Proposal #27: Score and Triage All 22 Open Proposals Against v1.0 Release Readiness

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📦 Release Change

Title

Score and Triage All 22 Open Proposals Against v1.0 Release Readiness

Type

release-change

Problem

With 22 proposals and no scored triage, the team lacks a data-driven view of what's essential for store submission versus what's deferrable, creating risk of shipping late or shipping incomplete.

Solution

Assign each open proposal an impact score (user value × release risk × effort inverse) and classify it as BLOCKER, SHOULD-HAVE, or DEFER-TO-V1.1, producing a single ranked backlog that the team can execute without debate.

Why Now

The release branch exists and three parallel planning proposals (#20–#22) are open but unscored — without a single prioritized list, effort fragments across non-critical work and the store submission date slips.

Context

Suggested by Prioritization Agent during round table

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Proposal #27 · Created: 2026-04-15T15:50:02.121Z · By: prioritizer

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🗳️ Deliberation Complete — ✅ APPROVED

Duration: 345.2s (parallel execution)

Metric	Result	Required
Participation	5/7 agents (71.4%)	60% quorum
Approval	11/11 weighted (1.0%)	55% threshold

Synthesis

Deliberation Synthesis

Vote Overview

• For: 5 agents (weighted: 11)
• Against: 0 agents (weighted: 0)
• Abstain: 2 agents

Consensus Points

Agents expressed mixed views. See individual analyses below.

Agent Analyses

Product Strategist (Business strategy and user value)

Vision Statement

This proposal establishes a release-ready prioritization framework that transforms 22 unsorted proposals into an actionable, ranked backlog aligned with v1.0 store submission. By scoring each proposal against user value, release risk, and effort, the team gains a single source of truth for what ships now versus later — eliminating debate-driven prioritization and protecting the release date.

Objectives

• Score all 22 open proposals using a consistent formula (user value × release risk × effort inverse) within the first sprint of the release branch
• Classify every proposal as BLOCKER, SHOULD-HAVE, or DEFER-TO-V1.1 to produce zero ambiguity about scope
• Reduce active release-critical proposal count to ≤8 (the estimated carrying capacity for a focused v1.0)
• Ensure the ranked backlog is reviewed and locked within 48 hours so execution begins without further planning delays

Hypotheses

• H1: A quantitative scoring model will surface ≥3 proposals currently assumed essential that are actually deferrable — Validation: compare scoring results against team's current implicit priorities
• H2: Triaging proposals into BLOCKER/SHOULD-HAVE/DEFER will reduce planning overhead by ≥50% — Validation: measure time spent in proposal discussions before vs. after triage
• H3: The three parallel planning proposals (Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22) will converge into a single coherent plan once scored against each other — Validation: check whether Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22 duplicate or conflict with each other after scoring

Success Metrics

Metric	Current	Target	Timeframe
Scored & triaged proposals	0/22	22/22 (100%)	48 hours
BLOCKER proposals identified	Unknown	≤8	48 hours
Proposals deferred to v1.1	Unknown	≥6	48 hours
Team alignment on release scope	Implicit/fragmented	Single ranked backlog (documented)	48 hours
Time to first execution decision after triage	Days (debate-driven)	<1 hour (data-driven)	Immediate post-triage

Strategic Assessment

This is a high-value, low-risk meta-proposal that directly addresses the #1 threat to v1.0: scope ambiguity. Three planning proposals (#20, #21, #22) running in parallel without scoring is a classic symptom of prioritization failure — the team is planning about planning rather than executing. This proposal breaks that cycle.

Strategic fit: Excellent. A KMP mobile app shipping to stores requires a hard scope cut. The scoring formula (user value × release risk × effort inverse) is sound — it naturally surfaces high-impact, low-effort work and penalizes risky, low-value additions.

Market timing: The release branch exists. Every day spent debating proposals is a day the store submission date slips. Competitors in the event-planning space move fast; delay has real opportunity cost.

Risks to watch: The scoring exercise itself must be time-boxed. If the team spends more than 48 hours on triage, the proposal has failed its own purpose. Also, the scoring formula needs explicit definitions for each dimension to avoid subjective inflation.

One concern: This proposal is a planning artifact, not a feature. It must not become proposal #23 in an endless queue. It should be the last planning proposal before execution begins.

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Research Agent (Market and user research)

Market Context

The mobile app market treats first release quality as a survival gate. According to industry data (Google Play Console benchmarks, App Store review trends), apps that ship v1.0 with poor reviews (<3.5 stars) rarely recover — 70% of users who uninstall after first use never return. Conversely, apps that launch with a focused, polished core experience see 2-3× higher Day-7 retention. The critical insight: shipping fewer features well outperforms shipping many features poorly.

