RCP-000-000-063-CONTENT-GAP-ANALYZER

{
“schema”: “CRAFT_AI_TO_AI_COMMUNICATION”,
“schema_version”: “1.1”,
“schema_profile”: “standalone-recipe”,
“recipe_id”: “RCP-000-000-063”,
“recipe_name”: “Content Gap Analyzer”,
“version”: “v3.00a”,
“original_version”: “v2.00a”,
“cookbook”: “Standalone (No Cookbook)”,
“category”: “Content Strategy”,
“subcategory”: “Competitive Analysis”,
“difficulty”: “Advanced”,
“status”: “Experimental”,
“craft_flavors”: “CRAFT Cowork”,
“recommended_personas”: “Not specified”,

“summary”: {
“what_it_does”: “Identifies content gaps in a competitive landscape by combining two intelligence sources: (1) user-provided firsthand competitor knowledge captured through a 10-question structured questionnaire, and (2) Deep Research investigation of competitors’ published content. Synthesizes both sources with transparent attribution into a prioritized gap analysis with differentiation strategies and action plans.”,
“what_it_is_not”: “Not a comprehensive competitive intelligence platform, SEO tool, analytics substitute, or performance prediction engine. Cannot access gated content, email newsletters, private communities, or actual engagement/traffic metrics. Deep Research coverage depends on public content accessibility and search visibility.”,
“key_design_decisions”: [
“Two-source intelligence model — user knowledge is primary, DR is supplementary. Every insight is source-tagged ([YOUR INPUT] / [DR FINDING]). Conflicts between sources are surfaced, not silently resolved.”,
“Gaps framed as hypotheses — ‘I did not find competitor content on X’ is not the same as ‘competitors don’t cover X.’ Confidence levels (HIGH/MEDIUM/LOW) communicate evidence strength.”,
“User-driven prioritization — AI identifies potential gaps and suggests criteria, but the user ranks them. Only the user knows their resources, strategic priorities, and competitive dynamics.”,
“DR transparency reporting — after each investigation, AI reports what it found, what it searched for but couldn’t find, and access limitations encountered. No fabricated competitor data.”,
“Experimental methodology documentation — Research Notes section at end of each run captures what DR strategies worked and what didn’t, enabling recipe refinement through use.”,
“AI-determined DR prompt count — 2-5 prompts based on competitor count, scope, and user knowledge depth. Research plan presented for user approval before execution.”
],
“revision_notes”: “v2.00a → v3.00a (QA revision, P051f H018). 14 gaps identified (4 CRITICAL, 5 HIGH). Full DR integration + user knowledge questionnaire + experimental tagging. Added 13 behavioral rules (was zero), 5-phase interactive delivery with 5 WAIT gates (was single prompt-and-dump), 4 adaptable DR prompt templates, 10-question competitor knowledge questionnaire. Stripped all false claims about URL browsing and performance data access. Difficulty: Intermediate → Advanced. Flavors: all → Studio (now CRAFT Cowork). Status: Official → Experimental. Code expanded from ~150 to ~640 lines.”
},

“parameters”: {
“count”: 5,
“required_count”: 3,
“optional_count”: 2,
“items”: [
{
“name”: “industry”,
“type”: “string”,
“required”: true,
“description”: “Business industry or niche. Specificity matters: ‘B2B HR compliance software for mid-market companies’ not just ‘HR tech.'”
},
{
“name”: “competitors”,
“type”: “list”,
“required”: true,
“description”: “2-3 competitors to analyze. Provide company names and website URLs. AI uses URLs for Deep Research investigation.”
},
{
“name”: “analysis_scope”,
“type”: “string”,
“required”: true,
“description”: “What content channels and types to focus on. Examples: ‘blog content and resource library,’ ‘social media and thought leadership,’ ‘full content ecosystem.'”
},
{
“name”: “your_content_strengths”,
“type”: “string”,
“required”: false,
“default”: “not provided”,
“description”: “What content you already produce well and where your expertise lies. Helps identify gaps you are best positioned to fill.”
},
{
“name”: “strategic_goal”,
“type”: “string”,
“required”: false,
“default”: “identify differentiation opportunities”,
“description”: “What you hope to achieve: market entry, repositioning, thought leadership, audience growth, etc.”
}
]
},

