Pm Opportunity To Bet

PM Opportunity to Bet

Use this skill when a PM has more than one opportunity candidate, product signal, request, or problem frame and needs to choose one bounded bet for the next learning or delivery step. It keeps the source, job, alternative, risk, and smallest validation visible before a team spends meaningful time.

The output is a decision packet for review. A bet is a deliberately bounded direction to test or specify; it is not a roadmap commitment, a build result, an adoption claim, or a forecast of business impact.

When to use

Use it for:

• comparing several source-backed problems or opportunity candidates;
• choosing one next product bet after interviews, trend review, feedback, experiment readout, or an AI evaluation;
• making opportunity cost explicit before a design or engineering handoff;
• narrowing a broad AI-product idea into one reversible learning boundary;
• deciding whether the evidence is strong enough to test, specify, hold, or collect more evidence.

Do not use it to:

• manufacture a market, segment, urgency, reach, impact, confidence, effort, or revenue estimate from thin evidence;
• calculate RICE, ICE, WSJF, or another numeric priority score when the inputs are not supplied and methodologically supported;
• turn one request, quote, trend, competitor move, or owner preference into a broad requirement or roadmap commitment;
• hide alternatives, contradictions, missing evidence, or the cost of doing something else;
• create tickets, edit code, change a roadmap, publish a decision, message a customer, or write to an external system automatically;
• replace source analysis, an experiment readout, an outcome metric contract, an AI evaluation plan, or a product specification when those are the next evidence boundary.

Guardrails

• Treat supplied material as the evidence boundary. If a source ID, date, context, user job, outcome, or decision owner is missing, write Not provided, Not verified, or Not measured.
• Give every candidate a stable source mapping. Separate observed, reported, inferred, proposed, and missing evidence; do not let a polished synthesis erase the distinction.
• One signal is a single signal, not market demand, prevalence, urgency, segment proof, or willingness to pay. Repeated qualitative signals still do not provide a rate without a denominator and comparable contexts.
• Keep the user job, current workaround, desired progress, and context visible. A feature request is not the job and an opportunity label is not a problem diagnosis.
• Compare candidates with explicit qualitative criteria such as evidence strength, job clarity, reversibility, learning value, risk, and smallest validation. If a numeric score is supplied by a source, preserve its method and source; never invent missing inputs or present a proposed ordering as a measured result.
• Choose one bet while keeping the strongest alternatives and their opportunity cost visible. Equal evidence can legitimately end in Need evidence or a tie-break validation rather than false certainty.
• Keep a bet boundary: target context, job, proposed mechanism, smallest validation, non-goals, dependencies, and stop or revise rule. A bet does not authorize a build, launch, migration, provider call, or external write.
• Redact names, emails, customer identifiers, private URLs, credentials, tokens, confidential roadmap detail, raw sensitive content, and proprietary prompts before writing the packet.
• For AI-related bets, preserve model/provider/config assumptions, human review, provenance, uncertainty, fallback, cost/latency status, evaluation slices, and rollback questions. Do not treat a model capability or demo as an outcome.
• The skill is tool-free and produces a reviewable handoff only. It does not call a provider, browse, open an issue, send a message, change a roadmap, or perform the validation.

Workflow

1. Frame the decision

Write the decision on the desk, decision owner, user/job, current workaround, time or resource boundary, and what evidence could change the choice. If the decision is missing, keep it as Not provided and do not infer authority.

2. Build the opportunity ledger

Give each candidate a stable ID such as O1, O2, or the supplied ID. Record the source IDs, context, job, observed or reported friction, desired progress, current workaround, requested change, evidence status, and limitation. Keep unlike segments, versions, tasks, or environments in separate rows.

3. Classify the evidence boundary

For each candidate, mark what is source-backed, inferred, proposed, or missing. Use bounded statuses such as single signal, repeated qualitative signal, conflicting signal, missing evidence, or source-supplied score. State what the candidate supports and what it does not prove.

4. State the comparison criteria

Use a short qualitative comparison, not an invented score. Consider:

• evidence traceability and fit to the decision;
• clarity of the user job and current workaround;
• smallest reversible test and expected learning value;
• risk, privacy, safety, dependency, cost, and rollback burden;
• opportunity cost and what would remain unknown if this candidate wins.

If the owner supplied a weighted method, reproduce the inputs, formula, date, and source before using it. Otherwise label the ordering Proposed and keep the rationale inspectable.

5. Choose one bounded bet

Select one candidate or explicitly choose Need evidence. Write the target context, user job, desired progress, proposed mechanism, smallest coherent boundary, and decision owner. Keep alternatives visible with the reason they were deferred; do not call them rejected unless the evidence supports that.

6. Write assumptions, risks, and opportunity cost

List the assumptions that must be true, the evidence that would falsify each one, the risks and dependencies, the cost of doing this instead of each alternative, and the non-goals. Mark product, market, AI, privacy, and operational claims as Proposed or Unknown when they are not evidenced.

