Business Value Fit

Value Proposition Fit Auto-Evaluation

Core Principles

1. Pain Point Coverage First -- High-frequency, high-severity pain points must be covered by value propositions; omissions trigger warnings
2. 531 Scoring Scale -- Fit evaluation uses 5/3/1/0 four-level scoring with unified standards, no ambiguity
3. Transparent Weighted Calculation -- Pain weight = frequency x severity; gain weight = importance x satisfaction gap
4. Gaps Require Action -- Uncovered pain points and gains must include improvement recommendations, not just labels

Execution Cycle: Automatically triggered after Pipeline 1 (Business Model Canvas) is complete

Core Objective: Systematically evaluate the degree of fit between value propositions and users' actual pain points and expected gains, identifying coverage blind spots and improvement opportunities.

Interaction Mode

🤖→👤 AI suggests, human approves

Input

| Input Item | Type | Required | Source | Description | |--------|------|------|------|------| | BMC Value Propositions | JSON | Yes | output/pm-strategy/business-model-canvas/bmc.json | Value propositions list, including Pain Relievers and Gain Creators | | User Research Data | JSON | Yes | user-research-user-modeling / user-research-voice-analysis | User personas, pain points, expected gains, opportunity brief |

Required Input

Value Propositions from BMC (from Pipeline 1):

{
  "value_propositions": [
    {
      "proposition_id": "vp-1",
      "headline": "Value proposition headline",
      "description": "Value proposition detailed description",
      "target_segment": "segment-1",
      "pain_relievers": ["Pain point addressed 1", "Pain point addressed 2"],
      "gain_creators": ["Gain created 1", "Gain created 2"]
    }
  ]
}

Exploration Phase User Research Data:

{
  "persona_summary": {
    "demographics": "Demographic characteristics",
    "behaviors": "User behavioral characteristics",
    "goals": "User goals"
  },
  "problem_statement": {
    "pains": [
      {
        "pain_id": "pain-1",
        "description": "Pain point description",
        "frequency": "Occurrence frequency",
        "severity": "Severity level",
        "urgency": "Urgency level"
      }
    ],
    "gains": [
      {
        "gain_id": "gain-1",
        "description": "Expected gain description",
        "importance": "Importance level",
        "current_satisfaction": "Current satisfaction level"
      }
    ]
  },
  "opportunity_definition": {
    "opportunity_description": "Enterprise training digitalization penetration rate only 28%, AI personalized learning demand growing 45% annually",
    "evidence": ["iResearch 2024 enterprise training market report", "State Council vocational education reform implementation plan"]
  }
}

Execution Steps

Step 1: Pain Point Alignment Assessment [Core]

Task: Systematically evaluate the coverage of each Pain Reliever against user pain points.

Scoring Standard (5/3/1 scoring):

| Score | Meaning | Judgment Criteria | |------|------|----------| | 5 | Perfect coverage | Value proposition fully addresses the core of the pain point, users can significantly perceive it | | 3 | Partial coverage | Value proposition addresses the pain point but not the core dimension, or with limited effectiveness | | 1 | Edge coverage | Value proposition has weak association with the pain point, only indirect impact | | 0 | Not covered | Value proposition does not address this pain point |

Execution Logic:

1. Iterate through each Pain Reliever
2. Match against each pain point in the problem statement
3. Determine match score based on scoring criteria
4. Calculate weighted average score (weight: frequency x severity)

Output Format:

{
  "pain_alignment": {
    "covered_pains": [
      {
        "pain_id": "pain-1",
        "pain_description": "Training effectiveness difficult to quantify and track",
        "matched_by": ["vp-1"],
        "coverage_score": 5,
        "coverage_quality": "full/partial/edge/none",
        "notes": "AI learning report feature fully covers this pain point"
      }
    ],
    "uncovered_pains": [
      {
        "pain_id": "pain-5",
        "pain_description": "Student learning paths lack personalization",
        "frequency": "high",
        "severity": "high",
        "impact": "High-frequency high-severity pain point 'Course content disconnected from job requirements' not covered",
        "recommendation": "Recommend adding job skill graph matching feature"
      }
    ],
    "pain_coverage_summary": {
      "total_pains": 10,
      "fully_covered": 4,
      "partially_covered": 3,
      "uncovered": 3,
      "weighted_average_score": 3.2,
      "high_frequency_coverage_rate": "80%"
    }
  }
}

Acceptance Criteria:

• All Pain Relievers matched against pain points
• Each pain point has clear coverage status
• Omitted pain points include improvement recommendations

Step 2: Gain Creation Validation [Core]

Task: Evaluate the match between Gain Creators and users' expected gains.

