Spec Interview

Pack Availability Guard

Before telling the user to run a skill from another project-local pack, check .agents/project.json.enabled_packs. If the target pack is not enabled, recommend npx skillpacks install <pack> from the project shell, instead of the target skill. Only the currently running skill and skills verified available in the active session or project-local install state are directly recommendable. For unavailable pack skills, recommend npx skillpacks install <pack-or-skill>; for unavailable base skills, recommend npx skillpacks init before the skill.

Prototype Gate

Before starting the interview process, verify:

1. A consolidated prototype exists at prototypes/{topic}/consolidated/. If missing, halt and recommend $consolidate-prototypes first.
2. An AFPS graduation document exists at design/afps-graduation-{topic}.md or design/{slug}/afps-graduation-{topic}.md. If missing, halt and recommend $consolidate-prototypes to produce the graduation document before spec work.
3. The AFPS graduation document marks the consolidated MVP ready for post-prototype cleanup and production specification and includes the recommended platform strategy from Platform Fit Workshop / platform-probe evidence when those exist. If it lists unresolved consolidation decisions, platform risks, or blocking prototype risks, halt and recommend resolving those first.
4. All blocking research tasks from the --post-prototype pass are completed — check tasks/todo.md for unchecked items under ## Priority Documentation Todo that reference post-prototype research, contradicted research, stale research, or pre-spec blockers. If unchecked blocking post-prototype items remain, halt and recommend completing those first.

If both gates pass, proceed with the interview.

Spec Interview

Invoke as $spec-interview.

Use this skill when the user has a consolidated prototype and research context that needs to be turned into a complete production implementation specification. For half-formed product ideas, run $idea-scope-brief before this skill.

Design-Tree Flow

This skill runs the unified 5-stage design-tree flow (interrogation → research → design → plan → implement(scoped)) from DESIGN-TREE-LOOP.md as the tree's terminal spec writer, formalizing the approved MVP into a production-ready v1 specification. The ## Process steps below group by stage:

• Stage 0 — Interrogation: the ## Prototype Gate plus the stage-zero loop in ## Interrogation Page / INTERROGATION-PAGE.md and the prototype-grounded assumptions checkpoint — confirm the consolidated prototype, AFPS graduation readiness, platform strategy, research frame, and that no unchecked blocking post-prototype items remain.
• Stage 1 — Research: read the consolidated prototype, research context, AFPS graduation platform strategy, and design/**/flow-tree-*.yaml as upstream surface/channel, platform-fit, and route/screen realization evidence.
• Stage 2 — Design: walk each consolidated route/screen realization and relevant non-visual channel behavior to extract production behaviors, data, contracts, and acceptance criteria.
• Stage 3 — Plan: the surface-aware route/screen spec outline is the slice the implementation phase realizes.
• Stage 4 — Implement (scoped): write the production specification, keeping specs/ as the canonical output directory, behind the single binding alignment gate. This same confirmed alignment record owns the Production Ready Approval: approval to move from concept/prototype/spec into a production product build, not proof that the product is already shipped.

Per-branch iteration contract. Each session cold-starts, reads the approved tree, resolves the consolidated MVP ready to specify, runs the staged flow, writes the spec on approval, and stops with the handoff in ## Next Work.

Modify-back. If specification reveals an unresolved design flaw or platform-strategy gap, record a modify decision whose targets[] re-opens the upstream model_ref, platform_fit, or user-flow branch rather than encoding the gap into the spec.

Process

0. Product-Path Scope Resolution

Resolve research scope by product path before using code or app structure as a hint:

1. If $ARGUMENTS names a non-archived research/{slug}/ directory or a product-path ID whose scope_path points there, use that path. Treat {slug} as the product/app name, not the ICP, audience, or segment label.
2. If $ARGUMENTS names only research/_archive/{slug}/ or a manifest entry with status: archived or legacy status: abandoned, stop and warn that the path is archived; do not write or update scoped outputs there.
3. Read research/.progress.yaml when present. Normalize legacy active_path to active_paths on read and write back active_paths on manifest updates. Treat legacy abandoned as archived; exclude archived, abandoned, deferred, revisit_candidate, promoted, and any scope_path under research/_archive/ from active target selection.
4. If active product paths exist in the manifest, use those paths. If multiple active paths exist, ask which one to target unless this skill explicitly supports cross-path output.
5. If no active manifest target exists, list non-archived product directories under research/, excluding research/_archive/ and dot directories. Auto-select only when exactly one exists; ask when multiple exist.
6. If no product directories exist, use flat research/ single-product mode.
7. Detect monorepo/app/package structure only as a secondary hint. Suggest creating a missing research/{slug}/ product path when code clearly exposes an app, but do not require code or monorepo detection before using research/{slug}/.