7. Design the smallest validation

Define one reversible test, review, prototype comparison, or source-backed follow-up. State audience/context, first action, primary learning signal, unit, guardrail, timebox, evidence capture, owner, and a proposed decision rule. If the outcome or denominator needs design, route to pm-outcome-to-metric; if a measured test already exists, route to pm-experiment-to-readout.

8. Pre-commit stop, revise, or continue

State what would make the owner Continue, Revise, Hold, Need evidence, or Reject the bet. Keep thresholds Proposed unless supplied and verified. Name the safe recovery or rollback path. A validation plan does not prove that the validation ran.

9. Hand off and write back

Give design, engineering, research, and QA the smallest next action and the evidence they must collect. Route a confirmed build boundary to pm-decision-to-spec, a concrete reproducible mismatch to pm-feedback-to-fix, a release learning loop to pm-release-to-learn, and a shareable verified proof packet to pm-proof-to-share.

Output contract

Return the following sections in this order. Keep unsupported fields explicitly Not provided, Unknown, Not measured, Proposed, or Not covered.

Decision on the desk

State the decision, owner, user/job, current workaround, time or resource boundary, and the evidence that could change the decision.

Opportunity set

Use a table with candidate ID, source IDs, context, job or friction, evidence status, what it supports, what it does not prove, and limitation. Keep conflicting contexts separate.

Evidence boundary

Separate observed behavior, reported experience, source-supplied score, inference, proposal, missing evidence, and any prior result. State the method, date/version, denominator, and provenance when supplied; otherwise mark them as not provided.

User job and bet

Describe the selected context, trigger, user job, desired progress, current alternative, proposed mechanism, smallest boundary, and decision owner. State why the bet is the most useful next learning step without pretending it is a validated market priority.

Assumptions and risks

List each assumption, evidence that would test or falsify it, status, owner, and product, AI, privacy, safety, dependency, cost, latency, accessibility, or operational risk that applies.

Opportunity cost and non-goals

Name the visible alternatives, what is deferred by choosing the bet, cost of doing nothing, and Should-not-build items. Do not hide a deferred candidate inside a feature list.

Smallest validation

State the test or review, audience/context, first action, primary signal, unit, guardrail, timebox, evidence capture, owner, version/environment boundary, and proposed decision rule. Mark execution Not run until fresh evidence exists.

Stop, revise, or continue rule

Define the conditions for Continue, Revise, Hold, Need evidence, or Reject, the threshold status, and the safe recovery or rollback action. Keep the action reversible and human-owned.

Not covered

List unsupported market size, prevalence, segment, urgency, business impact, revenue, adoption, traffic, stars, retention, quality, AI performance, provider behavior, cost, latency, safety, accessibility, localization, versions, environments, or rollback execution.

Implementation handoff

Give the authorized owner one smallest next action, affected surfaces, source and privacy review, acceptance or learning evidence to collect, writeback location, and the follow-on skill. A handoff is not a ticket or proof that implementation occurred.

Review ask

Ask for exactly one decision: Continue, Revise, Hold, Need evidence, or Reject. Name the unresolved evidence or risk the reviewer must correct.

Edge cases

• Only one candidate: keep it as the only visible option, state that no comparison was possible, and use a smallest validation rather than calling it the priority.
• Conflicting signals: preserve the contexts and choose Need evidence or a tie-break validation when the conflict changes the bet.
• Equal candidates: state the tie, compare reversibility and learning value, and choose one smallest tie-break test or ask the owner to decide.
• Feature request without a confirmed problem: record the request as reported evidence and route back to discovery; keep it out of Must-have.
• No usable evidence: return Need evidence, define the smallest safe collection step, and do not create a polished priority list.
• Synthetic or fictional input: label the entire output a fictional fixture; it can test the packet but cannot support a real bet, adoption, or growth claim.
• Source-supplied numeric score: preserve source, method, inputs, and date; do not fill missing values or compare it with an invented score.
• High-impact or irreversible bet: require human approval, narrow exposure, explicit consent, a visible fallback, and a tested rollback before action.
• AI provider or model change: route to an evaluation plan with fixed slices, provenance, fallback, cost/latency status, and release evidence.
• Unknown opportunity cost: write Not provided, identify the missing decision owner or alternative, and avoid claiming an optimal choice.
• No safe validation: hold the bet and define what evidence or permission is required before testing.

Final check

Before handoff, confirm that:

• every candidate has source IDs, context, evidence status, and limitations;
• observed, reported, inferred, proposed, and missing evidence remain separate;
• no unsourced numeric score, market claim, segment claim, or growth promise was added;
• one bet or Need evidence is explicit while alternatives and opportunity cost remain visible;
• the user job, boundary, assumptions, risks, non-goals, and smallest validation are concrete;
• the validation has a primary signal, unit, guardrail, timebox, owner, and proposed decision rule;
• stop/revise/continue and safe recovery are stated;
• privacy, AI trust, accessibility, and high-impact boundaries are addressed when relevant;
• the output ends with Not covered and exactly one review decision.