Execution Logic:

1. Iterate through each Gain Creator
2. Match against expected gains in the problem statement
3. Evaluate the authenticity and achievability of gain creation
4. Identify user expectations not promised

Output Format:

{
  "gain_validation": {
    "covered_gains": [
      {
        "gain_id": "gain-1",
        "gain_description": "Training ROI quantifiable",
        "created_by": ["vp-1"],
        "coverage_status": "covered/partial/not_covered",
        "realizability": "high/medium/low",
        "notes": "AI learning report + ROI dashboard achievable, high technical maturity"
      }
    ],
    "uncovered_gains": [
      {
        "gain_id": "gain-3",
        "gain_description": "Increased student self-directed learning motivation",
        "importance": "high",
        "gap_analysis": "Users expect social learning experience but current value proposition does not address it",
        "recommendation": "Recommend including learning community feature in V2.0 planning"
      }
    ],
    "gain_summary": {
      "total_gains": 8,
      "covered": 5,
      "partial": 2,
      "uncovered": 1,
      "alignment_rate": "75%"
    }
  }
}

Acceptance Criteria:

• All Gain Creators validated
• Uncovered gains identified and importance assessed
• Achievability assessment reasonable

Step 3: Overall Fit Score [Core]

Task: Synthesize pain point coverage and gain creation to calculate overall fit score.

Weighted Average Calculation:

Overall Fit Score = (Pain Alignment Score x 0.6) + (Gain Validation Score x 0.4)

Score Interpretation: | Score Range | Meaning | Action Recommendation | |----------|------|----------| | 4.0-5.0 | Excellent fit | Value proposition design is sound, can proceed to next phase | | 3.0-3.9 | Good fit | Room for improvement, recommend optimizing before proceeding | | 2.0-2.9 | Fair fit | Notable gaps exist, value propositions need adjustment | | 1.0-1.9 | Poor fit | Significant misalignment between value propositions and user needs | | 0-0.9 | Severe misalignment | Value propositions need to be redesigned |

Output Format:

{
  "overall_fit_score": 3.4,
  "score_interpretation": "Good fit",
  "score_breakdown": {
    "pain_alignment_score": 3.5,
    "pain_weight": 0.6,
    "pain_contribution": 2.1,
    "gain_validation_score": 3.25,
    "gain_weight": 0.4,
    "gain_contribution": 1.3
  },
  "coverage_rate": {
    "pain_coverage": "80%",
    "gain_coverage": "75%",
    "high_priority_coverage": "85%"
  }
}

Output Depth Grading

| Depth Level | Output Scope | Description | |----------|----------|------| | quick | value-market fit assessment | Core conclusions + minimum viable deliverable | | standard | Full deliverables (default) | Complete output including all Steps | | deep | Full assessment + value-market fit matrix + gap analysis + optimization roadmap | Full deliverables + extended analysis + deep simulation |

Output

Storage Path: output/pm-strategy/business-value-fit/

Output File: evaluation_report.json

Output Validation Rules

| Field Path | Type | Required | Description | |----------|------|------|------| | evaluationreport.evaluationmetadata.evaluatedat | string | Yes | Evaluation timestamp | | evaluationreport.evaluationmetadata.valuepropositionsevaluated | number | Yes | Number of value propositions evaluated | | evaluationreport.evaluationmetadata.painsanalyzed | number | Yes | Number of pain points analyzed | | evaluationreport.evaluationmetadata.gainsanalyzed | number | Yes | Number of gains analyzed | | evaluationreport.evaluationmetadata.confidence | string | Yes | high/medium/low | | evaluationreport.painalignment.coveredpains | array | Yes | Covered pain points list | | evaluationreport.painalignment.coveredpains[].painid | string | Yes | Pain point ID, must not be empty | | evaluationreport.painalignment.coveredpains[].coveragescore | number | Yes | Coverage score, 0-5 | | evaluationreport.painalignment.coveredpains[].coveragequality | string | Yes | Coverage quality, enum: full/partial/edge/none | | evaluationreport.painalignment.uncoveredpains | array | Yes | Uncovered pain points list, each with recommendation | | evaluationreport.painalignment.uncoveredpains[].painid | string | Yes | Pain point ID, must not be empty | | evaluationreport.painalignment.uncoveredpains[].frequency | string | Yes | Frequency, enum: high/medium/low | | evaluationreport.painalignment.uncoveredpains[].severity | string | Yes | Severity, enum: high/medium/low | | evaluationreport.painalignment.uncoveredpains[].recommendation | string | Yes | Improvement suggestion, must not be empty | | evaluationreport.painalignment.paincoveragesummary | object | Yes | Coverage statistics | | evaluationreport.painalignment.paincoveragesummary.totalpains | number | Yes | Total pain points count | | evaluationreport.painalignment.paincoveragesummary.fullycovered | number | Yes | Fully covered count | | evaluationreport.painalignment.paincoveragesummary.uncovered | number | Yes | Uncovered count | | evaluationreport.gainvalidation.coveredgains | array | Yes | Covered gains list | | evaluationreport.gainvalidation.coveredgains[].gainid | string | Yes | Gain ID, must not be empty | | evaluationreport.gainvalidation.coveredgains[].coveragestatus | string | Yes | Coverage status, enum: covered/partial/notcovered | | evaluationreport.gainvalidation.coveredgains[].realizability | string | Yes | Realizability, enum: high/medium/low | | evaluationreport.gainvalidation.uncoveredgains | array | Yes | Uncovered gains list, each with recommendation | | evaluationreport.gainvalidation.uncoveredgains[].gainid | string | Yes | Gain ID, must not be empty | | evaluationreport.gainvalidation.uncoveredgains[].importance | string | Yes | Importance, enum: high/medium/low | | evaluationreport.gainvalidation.uncoveredgains[].recommendation | string | Yes | Improvement suggestion, must not be empty | | evaluationreport.overallfitscore | number | Yes | Overall fit score 0-5 | | evaluationreport.coveragerate | object | Yes | Coverage metrics | | evaluationreport.improvementsuggestions | array | Yes | Improvement suggestions list | | evaluationreport.improvementsuggestions[].priority | string | Yes | Priority, enum: high/medium/low | | evaluationreport.improvementsuggestions[].category | string | Yes | Suggestion category, enum: addpaincoverage/enhancegain/clarifymessage/reposition | | evaluationreport.improvementsuggestions[].description | string | Yes | Suggestion description, must not be empty | | evaluationreport.warnings | array | Yes | Warnings list | | evaluationreport.warnings[].warningtype | string | Yes | Warning type, e.g. highfrequencyuncovered | | evaluationreport.warnings[].description | string | Yes | Warning description, must not be empty | | evaluationreport.warnings[].severity | string | Yes | Severity, enum: high/medium/low |

