trend-scout

name

trend-scout

description

Interactive trend scouting workflow with industry selection, bilingual support (DE/EN), and deeper-research integration. Scouts trends across 4 dimensions (each trend gets full TIPS expansion). Creates research projects with industry-contextualized trend candidates that can be passed to deeper-research-0. Use when: (1) Starting smarter-service research with industry context, (2) User wants to scout trends for a specific industry and subsector, (3) User mentions "trend scouting", "industry trends", "trend selection", (4) Preparing input configuration for deeper-research-0.

Trend Scout

Interactive workflow for scouting trends across 4 dimensions with industry selection and bilingual support. Each trend discovered is later analyzed through the complete TIPS framework (Trend → Implications → Possibilities → Solutions). Produces configuration files for downstream deeper-research-0 research.

Purpose

This skill enables users to:

1. Select an industry and subsector from a standardized taxonomy
2. Initialize a research project with semantic slug
3. Generate 76 trend candidates (7 ACT + 7 PLAN + 5 OBSERVE per dimension)
4. Pre-select candidates per cell (5 ACT + 5 PLAN + 3 OBSERVE per dimension = 52 total) for downstream use (user can adjust)
5. Produce configuration for deeper-research-0 skill

Bilingual Support

Full German and English support throughout:

• Industry taxonomy presented in both languages
• Web research queries in both languages (global + DACH regions)
• User-facing messages in detected language
• Output files respect project_language setting

Prerequisites

• Projects are stored relative to the current working directory (or COGNI_WORKSPACE_ROOT if set)
• Web access enabled for live trend research

Shell Execution Constraints

CRITICAL - Do NOT improvise shell commands:

1. All project initialization MUST use the provided scripts
2. NEVER generate inline bash code for slug generation or project creation
3. If a script is not found, report the error and ask user to verify installation
4. Do NOT attempt workarounds with inline $(...) command substitution

Path Variable Distinction:

Variable	Purpose	Example
CLAUDE_PLUGIN_ROOT	Plugin installation (scripts, skills)	~/.claude/plugins/marketplaces/cogni-tips
COGNI_WORKSPACE_ROOT	Optional workspace root override (default: $PWD)	Current working directory

IMPORTANT - Environment Variables:

• CLAUDE_PLUGIN_ROOT is automatically injected by Claude Code from settings.local.json
• COGNI_WORKSPACE_ROOT is optional — defaults to the current working directory if not set
• DO NOT source .workplace-env.sh - variables are already available at runtime

Script Locations (always use CLAUDE_PLUGIN_ROOT):

• ${CLAUDE_PLUGIN_ROOT}/skills/trend-scout/scripts/generate-project-slug.sh
• ${CLAUDE_PLUGIN_ROOT}/skills/trend-scout/scripts/update-industry-metadata.sh
• ${CLAUDE_PLUGIN_ROOT}/skills/trend-scout/scripts/finalize-candidates.sh
• ${CLAUDE_PLUGIN_ROOT}/scripts/initialize-trend-project.sh

References Index

Read references only when needed for the specific task:

Reference	Read when...
references/industry-taxonomy.md	Presenting industry selection to user
references/i18n/messages-en.md	English user messages
references/i18n/messages-de.md	German user messages
references/methodology.md	Academic foundations (Ansoff, Rohrbeck, Rogers), full methodology explanation
references/scoring-framework.md	Scoring candidates, indicator classification, diffusion stage (Phase 2)
references/dach-sources.md	DACH site-specific web searches (Phase 1)
references/funding-signals.md	VC/funding signal queries (Phase 1)
references/job-market-signals.md	Job market signal queries (Phase 1)
references/academic-api-queries.md	Academic API searches - OpenAlex, Semantic Scholar, arXiv (Phase 1)
references/patent-api-queries.md	Patent API searches - USPTO, Lens.org, EPO (Phase 1)
references/regulatory-feeds.md	Regulatory API searches - EUR-Lex, SEC EDGAR, FDA (Phase 1)
references/workflow-phases/phase-0-initialize.md	Project init + industry selection
references/workflow-phases/phase-3-present.md	Writing trend-candidates.md with scores
references/workflow-phases/phase-4-process.md	Processing user selections
references/workflow-phases/phase-5-finalize.md	Generating deeper-research config

