Manage persistent industry catalogs that accumulate TIPS knowledge across pursuits. Use whenever the user mentions "industry catalog", "catalog", "initialize catalog", "import to catalog", "promote to catalog", "catalog status", "list catalogs", "create catalog", "export catalog", "catalog analytics", "cross-pursuit", "reusable patterns", "industry knowledge base", or wants to manage the persistent repository of TIPs, Solution Templates, SPIs, Metrics, and Collaterals that new pursuits inherit from. Also trigger when the user has completed value-modeler curation (Phase 5) and wants to actually persist the recommendations.
From cogni-trendsnpx claudepluginhub cogni-work/insight-wave --plugin cogni-trendsThis skill is limited to using the following tools:
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Builds production Apache Airflow DAGs using best practices for operators, sensors, testing, and deployment. For data pipelines, workflow orchestration, and batch jobs.
Designs scalable batch/streaming data pipelines, warehouses, lakehouses using Spark, dbt, Airflow, Kafka/Flink, and cloud platforms like Snowflake, BigQuery, Databricks.
This skill reads ALL required state from pursuit project files and catalog directories — it does not depend on prior conversation context. If invoked after trends-resume or value-modeler, context compaction is safe and recommended before starting.
Before executing any operation, run /compact to free working memory. Catalog import operations read the full tips-value-model.json and merge it with existing catalog entities, which benefits from maximum available context.
If /compact is unavailable or this is the first skill in the session, skip compaction and proceed directly.
Manage persistent industry catalogs — the reusable knowledge base that makes each successive customer pursuit faster, more consistent, and more valuable.
Without catalogs, every TIPS pursuit starts from scratch. The trend-scout generates candidates, the value-modeler builds paths and solutions, but all that structured knowledge lives only in the pursuit directory. The next customer engagement in the same industry repeats the same work.
Catalogs solve this by creating a persistent, curated repository per industry/subsector. When a new pursuit is initialized, it inherits relevant TIPs, Solution Templates, SPIs, and Metrics from the catalog — giving the consultant a head start and ensuring consistency across engagements.
This implements the centralized repository concept from the TIPS Value Modeler methodology (WO2018046399A1, Claim 3 and Fig. 4).
cogni-trends/catalogs/{industry}/{subsector}/
├── catalog.json # Root manifest (industry, version, stats)
├── tips-entities.json # Curated TIP entities (T, I, P roles)
├── solution-templates.json # Proven Solution Templates
├── spis.json # Validated Solution Process Improvements
├── metrics.json # Effective success KPIs
├── collaterals.json # Available supporting content
└── .history/ # Version snapshots (catalog.json copies)
Catalogs live at the plugin workspace level (cogni-trends/catalogs/), not inside
any pursuit project directory. They persist across projects and sessions.
Create an empty catalog for an industry/subsector combination.
/trends-catalog init
Steps:
$CLAUDE_PLUGIN_ROOT/skills/trend-scout/references/industry-taxonomy.md)catalog.jsoncatalog.json schema:
{
"version": "1.0.0",
"industry": {
"primary": "manufacturing",
"primary_en": "Manufacturing",
"primary_de": "Fertigung",
"subsector": "automotive",
"subsector_en": "Automotive",
"subsector_de": "Automobil"
},
"created": "2026-03-11T10:00:00Z",
"updated": "2026-03-11T10:00:00Z",
"stats": {
"tips_entities": 0,
"solution_templates": 0,
"spis": 0,
"metrics": 0,
"collaterals": 0,
"pursuits_contributed": 0
},
"taxonomy_template": null,
"pursuit_history": []
}
Import curated entities from a completed value-modeler pursuit into the catalog. This is the write-back loop that makes catalogs grow smarter over time.
/trends-catalog import
Steps:
workflow_state: "curated" or "complete")tips-value-model.json → curation_recommendations),
use them as the starting point — these are pre-analyzed by Phase 5Import rules:
provenance field:{
"source_pursuit": "automotive-ai-predictive-maintenance-abc12345",
"imported_at": "2026-03-11T14:00:00Z",
"original_ref": "st-001",
"generalization_notes": "Replaced 'BMW Regensburg line sensors' with 'production line sensors'"
}
stats counts and add pursuit to pursuit_history:{
"pursuit_slug": "automotive-ai-predictive-maintenance-abc12345",
"imported_at": "2026-03-11T14:00:00Z",
"entities_imported": { "tips": 3, "sts": 2, "spis": 1, "metrics": 2, "collaterals": 0 }
}
/trends-catalog list
List all catalogs in cogni-trends/catalogs/ with their industry, subsector, entity counts,
and last update date. Show a compact table.
/trends-catalog show [industry/subsector]
Display the full contents of a specific catalog with entity counts per type, most recent imports, and coverage summary. If the catalog was initialized from a taxonomy template, show coverage against the template categories.
/trends-catalog analytics [industry/subsector]
Analyze patterns across all pursuits that contributed to a catalog:
Present insights as a structured summary with actionable recommendations.
TIP entities in the catalog are generalized versions of pursuit-specific candidates. They retain the TIPS role (T/I/P) and dimension mapping but are stripped of pursuit context.