“behavioral_rules”: {
“count”: 13,
“items”: [
{“id”: “RULE-01”, “name”: “Two-Source Intelligence Model”, “summary”: “Two information sources: user-provided competitor knowledge and DR findings. Always clearly label which source each insight comes from using [YOUR INPUT] and [DR FINDING] tags. Never blend sources silently.”},
{“id”: “RULE-02”, “name”: “DR Transparency — Report What You Found and Didn’t”, “summary”: “After each DR investigation, report: (a) what was found, (b) what was searched for but not found, (c) access limitations encountered. Do not fill gaps with fabricated information.”},
{“id”: “RULE-03”, “name”: “No Fabricated Competitor Data”, “summary”: “Never invent competitor content titles, publishing frequencies, engagement metrics, traffic estimates, or performance data. Cite DR findings or user input. Fabricated intelligence is worse than no intelligence.”},
{“id”: “RULE-04”, “name”: “Hypothesis Framing for Gap Claims”, “summary”: “Frame all identified gaps as hypotheses, not certainties. ‘Based on what I found, this appears to be a gap’ — not ‘This is a gap your competitors have overlooked.'”},
{“id”: “RULE-05”, “name”: “No Engagement or Performance Claims”, “summary”: “Do not claim to know which competitor content performs well, gets high engagement, or drives traffic. DR can observe search prominence and visible social signals — frame as observational, not performance data.”},
{“id”: “RULE-06”, “name”: “User Knowledge Is Primary, DR Is Supplementary”, “summary”: “User’s firsthand market knowledge is more reliable than DR for areas they know well. DR should verify and extend, not contradict without strong evidence. Present both and let user judge conflicts.”},
{“id”: “RULE-07”, “name”: “AI Determines DR Prompt Count”, “summary”: “Based on competitor count, scope, and user knowledge depth, AI determines 2-5 DR prompts needed. State the plan before executing.”},
{“id”: “RULE-08”, “name”: “Structured DR Research Plan”, “summary”: “Before executing DR queries, present research plan to user. Each query specifies what it investigates, which competitors it targets, and what questions it aims to answer. User can adjust before execution.”},
{“id”: “RULE-09”, “name”: “Experimental Status Acknowledgment”, “summary”: “Briefly acknowledge experimental nature at session start and when presenting final results. DR effectiveness varies by industry. One sentence at opening, one at closing — do not over-disclaim.”},
{“id”: “RULE-10”, “name”: “Gap Prioritization Requires User Input”, “summary”: “AI identifies potential gaps and suggests prioritization criteria. User must drive final prioritization — only they know actual resources, strategic priorities, and competitive dynamics.”},
{“id”: “RULE-11”, “name”: “Differentiation Must Be Specific”, “summary”: “Go beyond generic advice like ‘create better content.’ Tie strategies to specific observations about competitor weaknesses and user’s stated strengths.”},
{“id”: “RULE-12”, “name”: “Scope Honesty at Opening”, “summary”: “Explain what this tool does and does not do. IS a structured gap analysis combining user knowledge with AI research. Is NOT a competitive intelligence platform, SEO tool, or analytics substitute.”},
{“id”: “RULE-13”, “name”: “Document DR Methodology for Refinement”, “summary”: “Because this recipe is experimental, document DR search strategies used, what worked well, and what produced thin results. Include Research Notes section for user feedback on methodology.”}
]
},

“delivery_structure”: {
“phases”: 5,
“wait_gates”: 5,
“flow”: [
“Phase 1: Context Collection — industry, competitors (2-3 with URLs), analysis scope, content strengths, strategic goal”,
“WAIT GATE 1 — collect context before questionnaire”,
“Phase 1B: Competitor Knowledge Questionnaire — 10 structured questions across 3 categories (Content Coverage, Content Quality + Positioning, Your Competitive Position)”,
“WAIT GATE 2 — collect questionnaire before DR planning”,
“Phase 2: Deep Research Planning + Execution — AI designs research plan (2-5 queries), user approves, DR executes, findings report with found/not-found/limitations”,
“WAIT GATE 3 — user approves research plan before DR execution”,
“Phase 3: Merged Intelligence Synthesis — unified competitor content landscape with source attribution, conflict surfacing, coverage confidence rating”,
“WAIT GATE 4 — user validates merged intelligence”,
“Phase 4: Gap Identification + Prioritization — 5-8 gaps as hypotheses with confidence levels, user-driven prioritization framework”,
“WAIT GATE 5 — user selects priority gaps”,
“Phase 5: Action Planning + Differentiation — specific differentiation strategies, content approach, validation steps, Research Methodology Notes for experimental refinement”
],
“output”: “5-deliverable interactive session: competitor knowledge capture, DR investigation report, merged intelligence landscape, prioritized gap analysis, and differentiated action plans with methodology notes. Each deliverable builds on user input from prior phases.”
},

“relationships”: {
“family”: “Content Strategy”,
“series”: “Content Strategy (062-063 + 026)”,
“series_position”: “2 of 3”,
“companion_recipes”: [
{“id”: “RCP-000-000-062”, “name”: “Content Idea Generator”, “relationship”: “Brainstorm content ideas before or after gap analysis — validates brainstormed ideas against competitive landscape”},
{“id”: “RCP-000-000-026”, “name”: “Content Performance Optimizer”, “relationship”: “Optimize content after production — works downstream of gap identification”},
{“id”: “RCP-000-000-041”, “name”: “Competitor Differentiator”, “relationship”: “Broader business differentiation beyond content — different analytical domain”}
],
“related_analytical”: []
},

“lessons_learned”: [
{
“id”: “LL-CRPW-063-001”,
“category”: “PATTERN”,
“lesson”: “CRAFT_FLAVORS stale value ‘Studio’ — 7th consecutive encounter in the pipeline (H077-H083), 4th distinct surface form. Prior forms: ‘All’ (runs 37-40), ‘All Flavors’ (run 40), ‘Foundations, Express, Studio’ (run 41), now ‘Studio’ (run 42). This is the first single-flavor name variant. The audit report confirms deliberate Studio-only gating for DR requirement, but flavor-based capability gating is moot post-Cowork. Same mechanical UPDATE to ‘CRAFT Cowork’.”,
“source”: “CWK-ADM-079 F-01, pipeline run 42”
},
{
“id”: “LL-CRPW-063-002”,
“category”: “OBSERVATION”,
“lesson”: “EXPERIMENTAL status inline meta-commentary in user-facing WPRM fields (DESCRIPTION and NOTES) is an editorial framework question outside CRPW pipeline scope. The recipe’s DESCRIPTION contains an ‘EXPERIMENTAL STATUS’ paragraph and the NOTES field opens with ‘EXPERIMENTAL RECIPE:’ — both function as developer/QA notes rather than user-facing copy. The text is honest and user-beneficial. Editorial treatment of experimental recipes (banner vs inline vs both) is a recipe-page template decision for P067 goal [6].”,
“source”: “CWK-ADM-079 F-02, pipeline run 42”
}
],

“pipeline_metadata”: {
“pipeline_run”: 42,
“recipe_number”: 41,
“standalone_number”: 37,
“handoff”: “H083”,
“date”: “2026-04-26”,
“project”: “CFT-PROJ-CP-067”
}
}

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