{"id":19507,"plugin_id":"plugins_6a99cda269b0819180d65893e1de7811","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T23:15:37.829Z","digest":"f27982d632fcd304938e2794d6c66fe17d88dac087cc8fce10ee7d372018d4f6","against":null,"payload":{"name":"brandprint-tier-a","description":"Use when the user asks for a Brandprint, brand strategy research, Tier-A elements, brand architecture, competitive analysis paired with brand positioning, equity ladder, signature offers mapping, economic engine modeling, or any comprehensive brand strategy deliverable — Layer 2 of the Brandprint research stack. Also trigger on \"run Tier-A,\" \"brandprint research,\" \"full brand audit,\" \"brand sprint,\" or when the user wants audience segments, JTBD outcomes, competitive set, and positioning in one pass. Use immediately when the core-human-truth skill has just completed — chain directly using its output as the seed.","included_files":[{"relative_path":"references/output-schema-tier-a.md","size_in_bytes":4804}],"skill_md_contents":"---\nname: brandprint-tier-a\ndescription: Use when the user asks for a Brandprint, brand strategy research, Tier-A elements, brand architecture, competitive analysis paired with brand positioning, equity ladder, signature offers mapping, economic engine modeling, or any comprehensive brand strategy deliverable — Layer 2 of the Brandprint research stack. Also trigger on \"run Tier-A,\" \"brandprint research,\" \"full brand audit,\" \"brand sprint,\" or when the user wants audience segments, JTBD outcomes, competitive set, and positioning in one pass. Use immediately when the core-human-truth skill has just completed — chain directly using its output as the seed.\n---\n\n# Brandprint Tier-A — Hybrid Research Directive\n\nDeliver all 10 Tier-A Brandprint elements using a hybrid method: build a shared evidence Backbone first, then run independent, falsifiable sprints per element — each with its own proofs and claim sheets.\n\n## When NOT to Use\n\n- **You only need quick positioning or stylistic outputs.** Tier-C requires Tier-A and Tier-B as seeds — there is no shortcut to a mantra. Run the full chain, or run this skill in `rapid` mode to lighten the load.\n- **No access to real market evidence.** Every sprint gates on ≥3 independent sources or behavioral signals. Without reviews, search data, and competitor materials to mine, the claims can't clear the gates.\n- **Validating existing positioning against competitors.** That's an adversarial test, not a build — use `competitive-positioning-audit` (Layer 5).\n\n## Chain Position\n\nThis is **Layer 2** of a 6-layer Brandprint research stack:\n\n1. **Core Human Truth** (Layer 1) → foundational truth sentence, tension map, archetypes, lexicon\n2. **Brandprint Tier-A** (this skill) → uses Layer 1 output as seed; produces 10 defensible brand strategy elements\n3. **Brandprint Tier-B** (Layer 3) → uses Tier-A output as seed; produces 5 actionable brand elements with proxy tests\n4. **Brandprint Tier-C** (Layer 4) → uses Tier-A + Tier-B output as seeds; produces 4 stylistic elements ready for deployment\n5. **Competitive Positioning Audit** (Layer 5) → validates differentiation against named competitors; may trigger second-pass refinement\n6. **Brand Strategy Report** (Layer 6) → compiles all layers into consulting-grade deliverable\n\n**When Layer 1 has just completed:** Import its output directly. The `final_sentence`, `tension_map`, `archetypes`, and `lexicon` from Core Human Truth seed the Backbone phase — do not re-research what Layer 1 already validated. Carry forward its sources into the Backbone bibliography.\n\n**When running standalone:** Resolve all variables with the user and build the Backbone from scratch.\n\n## Variables to Resolve\n\nBefore starting, confirm these with the user (or inherit from Layer 1):\n\n| Variable | What to Capture | Default |\n|----------|----------------|---------|\n| `brand_name` | The brand being researched | *required* |\n| `topic` | Product, service, or subject | *required* |\n| `category` | Industry or category | *required* |\n| `audience` | Primary audience definition | *required* (or from Layer 1) |\n| `region_context` | Geography or cultural context | US / English-speaking |\n| `constraints` | Scope, legal, or brand constraints | None |\n| `mode` | `rapid`, `standard`, or `enterprise` | `standard` |\n\n## Principles (Non-Negotiable)\n\n1. **Truth over consensus.