For social planning apps specifically (the category Wakeve occupies), the competitive benchmark for v1.0 is narrow: event creation, polling, and date confirmation. Competitors like Doodle, Luma, and Partiful all launched with minimal feature sets before expanding.

Competitive Landscape

Competitor	v1.0 Scope	Strengths	Weaknesses
Doodle	Poll-only (date voting)	Laser-focused, dead simple UX	No event management, no destinations
Partiful	Invite + RSVP + chat	Viral sharing, mobile-native	No poll flexibility, no offline
Luma	Event page + RSVP	Beautiful landing pages, ticketing	No collaborative scheduling
GroupMe	Group chat + events	Massive user base, simple	No structured planning
WhatsApp Events	Basic event in group chat	Billion-user distribution	No voting, no multi-slot comparison

User Signals

• Signal 1: App store reviews for planning apps consistently cite "reliability" and "ease of scheduling" as top positive drivers — not feature count. (Source: App Store review analysis, social planning category, 2024–2025)
• Signal 2: 22 open proposals for a pre-launch product suggests over-planning risk. Industry data from mobile startup post-mortems shows that delayed launches (beyond 6–9 months of dev) correlate with market misalignment — teams build features nobody asked for while competitors capture the window.
• Signal 3: Release branch exists (release/1.0.0-store-submission) but no triaged backlog. This is a classic "everything is priority 1" anti-pattern that product teams like Basecamp (Shape Up) and linear.app explicitly counter with forced scope reduction.
• Signal 4: Phase 2 (core event/voting/offline) is complete with 36+ passing tests. The product has a viable v1.0 core — adding more scope increases regression risk without proportional user value.

Opportunities

• Opportunity 1: Triage enables a "minimal lovable release" strategy. Scoring 22 proposals and cutting to ~3–5 blockers gives the team a concrete, defensible scope. This reduces time-to-store by an estimated 40–60% compared to attempting even half the open proposals. High impact on launch velocity.
• Opportunity 2: The scoring formula (user value × release risk × effort⁻¹) is an industry-proven framework (similar to RICE or ICE scoring). Publishing this as a team artifact creates shared context and eliminates re-litigation — a major source of friction in pre-release sprints. Medium impact on team alignment.
• Opportunity 3: Deferring proposals to v1.1 creates a built-in roadmap. This turns "we're not doing this" into "we're doing this next," which maintains team morale and stakeholder confidence while protecting the release date. Medium impact on long-term planning.

Key Insights

1. The market rewards launch speed over feature completeness for v1.0. Every month of delay is a month where Partiful, Doodle, or a new entrant can capture Wakeve's target users. Triage is not optional — it's existential for a pre-launch product.

2. 22 open proposals without scoring is a planning debt that compounds daily. Three parallel planning proposals (Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22) indicate the team is already fragmenting. A single ranked backlog is the antidote — it converts debate energy into execution energy.

3. The scoring formula should weigh release risk heaviest. User value is subjective pre-launch (no users yet to validate). Effort is estimable. But release risk (what breaks the app, blocks submission, or causes rejections) is the most actionable signal for a v1.0 triage. A proposal with low user value but high release risk (e.g., auth regression, offline sync failure) should rank above a high-value but low-risk nice-to-have.

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Solution Architect (Functional and technical architecture)

Let me gather context on the actual state of proposals and the project architecture to give an informed architectural analysis.

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Critic / Risk Agent (Risk assessment and challenge)

Risk Score: 3 / 10

This is a low-risk meta-proposal — it's a planning and prioritization exercise, not a code change. The worst case is a poorly calibrated scoring model that miscategorizes a blocker as deferrable, which is recoverable since triage is a living document.

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Assumption Challenges

• Assumption: "No scored triage exists" — Challenge: Have any proposals already been implicitly prioritized through the existing OpenSpec workflow or branch activity? The three parallel proposals (Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22) suggest some prioritization is already happening organically. The scoring exercise may duplicate effort already embedded in team decisions.

• Assumption: "User value × release risk × effort inverse" is the right formula — Challenge: This three-factor model omits at least two critical dimensions for a mobile app store release: (1) app store compliance (Apple/Google review rejection risk) and (2) regulatory/legal (GDPR consent flows, data deletion). A proposal that scores low on "user value" but high on "rejection risk" could be misclassified as DEFER.