Immediate Action: Initialize TodoWrite

MANDATORY: Initialize TodoWrite immediately with workflow phases:

1. Phase 0: Initialize Project + Industry Selection [in_progress]
2. Phase 1: Bilingual Web Research [pending]
3. Phase 2: Generate Candidate Pool [pending]
4. Phase 3: Present Candidates [pending]
5. Phase 4: Process User Selection [pending]
6. Phase 5: Finalize deeper-research Config [pending]

Update todo status as you progress through each phase.

---

Core Workflow

Phase 0 → Phase 1 → Phase 2 → Phase 3 → [USER EDITS] → Phase 4 → Phase 5
   │          │          │         │            │            │         │
   │          │          │         │            │            │         └─ Write config + JSON
   │          │          │         │            │            └─ Validate selections
   │          │          │         │            └─ User marks [x], adds proposals
   │          │          │         └─ Write trend-candidates.md with scores, PAUSE
   │          │          └─ Generate + score 76 candidates (mix web + API + training)
   │          └─ 32 web searches + academic/patent/regulatory APIs
   └─ Language detect, industry select, project init

Phase 0: Initialize Project + Industry Selection

Read references/workflow-phases/phase-0-initialize.md, then execute:

1. Ask user for deliverable language: Read workspace language from .workspace-config.json (via ${PROJECT_AGENTS_OPS_ROOT}/.workspace-config.json or CWD). Ask user via AskUserQuestion: "Deutsch (DE) oder English (EN)?" If workspace language is set, present it as default (e.g., "[Default: DE based on workspace]"). Do NOT skip asking — always confirm with user.
2. Load references/industry-taxonomy.md
3. Present industries with subsectors (bilingual)
4. Capture user selection via AskUserQuestion
5. Capture research topic/focus
6. Generate project slug: {subsector}-{topic}-{hash}
7. Initialize project via initialize-trend-project.sh in the current working directory under cogni-tips/
8. Update .metadata/trend-scout-output.json with industry context

Required outputs:

• PROJECT_PATH, PROJECT_SLUG variables set
• PROJECT_LANGUAGE set from explicit user choice (de/en)
• INDUSTRY, SUBSECTOR selected
• RESEARCH_TOPIC captured
• Project structure initialized in current working directory under cogni-tips/

Phase 1: Bilingual Web Research + API Queries (DELEGATED)

Context Efficiency: This phase is delegated to the web-researcher agent to prevent context depletion from 20+ WebSearch results. The agent returns a compact JSON summary (~500 tokens) while logging full results to .logs/.

Invoke the web-researcher agent:

Task:
  subagent_type: "cogni-tips:trend-web-researcher"
  description: "Execute bilingual web research"
  prompt: |
    Execute Phase 1 web research for trend-scout.

    PROJECT_PATH: {{PROJECT_PATH}}
    INDUSTRY_EN: {{INDUSTRY_EN}}
    INDUSTRY_DE: {{INDUSTRY_DE}}
    SUBSECTOR_EN: {{SUBSECTOR_EN}}
    SUBSECTOR_DE: {{SUBSECTOR_DE}}
    RESEARCH_TOPIC: {{RESEARCH_TOPIC}}

Agent responsibilities:

1. Build 32 web search configurations (16 standard + 8 DACH site-specific + 4 funding + 4 job market)
2. Execute WebSearch for each config in parallel batches
3. Execute mandatory API queries (academic, patent, regulatory) with fallback handling
4. Extract and deduplicate trend signals
5. Classify signals by indicator type (leading/lagging) and source type
6. Write full results to {{PROJECT_PATH}}/.logs/web-research-raw.json
7. Return compact JSON with ~85 aggregated signals

Note: The web-researcher agent is self-contained with all search configurations and deduplication logic.