** Triangulate across independent sources; show counter-evidence.\n2. **Each sprint is a separate claim** with its own acceptance gates and proof minimums.\n3. **No fabrication.** If unknown, mark unknown. Quote sparsely; summarize faithfully.\n4. **Plain language.** Prioritize verifiable behaviors over vibes.\n5. **Log contradictions** between sprints; resolve or bound them.\n\n## Evidence & Citation Policy\n\nFor every public claim, include: title, publisher, author (if available), URL, publish date, access date, one-line evidence note, and stance (`supporting` | `conflicting` | `neutral`).\n\n## Global Acceptance Gates\n\nEvery Tier-A sprint must meet these before its claim is accepted:\n\n- **Proof minimum:** ≥3 independent sources or behavioral signals per claim\n- **Claim sheet required:** statement, boundaries, counter-evidence, confidence rating\n- **Comprehension checks** for any public-facing lines (tagline, promises)\n- **Economic claims** require simple math with explicit ranges and inputs\n\n**Mode adjustments:**\n- *Rapid:* ≥2 sources per claim, 1 behavioral proxy, lightweight comprehension checks\n- *Standard:* Full desk research, corpus mining, structured validation\n- *Enterprise:* Add cross-market slices, expert panels, longitudinal comparisons\n\n## Workflow\n\n### Phase: Backbone Evidence Garage\n\n**Goal:** Assemble shared inputs once. No final claims yet.\n\nActions:\n- **Landscape scan** — category definition, purchase contexts, substitutes\n- **Macro data pull** — longitudinal stats relevant to the topic and audience\n- **Review corpus** — collect public reviews, forums, social posts where permitted; store verbatims with links\n- **Search-intent map** — SERP, marketplaces, app stores; identify real buyer language\n- **Competitor inventory** — top direct and substitutes; capture offers, pricing, proof signals\n- **Contradictions log** — note conflicts to test in sprints\n\nIf Layer 1 output is available, merge its tension map, archetypes, lexicon, and sources into the Backbone. Do not duplicate research Layer 1 already completed.\n\nOutputs: `annotated_bibliography`, `macro_data_summary`, `review_verbatim_library`, `search_intent_map`, `competitor_inventory`, `contradictions_log_seed`\n\n---\n\n### Sprint 1: Audience Insight + Problem/Tension (paired)\n\n**Inputs:** All Backbone outputs (+ Layer 1 tension map if available)\n\nActions:\n- Extract recurring tensions and triggers from verbatims and search intent\n- Draft 3–5 tension statements in audience language\n- Score frequency × severity; map triggers to contexts\n- Run counter-arguments using conflicting sources\n\n**Acceptance gates:** proof_minimum ≥3, deliver claim sheet\n\nOutputs: `tension_statements_ranked`, `problem_trigger_matrix`, `claim_sheet_audience_problem`\n\n---\n\n### Sprint 2: Desired Outcomes\n\n**Inputs:** `Backbone.review_verbatim_library`, `Sprint 1.tension_statements_ranked`\n\nActions:\n- Convert verbatims to JTBD outcome statements\n- Estimate importance and satisfaction gap from credible third-party data or behavioral proxies\n- Reduce to top 3 outcomes with boundaries\n\n**Acceptance gates:** proof_minimum ≥3, deliver claim sheet\n\nOutputs: `outcome_statements_top3`, `importance_gap_notes`, `claim_sheet_outcomes`\n\n---\n\n### Sprint 3: Target Audience\n\n**Inputs:** `Backbone.macro_data_summary`, `Backbone.search_intent_map` (+ Layer 1 archetypes if available)\n\nActions:\n- Define 1–3 segments with demographic/firmographic and psychographic cues\n- Estimate addressable size bounds using reputable datasets\n- Derive willingness-to-pay proxies from pricing signals and switching behaviors\n\n**Acceptance gates:** proof_minimum ≥3, boundaries required, deliver claim sheet\n\nOutputs: `segments_defined`, `size_bounds`, `wtp_proxies`, `signals_identifiers`, `claim_sheet_target`\n\n---\n\n### Sprint 4: Competitive