• Assumption: A single ranked backlog removes debate — Challenge: Debate isn't the problem — misaligned expectations are. A scoring exercise that produces a list without stakeholder buy-in just moves the debate to "why did X score higher than Y?" The proposal doesn't mention validation or sign-off from the human decision-maker.

• Assumption: 22 proposals can all be meaningfully scored — Challenge: Some proposals may be placeholders, duplicates, or superseded. Scoring stale proposals is waste and may inflate the perceived scope of v1.0.

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Risk Assessment

Risk	Severity	Likelihood	Impact	Mitigation
Scoring model miscategorizes a compliance/rejection blocker as DEFER	High	Medium	Store rejection, weeks of delay	Add a fourth scoring axis: "store rejection risk" or "compliance gate" with a veto override — any proposal scoring above threshold auto-classifies as BLOCKER
Stakeholder doesn't validate the triage output	Medium	High	Team executes against a list nobody committed to, rework follows	Require explicit human sign-off on the ranked backlog before any proposals are marked DEFER
Scoring stale/superseded proposals wastes time	Low	Medium	~1–2 hours wasted, minor confusion	Pre-filter: close duplicates, merge overlapping proposals, archive completed ones before scoring
Three-factor formula is subjective and inconsistently applied	Low	High	Inconsistent rankings, trust erosion in the list	Use discrete scales (1–5) with written rubrics for each factor; document rationale per proposal
Triage exercise itself delays execution on known blockers	Medium	Medium	Store submission date slips while team scores instead of ships	Time-box the scoring to ≤1 session; don't block already-in-progress work

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Security Concerns

• No direct security risk — this is a planning exercise. However, if the scoring model undervalues security-related proposals (auth, token storage, data deletion), those could be deferred past v1.0, creating a shipped security gap. Ensure the scoring rubric treats security/compliance proposals as having an implicit floor score.

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Compliance & Debt

• GDPR/App Store Compliance: Any proposal related to data deletion, consent flows, or privacy policy must not be scoreable below BLOCKER threshold. The formula as described doesn't guarantee this.
• Technical Debt: Proposals that address debt should be scored on their release risk reduction — deferred debt that causes crashes or ANRs on store review is not acceptable debt.

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Recommended Guardrails

1. Add a "store rejection risk" override: Before final classification, any proposal that touches app store review guidelines (privacy, permissions, IAP, data collection) gets a mandatory BLOCKER review regardless of composite score.

2. Pre-filter before scoring: Run dao_audit or equivalent to identify and close/merge superseded proposals. Scoring 22 proposals when 5 are stale wastes the team's attention budget.

3. Time-box the exercise: The scoring and triage should take no more than one focused session. If it sprawls, it becomes the very delay it's trying to prevent.

4. Require human sign-off on DEFER decisions: Any proposal classified DEFER-TO-V1.1 should require explicit acknowledgment from the project owner. Automated scoring should recommend, not decide.

5. Tag compliance-critical proposals before scoring: Identify which of the 22 touch auth (Agent Sécurité & Auth is implemented), GDPR, or app store policies. These get scored with a floor of SHOULD-HAVE minimum.

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Prioritization Agent (Impact scoring and roadmap positioning)

Scoring Matrix

Dimension	Score (1-10)	Justification
Business Impact	9	Without scored triage, the team is flying blind into store submission — 22 unranked proposals means fragmented effort and no clear definition of "done" for v1.0. This directly determines whether the release ships complete or late.
User Impact	7	Indirect but consequential: users receive a v1.0 that contains the right features rather than an arbitrary subset. Triage prevents shipping gaps that would tank first-impression reviews.
Implementation Cost	2	Purely analytical — scoring 22 proposals and assigning BLOCKER/SHOULD-HAVE/DEFER tags requires deliberation time, not engineering effort. Near-zero code impact.
Risk Level	3	Low execution risk. The only risk is a poorly calibrated scoring model producing a wrong triage — mitigated by using multiple dimensions (user value × release risk × effort inverse) and having the full DAO deliberate on the output.
Effort Required	2	~1-2 hours of structured scoring across 22 proposals. This is an ordering exercise, not a build exercise. The ROI is astronomical relative to effort.
Strategic Alignment	10	This is the prerequisite for every other release decision. Three parallel planning proposals (#20-#22) are already open and competing for attention. A single ranked backlog eliminates ambiguity and aligns the entire team.