Process agent response:

The agent returns compact JSON with abbreviated fields for token efficiency:

{
  "ok": true,
  "signals": {
    "total": 85,
    "by_dimension": {...},
    "by_source": {"web": 48, "dach_site": 12, "funding": 8, "jobs": 6, "academic": 5, "patent": 4, "regulatory": 2},
    "by_indicator": {"leading": 38, "lagging": 47}
  },
  "items": [{"d": "dimension", "n": "name", "k": ["keywords"], "u": "url", "f": "freshness", "a": 5, "t": "type", "i": "leading", "lt": "12-24m"}]
}

Log file format (.logs/web-research-raw.json):

The log file uses full field names for debugging readability. Key structure:

{
  "metadata": {...},
  "searches_executed": {"total": 32, "successful": 30, "failed": 2, "by_category": {...}},
  "raw_signals_before_dedup": [
    {"dimension": "...", "signal": "...", "keywords": [...], "source": "url", "freshness": "...", "indicator_type": "leading|lagging", "lead_time": "...", "source_type": "..."}
  ],
  "api_queries_executed": {...}
}

To query the log file directly:

jq '.raw_signals_before_dedup[] | {dimension, signal, keywords, source}' .logs/web-research-raw.json

Convert to WEB_RESEARCH_CONTEXT:

• Expand abbreviated fields from agent response (d→dimension, n→signal_name, k→keywords, u→source_url, f→freshness, a→authority_score, t→source_type, i→indicator_type, lt→lead_time)
• Group by dimension for Phase 2 consumption
• Set WEB_RESEARCH_AVAILABLE = (response.ok == true)

Persist compact response for downstream fallback:

Write the agent's raw compact JSON response (the full response object including the .items array) to: {PROJECT_PATH}/phase1-research-summary.json

This file serves as a fallback for trend-report when .logs/web-research-raw.json is missing or incomplete.

Required outputs:

• WEB_RESEARCH_AVAILABLE flag set
• WEB_RESEARCH_CONTEXT with signals by dimension (including source_type and authority scores)

Fallback: If agent returns {"ok": false}, proceed with training-only generation (warning logged).

Phase 2: Generate Candidate Pool (DELEGATED)

Context Efficiency: This phase is delegated to the trend-generator agent to leverage Opus model's extended thinking for complex multi-framework reasoning. The agent returns a compact JSON summary (~600 tokens) while logging full candidate data to .logs/.

Invoke the trend-generator agent:

Task:
  subagent_type: "cogni-tips:trend-generator"
  description: "Generate 76 scored trend candidates"
  prompt: |
    Execute Phase 2 candidate generation for trend-scout.

    PROJECT_PATH: {{PROJECT_PATH}}
    INDUSTRY_EN: {{INDUSTRY_EN}}
    INDUSTRY_DE: {{INDUSTRY_DE}}
    SUBSECTOR_EN: {{SUBSECTOR_EN}}
    SUBSECTOR_DE: {{SUBSECTOR_DE}}
    RESEARCH_TOPIC: {{RESEARCH_TOPIC}}
    PROJECT_LANGUAGE: {{PROJECT_LANGUAGE}}
    WEB_RESEARCH_AVAILABLE: {{WEB_RESEARCH_AVAILABLE}}
    WEB_RESEARCH_SIGNALS: {{WEB_RESEARCH_CONTEXT}}

Agent responsibilities:

1. Load scoring-framework.md at runtime
2. Generate 76 trend candidates using extended thinking (MANDATORY)
3. Apply multi-framework scoring (TIPS, Ansoff, Rogers, CRAAP)
4. Classify indicator types (leading/lagging) and diffusion stages
5. Validate subcategory balance (MIN 1 per subcategory per cell)
6. Validate portfolio balance (≥40% leading indicators)
7. Write full results to {{PROJECT_PATH}}/.logs/trend-generator-candidates.json
8. Return compact JSON summary

Process agent response:

The agent returns compact JSON:

{
  "ok": true,
  "candidates": {"total": 76, "by_source": {...}, "by_dimension": {...}},
  "scoring": {"avg_score": 0.65, "confidence": {...}, "indicator": {...}},
  "validation": {"passed": true, "warnings": []},
  "log": ".logs/trend-generator-candidates.json"
}

Prepare Phase 3 data files:

Execute data preparation script to generate compact candidate data and browser app JSON:

bash "${CLAUDE_PLUGIN_ROOT}/skills/trend-scout/scripts/prepare-phase3-data.sh" "${PROJECT_PATH}"

This generates:

• trend-app-data.json in project root (full data for browser selector)
• trend-selector-app.html in project root (self-contained HTML with embedded data, works with file:// protocol)
• .logs/candidates-compact.json (compact format for Claude reading)

Load compact candidate data:

Read {{PROJECT_PATH}}/.logs/candidates-compact.json to build trend-candidates.md.

Field mapping for compact format:

• d → dimension, h → horizon, n → name
• s → trend_statement, r → research_hint, k → keywords
• sc → score, ct → confidence_tier, si → signal_intensity
• src → source, url → source_url

Required outputs:

• CANDIDATES_BY_CELL loaded from log file
• TOTAL_CANDIDATES = 76
• SCORING_METADATA populated from agent response
• Validation status confirmed

Fallback: If agent returns {"ok": false}, log error and halt workflow.

Phase 3: Present Candidates

Read references/workflow-phases/phase-3-present.md, then execute:

1. Write trend-candidates.md to {PROJECT_PATH}/ (project root)
2. Use bilingual template based on PROJECT_LANGUAGE
3. Include selection tables with checkboxes
4. Include "User Proposed" section
5. Include "More?" column for regeneration requests
6. Include selection summary table
7. Open Visual Selector App:

   # HTML was generated by prepare-phase3-data.sh with embedded data
   # Just open it in browser
   open "${PROJECT_PATH}/trend-selector-app.html"
   

Pre-selection: Top candidates per horizon (5 ACT, 5 PLAN, 3 OBSERVE by score) are automatically pre-selected and marked with a badge. Users can adjust these selections before finalizing. The visual selector app and markdown file both show pre-selected candidates.

PAUSE: After writing files and opening the app, provide persistent access info (in detected language):

English:

Visual Selector App (recommended):

• The app is now open in your browser
• App location: {PROJECT_PATH}/trend-selector-app.html
• Your selections are auto-saved in browser storage
• Click Export when 52 candidates are selected

Alternative: Markdown editing:

1. Open trend-candidates.md in the project folder
2. Mark candidates with [x] (5 ACT, 5 PLAN, 3 OBSERVE per dimension)
3. Optionally add proposals in "User Proposed" section

Re-invoke trend-scout skill when ready.

German:

Visual Selector App (empfohlen):

• Die App ist jetzt in Ihrem Browser geöffnet
• App-Pfad: {PROJECT_PATH}/trend-selector-app.html
• Ihre Auswahl wird automatisch im Browser gespeichert
• Klicken Sie Export wenn 52 Kandidaten ausgewählt sind

Alternative: Markdown-Bearbeitung:

1. Öffnen Sie trend-candidates.md im Projektordner
2. Markieren Sie Kandidaten mit [x] (5 ACT, 5 PLAN, 3 OBSERVE pro Dimension)
3. Fügen Sie optional Vorschläge im Abschnitt "Eigene Vorschläge" hinzu

Rufen Sie trend-scout erneut auf, wenn Sie bereit sind.

IMPORTANT: Always show the full app path so users can reopen it later without asking.