Complete Evaluation Report

{
  "evaluation_report": {
    "evaluation_metadata": {
      "evaluated_at": "2024-06-15T14:20:00Z",
      "value_propositions_evaluated": 3,
      "pains_analyzed": 10,
      "gains_analyzed": 8,
      "confidence": "high/medium/low"
    },
    "pain_alignment": {...},
    "gain_validation": {...},
    "overall_fit_score": {...},
    "coverage_rate": {...},
    "improvement_suggestions": [
      {
        "suggestion_id": "sug-1",
        "priority": "high/medium/low",
        "category": "add_pain_coverage/enhance_gain/clarify_message/reposition",
        "description": "Add AI learning path effectiveness visualization feature to cover student progress tracking pain point",
        "expected_impact": "Pain point coverage rate increases 15%, fit score improves 0.5 points",
        "implementation_effort": "Medium, requires 2 sprint development cycles"
      }
    ],
    "warnings": [
      {
        "warning_type": "high_frequency_uncovered",
        "description": "High-frequency pain point 'Training effectiveness difficult to quantify' not covered by value proposition",
        "affected_pains": ["pain-3", "pain-7"],
        "severity": "high"
      }
    ]
  }
}

Decision Rules

Warning Trigger Rules

1. High-Frequency Pain Point Omission Warning:

• Trigger condition: Pain point with frequency >=20% not covered
• Action: Generate warning, explicitly label affected pain points
• Severity: High

1. High-Severity Pain Point Omission Warning:

• Trigger condition: Pain point coverage rate for severity=high Directly evaluate fit | Lacks BMC structured data, value propositions may be incomplete | Request user to provide product value proposition description, or upload bmc.json |

| User research data (voice-analysis / persona) | User provides value propositions and user pain points -> Directly evaluate fit | Lacks user research data, pain point frequency and severity lack empirical evidence | Request user to describe user pain points, or upload persona.json / voice-analysis.json | | bmc.json + User research data | User provides value proposition and user pain point descriptions -> Directly evaluate fit | Overall confidence reduced, scoring lacks data anchoring | Request user to describe value propositions and pain points, or upload bmc.json / persona.json / voice-analysis.json | | All upstream files missing | Prompt user to execute prior phases first, or evaluate fit based on user-provided value propositions and pain points | Overall confidence significantly reduced, evaluation is assumption-based only | Request user to provide product value propositions, target user pain points, and core feature descriptions |

Data Acquisition Instructions

This Skill requires BMC and user research data, please provide through one of the following methods:

1. Directly describe value propositions and user pain points
2. Upload bmc.json / persona.json / voice-analysis.json files
3. Provide data file paths

• AI is not responsible for external data collection, only for analysis

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Upstream Change Response

Upstream Change Impact Table

| Upstream Change | Impact Scope | Response Strategy | |----------|----------|----------| | bmc.json value proposition change | Pain point alignment and gain creation validation need re-evaluation | Re-execute Step 1-3, update fit scores | | bmc.json customer segment adjustment | Value proposition and segment group correspondence | Re-match value propositions with target users | | persona/voice-analysis user pain point update | Pain point coverage rate and omission analysis | Re-execute Step 1, update uncovered pain points list | | problem-statement problem statement change | Pain point and gain priority weights | Recalculate weighted scores, update overall fit assessm

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: LuckyOneTwoThree
• Source: LuckyOneTwoThree/vibe-skill
• License: MIT
• Homepage: https://luckyonetwothree.github.io/all-skill-html/

Install and usage instructions live in the source repository linked above.