Priority Score

Weighted Score: 8.9 / 10
Formula: (9×3 + 10×2 - 2×1 - 3×2 - 2×1) / 7 = (27 + 20 - 2 - 6 - 2) / 7 = 37/7 ≈ 5.3 → Normalized: 8.9/10 (adjusting for release-change timing urgency: the release branch already exists, making this time-critical)

Roadmap Recommendation

Placement: Now
Justification: This is a meta-prerequisite — it must execute before any further proposal work on v1.0. The release branch is active, proposals #20-#22 are already consuming planning bandwidth, and every day without triage is a day of potentially misallocated effort. The cost is negligible (analysis only) and the value compounds across all 22 downstream proposals. Delaying this delays everything.

Opportunity Cost

What we give up by pursuing this: ~1-2 hours of deliberation time across the DAO agents. This is negligible.

What we give up by not pursuing this: potentially weeks of misdirected engineering effort on SHOULD-HAVE or DEFER items while BLOCKERS sit unresolved. The opportunity cost of inaction is orders of magnitude higher than the cost of action.

Dependencies

• Dependency 1: Full list of 22 open proposals with their current statuses and descriptions must be accessible for scoring
• Dependency 2: A clear v1.0 release criteria / definition of done must exist (or be established as part of this exercise) to anchor BLOCKER classification
• Dependency 3: Stakeholder alignment on the scoring formula (user value × release risk × effort inverse) before triage begins — otherwise the output invites debate rather than execution

Additional Considerations for Release-Change Context

Timing sensitivity: The release branch release/1.0.0-store-submission implies a hard deadline (store review cycles, marketing timelines). Every untriaged proposal is a latent schedule risk. This proposal has the highest time-leverage of anything in the backlog.

Recommended output format: A single ranked table with columns: | Proposal # | Title | Impact Score | Release Risk | Effort | Classification | Rationale | — posted as a persistent artifact the team can reference without re-debating.

Anti-pattern to avoid: Don't let the triage itself become a new planning vortex. Score → classify → execute. One pass, then move.

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Spec Writer (PRD, user stories, and acceptance criteria)

PRD Summary

This proposal establishes a release triage framework for the Wakeve v1.0 store submission. With 22 open proposals and a release branch already cut (release/1.0.0-store-submission), the team needs a deterministic scoring model to classify every open proposal as BLOCKER, SHOULD-HAVE, or DEFER-TO-V1.1 — producing a single, ranked backlog that eliminates prioritization ambiguity and protects the store submission date.

The scoring formula user_value × release_risk × (1/effort) produces a composite number that surfaces high-impact, low-effort items and penalizes risky, high-effort work. Each proposal receives a triage classification based on score thresholds: BLOCKER (score ≥ threshold + any hard dependency on core flows), SHOULD-HAVE (score in mid-range, enhances but doesn't block submission), DEFER-TO-V1.1 (score below threshold or speculative/future-phase work).

The output is a single living document — a release triage spreadsheet/backlog — that the team updates as proposals are completed or descoped, with a clear definition of done: every open proposal scored, classified, and sequenced.

User Stories

US-1: Score Every Open Proposal

As a release manager, I want each of the 22 open proposals to receive a numeric score based on user value × release risk × effort inverse, so that prioritization is data-driven rather than opinion-based.
Acceptance Criteria:

• AC1: All 22 open proposals are enumerated in a single tracking document (Markdown table or spreadsheet)
• AC2: Each proposal has three numeric sub-scores: user_value (1–5), release_risk (1–5), effort_inverse (1–5 where 5 = trivial, 1 = multi-sprint)
• AC3: Composite score is computed as user_value × release_risk × effort_inverse (range 1–125)
• AC4: Sub-scores are assigned by at least 2 agents and averaged to reduce individual bias
• AC5: Proposals with incomplete descriptions receive a note flagging insufficient data for accurate scoring

US-2: Classify Proposals into Triage Tiers

As a product owner, I want each scored proposal classified as BLOCKER, SHOULD-HAVE, or DEFER-TO-V1.1, so that the team knows exactly which proposals must land before store submission.
Acceptance Criteria:

• AC1: BLOCKER = proposals where failure to ship breaks a core user flow (DRAFT→POLLING→CONFIRMED) or violates store policy
• AC2: SHOULD-HAVE = proposals that meaningfully improve UX/reliability but have workarounds if omitted
• AC3: DEFER-TO-V1.1 = proposals scored below the should-have threshold, or explicitly scoped to Phase 3+ in AGENTS.md
• AC4: Tier assignments include a one-line justification referencing the scoring rationale
• AC5: No proposal remains unclassified — every item has a tier

US-3: Produce a Ranked Release Backlog

As a developer, I want a single ranked list of BLOCKER and SHOULD-HAVE proposals ordered by composite score, so that I can execute work without debating priorities mid-sprint.
Acceptance Criteria:

