product-shaping

Product Shaping

Pre-commitment methodology for product and engineering work, adapted from Ryan Singer's Shape Up (free edition at basecamp.com/shapeup), extended for teams whose builders include AI agents.

The loop: shape a raw idea into a bounded pitch → bet a fixed appetite on it → build by discovering scopes and hammering scope to fit the box → move on, letting post-ship feedback re-enter as raw ideas.

The core moves

1. Set boundaries — choose the appetite ("how much is this worth?") and narrow the problem to one specific story. Kill grab-bags ("redesign X", "X 2.0").
2. Find the elements — sketch the solution rough, solved, and bounded: breadboards for flows, fat-marker fidelity for visual problems, components-and-contracts for non-UI work.
3. Patch rabbit holes — attack your own sketch; settle hard decisions now, declare out-of-bounds cases, cut what the appetite can't afford.
4. Write the pitch — problem, appetite, solution, rabbit holes, no-gos.
5. Bet — commit the box uninterrupted, downside capped. No finish, no extension by default: the circuit breaker routes failure back to shaping.
6. Build — one integrated slice first, then discovered scopes tracked as uphill→downhill states; sequence scariest-first; compare down to baseline when deciding to stop.
7. Move on — scope cuts are not quality cuts; new feedback needs shaping, not instant yes.

Reference files

Load only what the current step needs:

Reference	Load when
references/principles.md	You need the why: appetite vs estimate, fixed-time-variable-scope, rough/solved/bounded, evidence boundaries, lineage
references/shaping.md	Shaping steps 1–4 in detail, including shaping non-UI/backend/infrastructure work
references/betting.md	Bets vs backlogs, circuit breaker mechanics, cycles as optional scaffolding, handling defects between bets
references/building.md	Hand-over-responsibility, one-piece-done, scope mapping, hill-state tracking, deciding when to stop
references/hybrid-adaptation.md	Any bet involving AI-agent builders: budget currencies, batched steering, verification cost inside scope, kill criteria for non-converging loops
references/anti-patterns.md	Before betting anything that matters — documented field failures and their mitigations

Templates

Template	Purpose
templates/PITCH.md	Fillable five-ingredient pitch document
templates/SCOPE_MAP.md	Fillable scope table with hill states, chowder list, and breaker check

Entry points

Situation	Start here
Raw idea or request arrived	references/shaping.md step 1
Idea is validated but unbounded	references/shaping.md
Ready to write up the concept	templates/PITCH.md
Deciding what gets the next box	references/betting.md
Bet placed, starting the build	references/building.md
Builders are AI agents	references/hybrid-adaptation.md
Project keeps not finishing / loops won't converge	references/anti-patterns.md, then hybrid-adaptation kill criteria

When not to use

• The problem itself isn't validated yet → product-discovery. Shaping narrows validated problems; it does not investigate whether the problem is real.
• The question is strategic (positioning, market entry, portfolio weight across quarters) → product-strategy or product-roadmapping-and-portfolio. This skill packages a single bet — one bounded commitment with an appetite and circuit breaker; roadmapping sequences many such bets across cycles with continue/pause/kill criteria.
• The bet is already placed and the work needs a formal spec → spec-driven- development consumes shaped output when formal specification is warranted.
• Comparing unrelated feature proposals by score → product-methodology (RICE/ MoSCoW). Appetite replaces scoring inside this skill's scope; use one system, not both on the same decision.
• An epic resists decomposition because nobody can define done → route BACK here: that is an unshaped project, and force-splitting it produces disconnected tasks.
• The work is small, routine, and fully understood — skip shaping overhead; just do it.
• The bet is placed and you need intent-to-delivery control — classification, five-field intent contracts, autonomy gating, failure routing, and resumable status across the run. Route to bmad. Shaping ends at the bet; bmad carries the placed intent through bounded, inspectable, resumable agent work.

Related skills

• product-discovery — upstream: validates the problem before narrowing begins
• product-strategy, product-roadmapping-and-portfolio — strategic context above bets
• product-methodology — downstream consumer of a won bet (prioritization, spec drafting)
• spec-driven-development — optional formal specification of shaped output post-bet
• implementation-planning, subagent-driven-development — execution after the bet; decompose only downhill work, never pre-shred a pitch
• bmad — intent-to-delivery control-plane protocol once the bet is placed: classification, five-field intent contracts, autonomy gating, failure routing, resumable spec status
• work-tracking — where scope/hill state lives during the build
• qa-methodology — edge-case QA as late-cycle level-up, not gate

Evidence note

Every practice claim originates from one company's account (Basecamp, 2019). Independent team records through 2026 show real adaptations and documented abandonments; read references/anti-patterns.md before betting anything that matters, and treat six-week cycles as tunable scaffolding rather than doctrine.