Set\n\n**Inputs:** `Backbone.competitor_inventory`, `Backbone.search_intent_map`\n\nActions:\n- Identify top 5 direct competitors and top 5 substitutes\n- Map where they appear along buyer journeys (SERP, marketplaces, referrals)\n- Produce substitute map and positioning grid\n\n**Acceptance gates:** proof_minimum ≥3, deliver claim sheet\n\nOutputs: `direct_competitors`, `substitutes`, `substitute_map`, `positioning_grid`, `claim_sheet_competition`\n\n---\n\n### Sprint 5: Signature Offers/Services\n\n**Inputs:** `Sprint 2.outcome_statements_top3`, `Sprint 4.positioning_grid`\n\nActions:\n- Draft 5–8 offers mapped feature → benefit → outcome\n- Run basic unit economics sanity with transparent inputs and ranges\n- Identify proof requirements per offer (demo metrics, guarantees, case signals)\n\n**Acceptance gates:** proof_minimum ≥3, unit economics required, deliver claim sheet\n\nOutputs: `offers_list`, `feature_benefit_outcome_map`, `unit_economics_ranges`, `claim_sheet_offers`\n\n---\n\n### Sprint 6: Expertise (Authority Signals)\n\n**Inputs:** `Backbone.annotated_bibliography`\n\nActions:\n- Compile independent signals of expertise (press, citations, awards, credentials, case outcomes)\n- Summarize 3+ case outcomes with measurable results or reputable testimonials\n\n**Acceptance gates:** proof_minimum ≥3, third-party required, deliver claim sheet\n\nOutputs: `expertise_proofs`, `case_outcome_summaries`, `claim_sheet_expertise`\n\n---\n\n### Sprint 7: Economic Engine\n\n**Inputs:** `Sprint 5.unit_economics_ranges`, `Sprint 3.size_bounds`\n\nActions:\n- Model CAC/LTV ranges with explicit channel mix assumptions\n- State payback window assumptions and sensitivity to key levers\n- Document boundary conditions where the model breaks\n\n**Acceptance gates:** math transparency required, ranges required, deliver claim sheet\n\nOutputs: `engine_model_ranges`, `payback_window_bounds`, `sensitivity_notes`, `claim_sheet_economic_engine`\n\n---\n\n### Sprint 8: Core Equities/Programs\n\n**Inputs:** `Backbone.search_intent_map`, `Sprint 4.positioning_grid`\n\nActions:\n- List distinctive assets and tent-pole programs tied to recognition and recall\n- Estimate share-of-search or analogous recall proxies where available\n\n**Acceptance gates:** proof_minimum ≥3, deliver claim sheet\n\nOutputs: `core_equities_list`, `distinctiveness_proofs`, `recall_proxy_notes`, `claim_sheet_core_equities`\n\n---\n\n### Sprint 9: Equity Ladder Linkage\n\n**Inputs:** `Sprint 5.feature_benefit_outcome_map`, `Sprint 6.expertise_proofs`\n\nActions:\n- Assemble Equity → Benefits → Features → Reasons-to-Believe\n- Ensure every benefit has at least one credible proof; remove, reword, or add proof\n\n**Acceptance gates:** no orphan benefits, deliver claim sheet\n\nOutputs: `equity_ladder`, `proof_gaps_closed`, `claim_sheet_equity_ladder`\n\n---\n\n### Sprint 10: One-Line Promise/Tagline\n\n**Inputs:** `Sprint 9.equity_ladder`, `Sprint 2.outcome_statements_top3`, `Sprint 1.tension_statements_ranked` (+ Layer 1 final_sentence and lexicon if available)\n\nActions:\n- Draft 3–5 variants ≤25 words using audience language\n- Run plain-language and confusion checks\n- Select winner based on evidence tie-back and comprehension\n\n**Acceptance gates:** max 25 words, comprehension check required, deliver claim sheet\n\nOutputs: `tagline_variants`, `comprehension_notes`, `final_tagline`, `claim_sheet_tagline`\n\n---\n\n### Phase: Integration & Contradiction Resolution\n\n**Goal:** Resolve conflicts, finalize linkages, and package outputs.