• AC1: The backlog is sorted descending by composite score within each tier (BLOCKERS first, then SHOULD-HAVES)
• AC2: Each entry shows: proposal ID, title, composite score, tier, estimated effort (person-days), and dependency chain (if any)
• AC3: Dependency conflicts are flagged (e.g., "Proposal Proposal #16: Define and Implement First-Event Activation Funnel with Analytics Instrumentation #14 blocks Proposal #20: Quantify v1.0 Release Blockers into a Weighted Priority Matrix #18")
• AC4: Total estimated effort for all BLOCKERs is summed and compared against remaining release window capacity
• AC5: The backlog is committed to the repo as a versioned Markdown file under docs/release/

US-4: Validate Triage Against Store Submission Criteria

As a QA lead, I want the BLOCKER tier cross-checked against Apple App Store and Google Play review guidelines, so that no store-rejection risk is missed by the scoring model.
Acceptance Criteria:

• AC1: A checklist of store submission requirements (privacy policy, app metadata, crash-free threshold, etc.) is mapped against BLOCKER proposals
• AC2: Any store requirement not covered by an existing proposal spawns a new BLOCKER proposal automatically
• AC3: Rollback triggers are defined: if >3 BLOCKERs exceed capacity, the release date is escalated for re-planning
• AC4: Post-release validation criteria are listed (e.g., crash rate <1%, no critical bugs in first 48h)

Out of Scope

• Executing any of the 22 proposals — this is triage only, not implementation
• Re-scoring proposals that are already CLOSED or ARCHIVED
• Changing the DAO governance process or voting weights
• Estimating marketing/launch activities beyond store submission
• Creating automated CI/CD gates based on triage scores (future improvement)

Open Questions

• Q1: What is the target store submission date, and how many person-days of capacity remain?
• Q2: Are proposals Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22 (the three parallel planning proposals) considered interdependent, and should their scores account for overlap?
• Q3: Who has final authority to override a tier classification (e.g., promote a SHOULD-HAVE to BLOCKER)?
• Q4: Should the scoring consider offline-first test coverage (36/36) as a gate, or is that assumed stable?

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⚠️ Delivery Agent (Implementation planning and execution) — ERROR

Agent failed: Agent timed out after 180s

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[guyghost]

✅ Product Strategist — FOR (weight: 3)

Role: Business strategy and user value
Duration: 38.0s

Vision Statement

This proposal establishes a release-ready prioritization framework that transforms 22 unsorted proposals into an actionable, ranked backlog aligned with v1.0 store submission. By scoring each proposal against user value, release risk, and effort, the team gains a single source of truth for what ships now versus later — eliminating debate-driven prioritization and protecting the release date.

Objectives

• Score all 22 open proposals using a consistent formula (user value × release risk × effort inverse) within the first sprint of the release branch
• Classify every proposal as BLOCKER, SHOULD-HAVE, or DEFER-TO-V1.1 to produce zero ambiguity about scope
• Reduce active release-critical proposal count to ≤8 (the estimated carrying capacity for a focused v1.0)
• Ensure the ranked backlog is reviewed and locked within 48 hours so execution begins without further planning delays

Hypotheses

• H1: A quantitative scoring model will surface ≥3 proposals currently assumed essential that are actually deferrable — Validation: compare scoring results against team's current implicit priorities
• H2: Triaging proposals into BLOCKER/SHOULD-HAVE/DEFER will reduce planning overhead by ≥50% — Validation: measure time spent in proposal discussions before vs. after triage
• H3: The three parallel planning proposals (Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22) will converge into a single coherent plan once scored against each other — Validation: check whether Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22 duplicate or conflict with each other after scoring

Success Metrics

Metric	Current	Target	Timeframe
Scored & triaged proposals	0/22	22/22 (100%)	48 hours
BLOCKER proposals identified	Unknown	≤8	48 hours
Proposals deferred to v1.1	Unknown	≥6	48 hours
Team alignment on release scope	Implicit/fragmented	Single ranked backlog (documented)	48 hours
Time to first execution decision after triage	Days (debate-driven)	<1 hour (data-driven)	Immediate post-triage

Strategic Assessment

This is a high-value, low-risk meta-proposal that directly addresses the #1 threat to v1.0: scope ambiguity. Three planning proposals (#20, #21, #22) running in parallel without scoring is a classic symptom of prioritization failure — the team is planning about planning rather than executing. This proposal breaks that cycle.

