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Autosolutions Npd Validator

Run a full NPD validation pipeline on a product concept. Orchestrates 8 specialized subagents (Research Coordinator, 5 Independent Evaluators, Devil's Advocate, Consensus Director) to produce an evidence-based GO / CONDITIONAL GO / REVISIT / NO-GO recommendation. Use when someone asks to validate a product idea, assess market fit, or evaluate whether a product should be launched.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/autosolutionsai-didac/autosolutions-npd-validator
Or

NPD Product Validation Pipeline

You are orchestrating a multi-agent product validation. Read the methodology skill at skills/npd-methodology/SKILL.md before proceeding.

Step 1: Determine Entry Mode

Analyze "$ARGUMENTS" to determine entry mode:

Mode A (Rough Idea): User provided <4 of 6 Concept Brief fields.

  • Ask targeted questions to fill the brief
  • Run a Quick Scan (1-2 searches per dimension, traffic light output)
  • Get user confirmation before deep validation

Mode B (Solid Idea): User provided ≥4 fields or said "just validate."

  • Confirm understanding
  • Populate the Concept Brief
  • Proceed to deep validation

Step 2: Populate Concept Brief

Create data/concept_brief.md with:

Product Name / Working Title: [...]
Category: [...]
One-Line Description: [≤15 words]
Target Consumer: [...]
Price Point Range: [...]
Key Differentiator: [...]
Launch Horizon: [now / 6 months / 12 months / 24+ months — specify target month if seasonal]
Brand Context (optional): [...]

If the user hasn't specified a launch horizon, ASK before proceeding. A trend that scores Timing 9/10 for launch "now" may score 3/10 for launch in 24 months.

Brief Coherence Check

Before launching the pipeline, scan the brief for internal contradictions:

  • Positioning vs Price: Does the price match the positioning? ("ultra-premium" at $5 is contradictory)
  • Target vs Positioning: Does the target consumer align with the positioning? ("budget-conscious teens" vs "luxury" is contradictory)
  • Differentiator vs Category: Does the differentiator make sense for this category?

If contradictions are found: flag them to the user with a specific question, e.g., "You described this as ultra-premium but priced at $5 — which is the actual intent? The validation results would be very different." Do NOT reject the idea — the user may have a valid strategy. But get clarity before investing 25+ searches in a pipeline built on an incoherent brief.

Multi-SKU Launch Detection

Multi-SKU launches come in two distinct flavors — identify which one applies before proceeding:

Bundle / Collection / Set: Multiple products sold together as a unit at a combined price (e.g., "The Morning Routine — 3 products, $65 together"). Consumers buy them as ONE transaction.

Line Launch / Drop: Multiple distinct products launched simultaneously but sold separately (e.g., "Full skincare line: cleanser $24, toner $22, serum $38, moisturizer $32, SPF $28 — available individually"). Consumers pick what they want.

Each has different dynamics:

Bundles — see Bundle-specific evaluator guidance below. Attachment rate is fixed at 100% for bundle buyers. SKU complexity is lower (1 bundle SKU + 3 components).

Metadata

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Updated2026-05-01
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-autosolutionsai-didac-autosolutions-npd-validator": {
      "enabled": true,
      "auto_update": true
    }
  }
}
Safety NoteClawKit audits metadata but not runtime behavior. Use with caution.

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