When product path {slug} is active, read and write research under research/{slug}/, specs under specs/{slug}/, and treat top-level research/*.md files as flat-mode documents or cross-path summaries.

1. Read consolidated prototype and research context:

   • Read the consolidated prototype directory at prototypes/{topic}/consolidated/ as the primary input. Walk through every screen, component, and interaction in the prototype to understand the current state.
   • Read design/afps-graduation-{topic}.md or design/{slug}/afps-graduation-{topic}.md as the readiness signal from consolidation into post-prototype cleanup and production specification.
   • Require the AFPS graduation platform strategy when platform_fit or platform_probe evidence exists. Treat it as the starting platform decision, not a loose suggestion.
   • Read the consolidation interview log and alignment/consolidate-prototypes-{topic}.html when present. Treat final MVP decisions, rejected alternatives, UAT evidence, unresolved risks, and stale-research cleanup notes as upstream evidence, not as a separate production-readiness state store.
   • Check .agents/project.json first. If it exists, use project_type and enabled_packs to choose the right research frame:
     • business-app → use business research artifacts such as research/icp.md.
     • game → use game artifacts such as research/game-audience.md, research/game-fantasy.md, and research/game-core-loop.md.
     • devtool → use devtool artifacts such as research/devtool-user-map.md, research/devtool-dx-journey.md, and research/devtool-integration-map.md.
   • If project type is missing or mismatched, recommend scripts/pack.sh recommend, npx skillpacks install <pack> from the project shell before doing domain-specific planning.
   • For business-app projects, check if research/idea-brief.md, research/icp.md, and research/journey-map.md exist. Read them as source evidence — ground implementation decisions against the concept constraints, ICP, user journey, customer journey, technical sophistication, customer provisioning model, path to aha, conversion path, retention loop, and champion dynamics. If lifecycle evidence is missing and the customer-lifecycle pack is not enabled, recommend npx skillpacks install customer-lifecycle from the project shell, before $journey-map. Flag conflicts (e.g., "Journey map says the buyer needs a demo before sign-up — does this self-serve-only onboarding fit?"). Do not re-interview on concept, ICP, or journey topics already covered.
   • Read design/user-flow-*.md, design/ux-variations-*.md, design/ui-*.md, and design/**/flow-tree-*.yaml when present as upstream pre-prototype design evidence for surface inventory, channels, platform fit, platform probes, visual UI candidates, route/screen realizations, branch decisions, state coverage, failure/recovery paths, handoffs, selected UX variation branches, UI approval state, and low-fidelity wireframe intent.

2. Surface a prototype-grounded assumptions checkpoint before probing:

   • After reading the consolidated prototype and research context but before asking deep probing questions, present a concise Assumptions Checkpoint grounded in what the prototype reveals.
   • Tag each assumption with its source:
     • [from spec] — explicitly stated in the draft or spec document
     • [from codebase] — derived from reading existing code, config, or infrastructure
     • [from research] — derived from research docs (ICP, audience, journey maps)
     • [from prototype] — derived from the consolidated prototype
     • [inferred] — not stated anywhere; you filled in a default or made a judgment call
   • Keep the checkpoint short enough to preserve interview momentum: normally 3 to 7 bullets, grouped only when useful. Do not dump a comprehensive manifest unless the user explicitly asks to review assumptions first.
   • Bias the checkpoint toward assumptions that are uncertain, risky, contradicted by evidence, or likely to change data/API/architecture choices. Omit obvious restatements that can be captured later in the spec.
   • Focus the checkpoint on what the prototype assumes vs what production needs:
     • What the prototype assumes about data model — what's fake/fixture vs what needs real persistence
     • What the prototype assumes about auth — none? mock? real?
     • What fake data needs to become real — hardcoded lists, mock APIs, placeholder content
     • What missing infrastructure is needed — database, auth, payments, analytics, deployment
     • What the AFPS graduation platform strategy implies for production architecture, permissions, distribution, monetization, and companion surfaces
     • What error/empty states the prototype skips vs what production needs
   • Deliver the checkpoint inline as the final message text of its own turn — never only as mid-turn text in a turn that ends with a tool or command call. In the next turn, ask the user to confirm or correct it together with one focused interview question so momentum is kept. Do not stop at the assumptions checkpoint unless the user explicitly asks to pause and review assumptions first.
   • If any [inferred] assumption is corrected, note the correction — these corrections are high-signal for downstream risk and must appear in the interview log.