{
"entity_id": "cat-tip-001",
"name": "Increasing regulatory pressure on AI systems",
"description": "Governments worldwide are implementing AI governance frameworks that create compliance requirements for industrial AI deployments",
"tips_role": "T",
"dimension": "externe-effekte",
"subcategory": "regulierung",
"keywords": ["ai-regulation", "compliance", "governance"],
"horizon": "act",
"provenance": {
"source_pursuit": "automotive-ai-predictive-maintenance-abc12345",
"imported_at": "2026-03-11T14:00:00Z",
"original_ref": "externe-effekte/act/1",
"generalization_notes": "Broadened from EU AI Act to global AI governance"
},
"pursuit_appearances": 3,
"avg_business_relevance": 4.2
}
{
"st_id": "cat-st-001",
"name": "Predictive Quality Analytics Platform",
"description": "ML-based quality prediction integrated with production line sensor data for real-time defect detection",
"category": "software",
"enabler_type": "process_improvement",
"typical_linked_tips": ["cat-tip-001", "cat-tip-005", "cat-tip-012"],
"generation_mode": "portfolio-anchored",
"blueprint_summary": {
"block_count": 3,
"taxonomy_span": [1, 6, 7],
"taxonomy_depth": 3,
"source_readiness_score": 0.64
},
"portfolio_mapping_hint": {
"suggested_dimension": 6,
"suggested_category": "6.6",
"suggested_category_name": "AI, Data & Analytics"
},
"provenance": {
"source_pursuit": "automotive-ai-predictive-maintenance-abc12345",
"imported_at": "2026-03-11T14:00:00Z",
"original_ref": "st-001",
"generalization_notes": "Removed brand-specific sensor references"
},
"pursuit_appearances": 2,
"avg_ranking_value": 4.1
}
The portfolio_mapping_hint uses b2b-ict-portfolio taxonomy category IDs when a taxonomy
template is loaded. This enables downstream mapping to cogni-portfolio features.
When importing Solution Templates that have solution_blueprint and generation_mode fields
(introduced in the multi-dimensional solutioning model), preserve this data in the catalog entry.
The blueprint captures the full portfolio composition needed to deliver the solution — building
blocks mapped to taxonomy categories with role-weighted readiness scoring.
generation_mode: Carry forward as-is ("portfolio-anchored" or "abstract")solution_blueprint: Generalize building block references (strip pursuit-specific feature slugs)
but preserve the dimensional structure (roles, taxonomy categories, coverage levels)readiness_score: Store as a reference point but note it reflects the source pursuit's portfolio
state — readiness will differ for a new pursuit with a different portfolioSolution Templates without blueprints (older projects) continue to work — blueprint fields are optional.
{
"spi_id": "cat-spi-001",
"name": "Establish data governance policy for sensor data",
"description": "Define data ownership, quality standards, and access controls for production data",
"change_type": "governance",
"typical_st_refs": ["cat-st-001"],
"provenance": { "..." : "..." },
"pursuit_appearances": 2
}
{
"metric_id": "cat-met-001",
"name": "Defect rate reduction",
"unit": "percentage",
"direction": "decrease",
"typical_baseline_range": "2-8%",
"typical_target_range": "0.5-2%",
"provenance": { "..." : "..." },
"pursuit_appearances": 3
}
{
"collateral_id": "cat-col-001",
"name": "Predictive Maintenance ROI Case Study",
"type": "case-study",
"typical_st_refs": ["cat-st-001"],
"status": "exists",
"provenance": { "..." : "..." }
}
Catalogs can be initialized from a taxonomy template that pre-defines the category structure. The template does not provide entities — it provides the dimensional framework that guides what kinds of entities should be collected.
Currently supported template:
| Template | Source | Categories |
|---|---|---|
b2b-ict-portfolio | cogni-trends | 57 categories across 8 dimensions (0-7) |
When a taxonomy is loaded, the catalog gains:
taxonomy_template field in catalog.json pointing to the templateTo use: during /trends-catalog init, when asked about taxonomy, reference the b2b-ict-portfolio
framework. Read the taxonomy definition from:
$CLAUDE_PLUGIN_ROOT/references/taxonomies/b2b-ict-portfolio.md
If the file does not exist yet, the user can provide the taxonomy source or skip taxonomy loading.
When value-modeler Phase 0 discovers a project, it should also search for a matching industry catalog:
tips-project.jsoncogni-trends/catalogs/{industry}/{subsector}/catalog.json.metadata/value-modeler-output.json as catalog_pathThe catalog does NOT replace trend-scout candidates. It supplements them:
When Phase 5 curation completes and generates recommendations:
/trends-catalog import immediately/trends-catalog initThis skill follows the shared language resolution pattern — see $CLAUDE_PLUGIN_ROOT/references/language-resolution.md.
Interaction language: Read workspace language from .workspace-config.json (via ${PROJECT_AGENTS_OPS_ROOT}/.workspace-config.json or CWD) at startup. Use this for all user-facing messages — catalog operation prompts, status displays, import approval questions, and analytics summaries.
Output language: Catalog entity names and descriptions can be bilingual (matching the pursuit language). The industry object always carries both _en and _de variants for consistency with the industry taxonomy.
All catalog operations report results to the user with clear counts and summaries. Import operations always require explicit user approval before writing.