agents
→ Onboarding program (2 weeks ramp)
→ Pair with senior (6 weeks buddy system)
→ Expected outcome: Utilization 60%, response time <2 hours
→ Timeline: Week 1 (job posting), hire by week 4
3. TICKETING WORKFLOW TRIAGE (Impact: 7/10, Effort: 3/10)
→ Create automated tags for issue type
→ Route to specialist (billing → finance person, bugs → dev)
→ Escalation rules (if unresolved 24h → manager)
→ Expected outcome: +6% CSAT, -15% time-to-escalation
→ Timeline: Week 3
4. CSAT SURVEY FOLLOW-UP (Impact: 6/10, Effort: 2/10)
→ 1-question survey post-resolution ("Would you recommend?")
→ Collect verbatim for pattern detection
→ Expected outcome: Early signal on satisfaction drops
→ Timeline: Week 2
WARNING: If you hire before knowledge base refresh, onboarding will be chaotic. Sequence: KB first (async), hiring starts week 2.About this skill
Big features stall because teams slice them by layer or call an unusable prototype an MVP. This skill cuts an approved feature into thin vertical slices that each ship to real users and prove something.
What it does
- Applies proven splitting patterns: workflow steps, happy path first, rule and data variations, segment, manual-behind-the-curtain, spikes
- Puts the riskiest assumption (adoption, feasibility, performance, commercial) in slice 1
- Gives each slice one success metric, one guardrail, and a keep-going bar decided before launch
- Flags one-way doors (data model, public API, pricing) to get right early and safe fallbacks for deferred scope
- Scales from a quick "what's the MVP?" answer to a full slicing plan
What you get
A sequenced slice table (in, out with fallback, size, metric, guardrail, threshold), the decision point after the first slices, per-release non-goals for sales and support, and open questions.
Best for
PMs and engineering leads facing an epic estimated at a quarter when the business needs something in weeks.
SKILL.md preview
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name: feature-scope-slicer
description: Cut a feature that is already approved but too big into thin, shippable vertical slices - each one usable by a real user, releasable on its own, and tied to a success metric and a guardrail - then sequence them so the riskiest assumption is tested first. Use when the user says "this epic is too big", "what's the MVP here?", "how do we split this feature?", "can we ship something in two weeks?", "break this into releases", "engineering says this is a quarter of work", or "what's v1 vs v2?".
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# Feature Scope Slicer
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