| name | prioritize-backlog |
| description | Rank opportunities or backlog items by evidence-backed Value and Confidence bands, storing exact 0-100 scores for audit. Use when the user says "prioritize my backlog", "rank these", "what should we build next", "score these opportunities", or hands over a list of problems/features to sequence. This is the Rank opportunities outcome, distinct from updating a roadmap's Now/Next/Later. Falls back to proxies when data is thin and flags lower confidence. |
Prioritize Backlog
Score and rank a set of problems or opportunities so the user knows what to build next and why. Produce a defensible ranking with explicit reasoning, not a gut order. In chat, show bands and recommendations first; store exact numbers in scope files.
Workflow
1. Gather the items and their context
Take the list to rank (from the user, from problem-framed themes, or from .product-eval/<scope>/). Read .product-eval/<scope>/context.md for the north-star metric and personas, and .product-eval/<scope>/sources.md for the confidence ceiling. If business context is missing, ask only the few questions needed to score (north star, primary personas) and proceed.
Preflight (data contract). When ranking themes already written to .product-eval/<scope>/, validate themes.md against the conformance preflight in DATA-CONTRACT.md (Validation) first (members resolve to real evidence, total_weight matches the members, confidence_band is consistent with confidence). On a failure, stop and report the file, field, and violation rather than scoring malformed input.
2. Score Value per item
Using references/scoring-framework.md, score four dimensions (each 0-25, summed). Store the exact 0-100 value and value_band; show the band in chat.
- Persona weight: buyer vs admin vs end-user vs edge case.
- Funnel location: retention/activation rank highest; advocacy lowest.
- Frequency: how often the affected users hit this.
- Competitive pressure: from "customers leaving" down to "no pressure".
When first-party data is missing, use the proxies in the reference (complaints as a drop-off proxy, sales objections as a conversion proxy, competitor investment as a gap proxy) and mark those scores as lower confidence.
3. Score Confidence per item, report as a band
Confidence reflects how well-supported the item is by evidence: count of independent evidence items, diversity of source types, their strength (1-5), recency, and how much is first-party. Store the exact 0-100 confidence for audit and calibration, but present confidence_band in chat (Low / Moderate / High). Respect the ceiling from .product-eval/<scope>/sources.md. Signal strength is an input to Confidence, not a separate verdict.
4. Apply the recommendation matrix
Map each item's (Value, Confidence) to a recommendation using the matrix in the reference: Decide now / Run a research sprint / Do not commit yet / Deprioritize. High value + low confidence means "research more", not "drop", keep these visible.
5. Rank and present
Order by recommendation tier first, then Value band within tier. Present a scannable ranked table: item, Value band, Confidence band, recommendation, one-line rationale. Call out the few items worth acting on now and the few to deprioritize. Put exact point scores only in an audit note or the scope file.
6. Record and offer next step
Write .product-eval/<scope>/scores.md with exact scores, bands, and reasoning, and log the ranking in .product-eval/<scope>/decisions-log.md, using the score and decisions-log schemas in the data contract (DATA-CONTRACT.md). Offer to decide on the top item or pressure-test the resulting shortlist. Honor user score overrides, but require a one-sentence reason and log it.
Output
A ranked table with bands, recommendations, and a short "act on these / hold these" summary. Keep exact math and internal paths out of the chat unless asked. End with Next move: and recommend deciding on the top item, pressure-testing the shortlist, rendering a scorecard, or writing the report the user asked for.