3. Surface-aware prototype walkthrough:

   • Walk through each route/screen realization in the consolidated prototype and any non-visual channel behavior it exposes or depends on. For each realization or channel contract, probe:
     • Data model? What entities, relationships, persistence does this screen need?
     • API calls? What endpoints, payloads, error responses?
     • Auth? What permissions, roles, access control apply to this screen?
     • Empty/error states? What happens when data is missing, requests fail, or the user has no content yet?
     • Performance? Loading states, pagination, caching needs?
     • Analytics? What events should be tracked on this screen?
   • Codex interview cadence: ask one primary decision question per turn by default. Use short follow-up bullets only when they clarify the same decision, not to batch unrelated questions.
   • If the session is already in Plan mode, request_user_input may present 2-3 real options for the current material decision. Otherwise ask one concise direct question in plain text.
   • Research and recommend by default. Use web search, upstream research docs, and codebase analysis to gather evidence before asking the user. Assume the user has no insider knowledge unless they explicitly provide it. Present findings with data, define relevant terms, state a recommendation with reasoning, and ask the user to approve, adjust, or override based on hard constraints, proprietary facts, or corrections. Only present options without a recommendation when internal constraints, preferences, or missing facts make evidence insufficient. For each real choice:
     • Explain the options with evidence
     • Give a brief pros and cons comparison
     • State a recommendation and why
     • Explain how to mitigate the recommended option's downside when useful

4. Iterative prototype updates:

   • When the interview reveals gaps in the prototype (missing route/screen realizations, unclear channel behavior, unclear flows, absent states), ask: "Should I update the prototype to add [gap]? This may warrant re-running upstream steps."
   • If the consolidated prototype exposes missing or unclear surface/channel coverage, route/screen flow, branch coverage, state coverage, failure/recovery behavior, or handoff structure, recommend $user-flow-map [topic] as the upstream remediation before continuing production-spec work.
   • The user decides per-update whether to update the prototype now or note it for production implementation only.
   • If the user approves a prototype update, make the change and note it in the interview log before continuing.

5. Cover all areas:

   • Continue until implementation goals, final production platform decision, architecture, data models, APIs/contracts, migrations, edge cases, security, performance, observability, test strategy, and scope boundaries are all covered.
   • Resolve remaining platform risks explicitly. If a platform risk changes the primary platform, companion platform, permission/trust model, distribution path, monetization path, or technical leverage assumptions, route back to $consolidate-prototypes or $user-flow-map instead of burying the uncertainty in the spec.
   • Coverage checkpoint — Before concluding, present a structured summary inline as the final message text of its own turn: list each area covered with key decisions made and the evidence/reasoning that supported each. In the next turn, ask: "Does this cover everything? Any constraints, missing facts, or areas to revisit?"