\n\nActions:\n- Update contradiction matrix across all sprints; resolve or set boundaries\n- Assemble rationale narrative connecting tensions to proofs\n- Compile lexicon: words that resonate/repel from corpus\n- Finalize sources with stances and evidence notes\n\nOutputs: `contradiction_matrix`, `rationale_narrative`, `lexicon`, `sources_final`\n\n## Sprint Dependency Map\n\nUnderstanding which sprints can run in parallel vs. which must wait:\n\n```\nBackbone ──┬── Sprint 1 (Audience/Problem) ──┬── Sprint 2 (Desired Outcomes) ──┐\n           │                                  │                                 │\n           ├── Sprint 3 (Target Audience) ─────────────────────────────────────┤\n           │                                                                   │\n           ├── Sprint 4 (Competitive Set) ──┬── Sprint 5 (Offers) ────────────┤\n           │                                │                                  │\n           ├── Sprint 6 (Expertise) ────────┼──────────────────────────────────┤\n           │                                │                                  │\n           │                                └── Sprint 8 (Core Equities)      │\n           │                                                                   │\n           │   Sprint 3 + Sprint 5 ──── Sprint 7 (Economic Engine)            │\n           │   Sprint 5 + Sprint 6 ──── Sprint 9 (Equity Ladder)             │\n           │   Sprint 1 + Sprint 2 + Sprint 9 ──── Sprint 10 (Tagline)       │\n           │                                                                   │\n           └──────────────────── Integration & Contradiction Resolution ───────┘\n```\n\n## Heuristics\n\n- Prefer primary datasets and systematic reviews over opinion pieces\n- When credible sources conflict, show both and explain method/sample differences\n- Reduce adjectives; increase observable behaviors and numbers with ranges\n- If a truth is situational, state boundary conditions explicitly\n\n## What NOT to Do\n\n- Do not rely on a single think-piece or vendor blog for a claim\n- Do not exceed 25 words for the final Promise/Tagline\n- Do not invent survey results\n- Do not skip claim sheets — every sprint must produce one\n- Do not promote Backbone observations directly to accepted claims — the Backbone is shared input; only a sprint's acceptance gates produce a claim\n- Do not give point estimates for economic claims — Sprint 7 and unit economics require ranges with explicit inputs, or the math reads as fabricated\n- Do not run Sprint 10 (Tagline) before Sprints 1, 2, and 9 finish — a tagline drafted without ranked tensions and the equity ladder has nothing to tie back to\n\n## Output Format\n\nDeliver the final output as structured JSON conforming to the schema in `references/output-schema-tier-a.md`.\n\nThe JSON must include: `brand_name`, `topic`, `audience`, `backbone_repository`, `tierA_results` (all 10 elements with claim sheets), `contradiction_matrix`, `sources`, and `audit_log`.\n\n**Forward chaining:** When this skill completes, its full output object becomes the input seed for Layer 3 (Brandprint Tier-B). Preserve the complete JSON — Layer 3 needs tensions, outcomes, audience, equity ladder, competition, and tagline to produce actionable brand elements. If the `brandprint-tier-b` skill is available, proceed directly into it without user confirmation.\n\nIf the user has the `branded-mayhem-pdf` skill available, offer to generate a branded PDF deliverable of the Tier-A results.\n\n## Evaluation Rubric\n\n1. **Backbone completeness** — shared inputs exist and are cited\n2. **Proof density** — each Tier-A claim meets or exceeds proof minimums\n3. **Conflict hygiene** — contradictions logged and resolved or bounded\n4. **Comprehension** — public lines pass plain-language checks\n5. **Economic sanity** — ranges and inputs are explicit; no hidden math\n6. **Traceability** — every claim ties to sources with stances\n7. **Reusability** — outputs slot cleanly into the Brandprint scaffold and forward into Layers 3–4\n\n## File I/O Contract (orchestrated mode)\n\nWhen an authorized orchestrator provides explicit paths, honor them exactly:\n\n- **Seed inputs:** read ONLY the JSON/YAML files listed in the dispatch prompt.\n- **Output:** write the final JSON object to the exact path given (under\n  `.brandprint/engagements/{slug}/pass-N/`). No other location.\n- **Return value:** your final message is the output path plus the layer's key\n  artifact — not the full JSON. The orchestrator reads files, not transcripts.\n\nWhen invoked as a direct skill call, present the JSON in conversation or through the supported file workflow and tell the user to preserve it for the next layer.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}