Phase 4: Process User Selection

Read references/workflow-phases/phase-4-process.md, then execute:

1. Read trend-candidates.md from project
2. Parse selected candidates (marked with [x])
3. Parse user-proposed candidates
4. Parse regeneration requests ([+N])
5. Validate: horizon-specific counts (5 ACT, 5 PLAN, 3 OBSERVE per dimension = 52 total)

If validation fails:

• Report which cells have wrong count
• If regeneration requested: generate N more candidates for those cells
• Update trend-candidates.md with new candidates
• PAUSE again for user to adjust

If validation passes: Proceed to Phase 5

Phase 5: Finalize Output

Read references/workflow-phases/phase-5-finalize.md, then execute:

1. Update consolidated trend-scout-output.json with agreed candidates
2. Update trend-candidates.md frontmatter status to agreed
3. Log completion with next-step instructions

Required outputs:

• .metadata/trend-scout-output.json - consolidated output (config + candidates)
• trend-candidates.md status updated to agreed

---

Output Schema

trend-scout-output.json (Consolidated)

Location: {PROJECT_PATH}/.metadata/trend-scout-output.json

{
  "version": "1.0.0",
  "project_id": "automotive-ai-predictive-maintenance-abc12345",
  "project_name": "automotive-ai-predictive-maintenance-abc12345",
  "project_path": "/path/to/project",
  "project_language": "de",
  "created": "2025-12-16T10:30:00Z",

  "config": {
    "research_type": "smarter-service",
    "dok_level": 4,
    "industry": {
      "primary": "manufacturing",
      "primary_en": "Manufacturing",
      "primary_de": "Fertigung",
      "subsector": "automotive",
      "subsector_en": "Automotive",
      "subsector_de": "Automobil"
    },
    "research_topic": "AI-driven predictive maintenance",
    "organizing_concept": "ai-driven-predictive-maintenance"
  },

  "tips_candidates": {
    "total": 52,
    "source_distribution": {
      "web_signal": 18,
      "training": 16,
      "user_proposed": 2
    },
    "web_research_status": "success",
    "search_timestamp": "2025-12-16T10:25:00Z",
    "scoring_metadata": {
      "avg_score": 0.68,
      "confidence_distribution": {
        "high": 12,
        "medium": 18,
        "low": 5,
        "uncertain": 1
      },
      "intensity_distribution": {
        "level_1": 4,
        "level_2": 6,
        "level_3": 10,
        "level_4": 12,
        "level_5": 4
      },
      "indicator_distribution": {
        "leading": 16,
        "lagging": 20,
        "leading_pct": 0.44
      },
      "diffusion_distribution": {
        "innovators": 3,
        "early_adopters": 8,
        "early_majority": 15,
        "late_majority": 8,
        "laggards": 2,
        "pre_chasm": 11,
        "post_chasm": 25
      },
      "scoring_framework_version": "1.0.0"
    },
    "items": [
      {
        "dimension": "externe-effekte",
        "dimension_de": "Externe Effekte",
        "subcategory": "regulierung",
        "subcategory_en": "Regulation",
        "subcategory_de": "Regulierung",
        "horizon": "act",
        "horizon_de": "Handeln",
        "sequence": 1,
        "trend_name": "EU AI Act Compliance",
        "keywords": ["ai-act", "regulation", "2024"],
        "rationale": "Immediate deadline pressure",
        "source": "web-signal",
        "source_url": "https://ec.europa.eu/...",
        "freshness_date": "2024-12",
        "score": 0.82,
        "confidence_tier": "high",
        "signal_intensity": 5,
        "indicator_classification": {
          "type": "leading",
          "lead_time": "12-24 months",
          "source_type": "regulatory"
        },
        "diffusion_stage": {
          "stage": "early_majority",
          "estimated_adoption": 0.25,
          "crossed_chasm": true
        }
      }
    ]
  },

  "execution": {
    "workflow_state": "agreed",
    "current_phase": 5,
    "phases_completed": ["phase-0", "phase-1", "phase-2", "phase-3", "phase-4", "phase-5"],
    "agreed_at": "2025-12-16T11:45:00Z"
  },

  "deeper_analysis_integration": {
    "source_type": "trend-scout",
    "auto_load_candidates": true,
    "skip_tips_selection": true,
    "auto_configure_research_type": true,
    "auto_configure_dok_level": true,
    "auto_configure_language": true
  }
}

---

Selection Constraints

Constraint	Value
Total candidates generated	76 (7 ACT + 7 PLAN + 5 OBSERVE per dimension)
Candidates per horizon	ACT: 7, PLAN: 7, OBSERVE: 5
Pre-selected per horizon	ACT: 5, PLAN: 5, OBSERVE: 3
Selection mode	Pre-select top candidates, user adjustable
Total pre-selected	52 ((5+5+3) × 4 dimensions)
Total agreed candidates	52 (user can adjust pre-selection)

---

Dimension Matrix

Each dimension is used to scout trends. Each trend discovered in any dimension is then analyzed through the complete TIPS framework (T→I→P→S).