Deliverables

• Write the completed specification to specs/[topic].md (create the specs/ directory if needed), where topic is a short kebab-case summary
• Keep specs/ as the canonical output directory for finalized post-prototype implementation specifications. Do not write pre-prototype flow maps, UX variation plans, UI branch packets, or flow-tree manifests from this skill; those remain design-phase artifacts under design/.
• The spec must use these canonical section headings (unnumbered):
  • ## Overview
  • ## Goals
  • ## Non-Goals
  • ## Detailed Design (architecture, data models, APIs, UI flows)
  • ## Edge Cases
  • ## Test Plan
  • ## Acceptance Criteria
  • ## Open Questions
  • ## Assumptions & Risks (the checkpoint output)
  • ## Production Platform Decision Additional topic-specific sections (e.g. ## Data Model, ## Security) may appear between Detailed Design and Edge Cases. Do not number sections.
• Write an interview log to [topic]-interview.md

The interview log should include:

• The Assumptions Checkpoint as presented, with user corrections noted
• Each question asked
• Options presented, if any
• The user's responses and chosen direction
• A closing summary of significant deviations from the initial draft and why they changed

Append an Assumptions & Risks section to the end of the spec listing: each checkpoint assumption that was confirmed, corrected, or left unresolved during the interview, its source tag, and the downstream risk if the assumption turns out to be wrong later. Flag any [inferred] assumptions that were never explicitly confirmed by the user. Include a Production Platform Decision section naming the final primary platform, companion platforms, deferred/rejected platforms, probe evidence, and production implications. Include a Production Ready Approval section in the spec or alignment page before implementation handoff. Follow docs/production-ready-approval.md: record the approved MVP scope, evidence basis, final production platform decision, remaining blockers, and whether the concept/prototype is approved to enter production build. Do not create a new state database, research/.progress.yaml schema, or competing lifecycle registry.

Alignment Page

Follow the shared alignment-page convention via the packaged convention resolver; output path is alignment/spec-interview-{topic}.html.

Next Work

Next work: the specification is the design tree's terminal artifact. After the Production Ready Approval is confirmed, hand off to implementation/execution — verify the target pack is enabled in .agents/project.json enabled_packs before recommending $roadmap then $exec (agent-work-admin pack).

Recommended next command: implementation handoff via $roadmap.

Invoke With YAML

Emit the agent_routing payload with the exact resolved implementation-handoff command, {slug}/{topic} filled to literal values: $roadmap then $exec (agent-work-admin pack; npx skillpacks install agent-work-admin if not enabled).

Constraints

• Do not assume draft text is final.
• Keep probing until the spec is decision-complete enough to implement.

Ideas Mode (--ideas)

When $ARGUMENTS contains --ideas, read tasks/ideas.md and run the interview process for each idea sequentially.

1. Read tasks/ideas.md and extract every distinct idea entry. If a filter keyword is provided, limit to matching ideas.
2. Show the user the list and ask them to confirm, skip any, or reorder.
3. For each idea, run the standard interview process using the idea's title and description as the initial implementation draft. If an idea is still only a raw concept, route it to $idea-scope-brief first.
4. Write deliverables (specs/[topic].md and [topic]-interview.md) for each completed idea.
5. After each idea, summarize decisions and move to the next. The user may say "skip".
6. If the user stops partway through, write deliverables for completed ideas and note which remain.

Do not repeat work already in tasks/roadmap.md, tasks/todo.md, or specs/.

Archive-First Replacement Policy

• Before replacing or substantively rewriting an existing canonical research/spec document (research/**/*.md, specs/**/*.md, or docs/specifications/**/*.md), copy the current file to docs/history/archive/YYYY-MM-DD/HHMMSS/<original-relative-path>.
• Preserve the archived snapshot exactly as it existed before the change; do not edit the archived copy after creating it.
• After the archive snapshot exists, write the updated document to the original canonical path.
• Report both the archive path and the updated canonical path in the final output.
• New files do not need archive snapshots. Append-only updates do not need archive snapshots unless an existing section is regenerated or rewritten.
• Keep any existing user approval requirement before overwriting or replacing a document; archiving does not replace asking when the skill already requires approval.

Interrogation Page

Follow the shared interrogation-page convention via the packaged convention resolver; output path is interrogation/spec-interview-r{N}-{branch}.html. Before producing research, run the stage-zero interrogation loop, starting with the assumptions manifest as round 1, and loop until the confidence gate passes. This skill cannot advance to stage one until the confidence gate passes with at least one completed interrogation round and every interview area covered or waived. Each round page must contain at least one genuinely open input (data-open-input).

Default Shipping Contract

Follow the shared shipping contract convention in CLAUDE.md.