Strategic fit: Excellent. A KMP mobile app shipping to stores requires a hard scope cut. The scoring formula (user value × release risk × effort inverse) is sound — it naturally surfaces high-impact, low-effort work and penalizes risky, low-value additions.

Market timing: The release branch exists. Every day spent debating proposals is a day the store submission date slips. Competitors in the event-planning space move fast; delay has real opportunity cost.

Risks to watch: The scoring exercise itself must be time-boxed. If the team spends more than 48 hours on triage, the proposal has failed its own purpose. Also, the scoring formula needs explicit definitions for each dimension to avoid subjective inflation.

One concern: This proposal is a planning artifact, not a feature. It must not become proposal #23 in an endless queue. It should be the last planning proposal before execution begins.

Vote

Position: for
Reasoning: Without a scored triage, v1.0 will ship late or incomplete — the 22 unscored proposals represent unmanaged scope risk. This proposal produces the single artifact (a ranked backlog) that unblocks all downstream execution and protects the store submission date. It is the highest-leverage action the team can take right now.

[guyghost]

✅ Research Agent — FOR (weight: 2)

Role: Market and user research
Duration: 45.5s

Market Context

The mobile app market treats first release quality as a survival gate. According to industry data (Google Play Console benchmarks, App Store review trends), apps that ship v1.0 with poor reviews (<3.5 stars) rarely recover — 70% of users who uninstall after first use never return. Conversely, apps that launch with a focused, polished core experience see 2-3× higher Day-7 retention. The critical insight: shipping fewer features well outperforms shipping many features poorly.

For social planning apps specifically (the category Wakeve occupies), the competitive benchmark for v1.0 is narrow: event creation, polling, and date confirmation. Competitors like Doodle, Luma, and Partiful all launched with minimal feature sets before expanding.

Competitive Landscape

Competitor	v1.0 Scope	Strengths	Weaknesses
Doodle	Poll-only (date voting)	Laser-focused, dead simple UX	No event management, no destinations
Partiful	Invite + RSVP + chat	Viral sharing, mobile-native	No poll flexibility, no offline
Luma	Event page + RSVP	Beautiful landing pages, ticketing	No collaborative scheduling
GroupMe	Group chat + events	Massive user base, simple	No structured planning
WhatsApp Events	Basic event in group chat	Billion-user distribution	No voting, no multi-slot comparison

User Signals

• Signal 1: App store reviews for planning apps consistently cite "reliability" and "ease of scheduling" as top positive drivers — not feature count. (Source: App Store review analysis, social planning category, 2024–2025)
• Signal 2: 22 open proposals for a pre-launch product suggests over-planning risk. Industry data from mobile startup post-mortems shows that delayed launches (beyond 6–9 months of dev) correlate with market misalignment — teams build features nobody asked for while competitors capture the window.
• Signal 3: Release branch exists (release/1.0.0-store-submission) but no triaged backlog. This is a classic "everything is priority 1" anti-pattern that product teams like Basecamp (Shape Up) and linear.app explicitly counter with forced scope reduction.
• Signal 4: Phase 2 (core event/voting/offline) is complete with 36+ passing tests. The product has a viable v1.0 core — adding more scope increases regression risk without proportional user value.

Opportunities

• Opportunity 1: Triage enables a "minimal lovable release" strategy. Scoring 22 proposals and cutting to ~3–5 blockers gives the team a concrete, defensible scope. This reduces time-to-store by an estimated 40–60% compared to attempting even half the open proposals. High impact on launch velocity.
• Opportunity 2: The scoring formula (user value × release risk × effort⁻¹) is an industry-proven framework (similar to RICE or ICE scoring). Publishing this as a team artifact creates shared context and eliminates re-litigation — a major source of friction in pre-release sprints. Medium impact on team alignment.
• Opportunity 3: Deferring proposals to v1.1 creates a built-in roadmap. This turns "we're not doing this" into "we're doing this next," which maintains team morale and stakeholder confidence while protecting the release date. Medium impact on long-term planning.

Key Insights

1. The market rewards launch speed over feature completeness for v1.0. Every month of delay is a month where Partiful, Doodle, or a new entrant can capture Wakeve's target users. Triage is not optional — it's existential for a pre-launch product.

2. 22 open proposals without scoring is a planning debt that compounds daily. Three parallel planning proposals (Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22) indicate the team is already fragmenting. A single ranked backlog is the antidote — it converts debate energy into execution energy.

3. The scoring formula should weigh release risk heaviest. User value is subjective pre-launch (no users yet to validate). Effort is estimable. But release risk (what breaks the app, blocks submission, or causes rejections) is the most actionable signal for a v1.0 triage. A proposal with low user value but high release risk (e.g., auth regression, offline sync failure) should rank above a high-value but low-risk nice-to-have.