Each dimension has 3 subcategories to ensure balanced trend discovery across all aspects:

Dimension	Subcategory	German	Focus	Trend Anchors
externe-effekte	wirtschaft	Wirtschaft	Market forces, competition, economic factors	Multikrise, Digital Transform, Net Neutral
externe-effekte	regulierung	Regulierung	Policy, compliance, legal frameworks	CSR-D/LKSG, EU AI Act, EU Data Act
externe-effekte	gesellschaft	Gesellschaft	Demographics, societal shifts	Demografie, De-Coupling, De-Carbonisation
neue-horizonte	strategie	Strategie	Business model direction, strategic goals	Nachhaltigkeit, Resilienz, OPs Excellence
neue-horizonte	fuehrung	Führung	Leadership approaches, organizational change	Business Agility, Open Leadership, Purpose
neue-horizonte	steuerung	Steuerung	Governance, analytics, control systems	Trends Driven, Risk Management, Predictive KI
digitale-wertetreiber	customer-experience	Customer Experience	Customer touchpoints, engagement	Digital First, Omnichannel, Metaverse
digitale-wertetreiber	produkte-services	Produkte & Services	Offerings, product innovation	Smartification, Digital Twin, Digital Ecosystem
digitale-wertetreiber	geschaeftsprozesse	Geschäftsprozesse	Operations, process optimization	Hyperautomate, Smart Manufacturing, Digi Supply Chain
digitales-fundament	kultur	Kultur	Organizational culture, mindset	New Work, Employee Wellbeing, Data Culture
digitales-fundament	mitarbeitende	Mitarbeitende	Workforce, skills, talent	Digital Workplace, Up/Reskilling, Talent Management
digitales-fundament	technologie	Technologie	Tech infrastructure, platforms	Cyber Security, Data Platforms, Industry X-Cloud

Balancing Rule: Each cell (dimension × horizon) must have at least 1 candidate from each subcategory. With 5 candidates per cell and 3 subcategories, this ensures complete coverage with flexibility.

---

Error Handling

Scenario	Response
Industry not selected	Cannot proceed - prompt user
Project init fails	Exit with error details
All web searches fail	Continue with training-only (warning)
trend-candidates.md not found (Phase 4)	Run Phase 1-3 first
Selection count invalid	Report errors, PAUSE for correction
User proposal off-sector	Warn but allow override

---

Integration with deeper-research-0

After trend-scout completes:

1. User invokes deeper-research-0 skill with explicit path:

   tips_source: {PROJECT_PATH}/.metadata/trend-scout-output.json
   

2. deeper-research-0 Phase 0 loads configuration from consolidated output

3. Auto-configures research_type, DOK level, language from .config

4. deeper-research-0 Phase 2 loads candidates from .tips_candidates.items

5. Research proceeds with auto-selected 52 candidates (5 ACT + 5 PLAN + 3 OBSERVE per dimension)

---

Debugging

Logging

# Log file location
${PROJECT_PATH}/.logs/trend-scout-execution-log.txt

# View phase transitions
grep "\[PHASE\]" "${PROJECT_PATH}/.logs/trend-scout-execution-log.txt"

# View validation results
grep "\[VALIDATION\]" "${PROJECT_PATH}/.logs/trend-scout-execution-log.txt"

Common Issues

1. "Industry not selected" - User must select from taxonomy
2. "Selection count invalid" - Wrong number of candidates marked per cell
3. "File not found" - Ensure trend-candidates.md exists before Phase 4
4. "Project init failed" - Check current working directory is writable (or COGNI_WORKSPACE_ROOT if set)