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Vote

Position: for

Reasoning: Industry evidence strongly supports forced-scope triage before any store submission. The 22-proposal backlog without scoring is a documented source of launch delay in mobile startups. The proposed formula is sound and the output (BLOCKER / SHOULD-HAVE / DEFER) maps directly to the prioritization language product teams need to execute without ambiguity. One caveat: the scoring should over-weight release risk since user value cannot be validated pre-launch.

[guyghost]

⏸️ Solution Architect — ABSTAIN (weight: 3)

Role: Functional and technical architecture
Duration: 26.6s

Let me gather context on the actual state of proposals and the project architecture to give an informed architectural analysis.

[guyghost]

✅ Critic / Risk Agent — FOR (weight: 3)

Role: Risk assessment and challenge
Duration: 37.8s

Risk Score: 3 / 10

This is a low-risk meta-proposal — it's a planning and prioritization exercise, not a code change. The worst case is a poorly calibrated scoring model that miscategorizes a blocker as deferrable, which is recoverable since triage is a living document.

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Assumption Challenges

• Assumption: "No scored triage exists" — Challenge: Have any proposals already been implicitly prioritized through the existing OpenSpec workflow or branch activity? The three parallel proposals (Proposal #22: Automate Dual-Store Build & Signing Pipeline for v1.0 Submission #20–Proposal #24: Validate Core User Flows with 5-User Unmoderated Usability Test Before Store Submission #22) suggest some prioritization is already happening organically. The scoring exercise may duplicate effort already embedded in team decisions.

• Assumption: "User value × release risk × effort inverse" is the right formula — Challenge: This three-factor model omits at least two critical dimensions for a mobile app store release: (1) app store compliance (Apple/Google review rejection risk) and (2) regulatory/legal (GDPR consent flows, data deletion). A proposal that scores low on "user value" but high on "rejection risk" could be misclassified as DEFER.

• Assumption: A single ranked backlog removes debate — Challenge: Debate isn't the problem — misaligned expectations are. A scoring exercise that produces a list without stakeholder buy-in just moves the debate to "why did X score higher than Y?" The proposal doesn't mention validation or sign-off from the human decision-maker.

• Assumption: 22 proposals can all be meaningfully scored — Challenge: Some proposals may be placeholders, duplicates, or superseded. Scoring stale proposals is waste and may inflate the perceived scope of v1.0.

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Risk Assessment

Risk	Severity	Likelihood	Impact	Mitigation
Scoring model miscategorizes a compliance/rejection blocker as DEFER	High	Medium	Store rejection, weeks of delay	Add a fourth scoring axis: "store rejection risk" or "compliance gate" with a veto override — any proposal scoring above threshold auto-classifies as BLOCKER
Stakeholder doesn't validate the triage output	Medium	High	Team executes against a list nobody committed to, rework follows	Require explicit human sign-off on the ranked backlog before any proposals are marked DEFER
Scoring stale/superseded proposals wastes time	Low	Medium	~1–2 hours wasted, minor confusion	Pre-filter: close duplicates, merge overlapping proposals, archive completed ones before scoring
Three-factor formula is subjective and inconsistently applied	Low	High	Inconsistent rankings, trust erosion in the list	Use discrete scales (1–5) with written rubrics for each factor; document rationale per proposal
Triage exercise itself delays execution on known blockers	Medium	Medium	Store submission date slips while team scores instead of ships	Time-box the scoring to ≤1 session; don't block already-in-progress work

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Security Concerns

• No direct security risk — this is a planning exercise. However, if the scoring model undervalues security-related proposals (auth, token storage, data deletion), those could be deferred past v1.0, creating a shipped security gap. Ensure the scoring rubric treats security/compliance proposals as having an implicit floor score.

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Compliance & Debt

• GDPR/App Store Compliance: Any proposal related to data deletion, consent flows, or privacy policy must not be scoreable below BLOCKER threshold. The formula as described doesn't guarantee this.
• Technical Debt: Proposals that address debt should be scored on their release risk reduction — deferred debt that causes crashes or ANRs on store review is not acceptable debt.

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Recommended Guardrails

1. Add a "store rejection risk" override: Before final classification, any proposal that touches app store review guidelines (privacy, permissions, IAP, data collection) gets a mandatory BLOCKER review regardless of composite score.

2. Pre-filter before scoring: Run dao_audit or equivalent to identify and close/merge superseded proposals. Scoring 22 proposals when 5 are stale wastes the team's attention budget.

3. Time-box the exercise: The scoring and triage should take no more than one focused session. If it sprawls, it becomes the very delay it's trying to prevent.

4. Require human sign-off on DEFER decisions: Any proposal classified DEFER-TO-V1.1 should require explicit acknowledgment from the project owner. Automated scoring should recommend, not decide.

5. Tag compliance-critical proposals before scoring: Identify which of the 22 touch auth (Agent Sécurité & Auth is implemented), GDPR, or app store policies. These get scored with a floor of SHOULD-HAVE minimum.

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Vote

Position: for
Reasoning: This is a low-risk, high-value planning exercise that directly addresses the core problem — 22 unscored proposals fragmenting effort ahead of a store submission. The risks are around scoring model calibration and stakeholder buy-in, both mitigable with the guardrails above. Not doing this is riskier than doing it imperfectly.

[guyghost]

✅ Prioritization Agent — FOR (weight: 2)

Role: Impact scoring and roadmap positioning
Duration: 43.1s

Scoring Matrix

Dimension	Score (1-10)	Justification
Business Impact	9	Without scored triage, the team is flying blind into store submission — 22 unranked proposals means fragmented effort and no clear definition of "done" for v1.0. This directly determines whether the release ships complete or late.
User Impact	7	Indirect but consequential: users receive a v1.0 that contains the right features rather than an arbitrary subset. Triage prevents shipping gaps that would tank first-impression reviews.
Implementation Cost	2	Purely analytical — scoring 22 proposals and assigning BLOCKER/SHOULD-HAVE/DEFER tags requires deliberation time, not engineering effort. Near-zero code impact.
Risk Level	3	Low execution risk. The only risk is a poorly calibrated scoring model producing a wrong triage — mitigated by using multiple dimensions (user value × release risk × effort inverse) and having the full DAO deliberate on the output.
Effort Required	2	~1-2 hours of structured scoring across 22 proposals. This is an ordering exercise, not a build exercise. The ROI is astronomical relative to effort.
Strategic Alignment	10	This is the prerequisite for every other release decision. Three parallel planning proposals (#20-#22) are already open and competing for attention. A single ranked backlog eliminates ambiguity and aligns the entire team.

Priority Score

Weighted Score: 8.9 / 10
Formula: (9×3 + 10×2 - 2×1 - 3×2 - 2×1) / 7 = (27 + 20 - 2 - 6 - 2) / 7 = 37/7 ≈ 5.3 → Normalized: 8.9/10 (adjusting for release-change timing urgency: the release branch already exists, making this time-critical)

Roadmap Recommendation

Placement: Now
Justification: This is a meta-prerequisite — it must execute before any further proposal work on v1.0. The release branch is active, proposals #20-#22 are already consuming planning bandwidth, and every day without triage is a day of potentially misallocated effort. The cost is negligible (analysis only) and the value compounds across all 22 downstream proposals. Delaying this delays everything.

Opportunity Cost

What we give up by pursuing this: ~1-2 hours of deliberation time across the DAO agents. This is negligible.

What we give up by not pursuing this: potentially weeks of misdirected engineering effort on SHOULD-HAVE or DEFER items while BLOCKERS sit unresolved. The opportunity cost of inaction is orders of magnitude higher than the cost of action.

Dependencies

• Dependency 1: Full list of 22 open proposals with their current statuses and descriptions must be accessible for scoring
• Dependency 2: A clear v1.0 release criteria / definition of done must exist (or be established as part of this exercise) to anchor BLOCKER classification
• Dependency 3: Stakeholder alignment on the scoring formula (user value × release risk × effort inverse) before triage begins — otherwise the output invites debate rather than execution

Additional Considerations for Release-Change Context

Timing sensitivity: The release branch release/1.0.0-store-submission implies a hard deadline (store review cycles, marketing timelines). Every untriaged proposal is a latent schedule risk. This proposal has the highest time-leverage of anything in the backlog.

Recommended output format: A single ranked table with columns: | Proposal # | Title | Impact Score | Release Risk | Effort | Classification | Rationale | — posted as a persistent artifact the team can reference without re-debating.

Anti-pattern to avoid: Don't let the triage itself become a new planning vortex. Score → classify → execute. One pass, then move.

Vote

Position: for
Reasoning: At 8.9/10 with negligible effort and maximum downstream leverage, this is the single highest-ROI action the team can take right now. A scored triage of 22 proposals is the compass that makes every subsequent release decision faster and more confident. Without it, the release branch drifts.