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Get Started Free →Create and maintain user personas from research data for product targeting
.claude/skills/a5c-ai-persona-development/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 158% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 167% | 0% |
Specialized skill for creating and maintaining user personas from research data. Enables product teams to develop rich, data-driven personas that guide product decisions and marketing strategies.
This skill integrates with the following processes:
user-story-mapping.js - Persona-driven story mappingjtbd-analysis.js - Jobs per persona analysisfeature-definition-prd.js - Persona targeting in PRDsproduct-launch-gtm.js - Persona-based launch targetingjson{ "type": "object", "properties": { "mode": { "type": "string", "enum": ["create", "update", "analyze", "map"], "description": "Operation mode" }, "researchData": { "type": "object", "properties": { "interviews": { "type": "array", "items": { "type": "object" } }, "surveys": { "type": "array", "items": { "type": "object" } }, "analytics": { "type": "object" }, "supportTickets": { "type": "array", "items": { "type": "object" } } }, "description": "Research data sources for persona creation" }, "existingPersonas": { "type": "array", "items": { "type": "object", "properties": { "id": { "type": "string" }, "name": { "type": "string" }, "description": { "type": "string" }, "attributes": { "type": "object" } } } }, "segmentationCriteria": { "type": "array", "items": { "type": "string" }, "description": "Criteria for persona segmentation" }, "productFeatures": { "type": "array", "items": { "type": "string" }, "description": "Features to map to personas" } }, "required": ["mode"] }
json{ "type": "object", "properties": { "personas": { "type": "array", "items": { "type": "object", "properties": { "id": { "type": "string" }, "name": { "type": "string" }, "tagline": { "type": "string" }, "demographics": { "type": "object", "properties": { "role": { "type": "string" }, "industry": { "type": "string" }, "companySize": { "type": "string" }, "experience": { "type": "string" } } }, "psychographics": { "type": "object", "properties": { "goals": { "type": "array", "items": { "type": "string" } }, "frustrations": { "type": "array", "items": { "type": "string" } }, "motivations": { "type": "array", "items": { "type": "string" } }, "behaviors": { "type": "array", "items": { "type": "string" } } } }, "jobs": { "type": "array", "items": { "type": "object", "properties": { "job": { "type": "string" }, "importance": { "type": "string" }, "currentSolution": { "type": "string" } } } }, "quotes": { "type": "array", "items": { "type": "string" } }, "marketSize": { "type": "object", "properties": { "tam": { "type": "string" }, "sam": { "type": "string" }, "som": { "type": "string" } } } } } }, "featureMapping": { "type": "object", "description": "Mapping of features to personas with priority" }, "comparisonMatrix": { "type": "object", "description": "Comparison of personas across key dimensions" }, "recommendations": { "type": "array", "items": { "type": "string" } } } }
javascriptconst personas = await executeSkill('persona-development', { mode: 'create', researchData: { interviews: [ { id: 'int-1', role: 'Product Manager', painPoints: ['...'], goals: ['...'] }, { id: 'int-2', role: 'Developer', painPoints: ['...'], goals: ['...'] } ], analytics: { userSegments: ['enterprise', 'smb', 'startup'], behaviorPatterns: ['power-user', 'casual', 'admin'] } }, segmentationCriteria: ['role', 'company_size', 'use_case'] });
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 27,897 | 39,499 | +42% | 1 | 1 | 0% | 4,637 | 6,241 | +35% | 0 | 0 | — |
case-02 | fail→fail | 27,773 | 30,448 | +10% | 1 | 1 | 0% | 3,900 | 6,057 | +55% | 0 | 0 | — |
case-03 | fail→fail | 21,596 | 19,182 | -11% | 1 | 1 | 0% | 3,725 | 5,003 | +34% | 0 | 0 | — |
case-04 | pass→pass | 15,036 | 21,094 | +40% | 1 | 1 | 0% | 2,960 | 5,787 | +96% | 0 | 0 | — |
case-05 | pass→pass | 12,256 | 13,314 | +9% | 1 | 1 | 0% | 2,269 | 3,788 | +67% | 0 | 0 | — |
case-06 | pass→pass | 10,736 | 8,887 | -17% | 1 | 1 | 0% | 2,415 | 3,355 | +39% | 0 | 0 | — |
case-07 | fail→pass | 36,178 | 24,911 | -31% | 1 | 1 | 0% | 6,598 | 5,907 | -10% | 0 | 0 | — |
case-08 | fail→pass | 22,525 | 25,688 | +14% | 1 | 1 | 0% | 4,005 | 5,933 | +48% | 0 | 0 | — |
case-09 | fail→pass | 13,892 | 28,069 | +102% | 1 | 1 | 0% | 2,515 | 6,488 | +158% | 0 | 0 | — |
case-10 | fail→pass | 15,579 | 19,079 | +22% | 1 | 1 | 0% | 3,210 | 4,913 | +53% | 0 | 0 | — |
case-11 | pass→pass | 18,139 | 22,667 | +25% | 1 | 1 | 0% | 2,898 | 4,890 | +69% | 0 | 0 | — |
case-12 | fail→pass | 10,485 | 23,473 | +124% | 1 | 1 | 0% | 1,947 | 5,192 | +167% | 0 | 0 | — |
case-13 | fail→pass | 15,149 | 24,936 | +65% | 1 | 1 | 0% | 2,881 | 6,174 | +114% | 0 | 0 | — |
case-14 | pass→pass | 21,413 | 16,001 | -25% | 1 | 1 | 0% | 3,931 | 3,996 | +2% | 0 | 0 | — |
case-15 | pass→fail | 17,014 | 26,464 | +56% | 1 | 1 | 0% | 2,980 | 5,506 | +85% | 0 | 0 | — |
case-16 | fail→pass | 14,597 | 18,193 | +25% | 1 | 1 | 0% | 2,810 | 4,960 | +77% | 0 | 0 | — |
case-17 | fail→pass | 20,589 | 25,804 | +25% | 1 | 1 | 0% | 3,788 | 6,025 | +59% | 0 | 0 | — |
case-18 | fail→pass | 29,362 | 29,291 | -0% | 1 | 1 | 0% | 4,621 | 6,952 | +50% | 0 | 0 | — |
case-19 | fail→pass | 18,844 | 22,697 | +20% | 1 | 1 | 0% | 3,478 | 5,433 | +56% | 0 | 0 | — |
case-20 | fail→pass | 16,218 | 31,441 | +94% | 1 | 1 | 0% | 2,751 | 6,875 | +150% | 0 | 0 | — |
case-21 | fail→fail | 21,996 | 32,569 | +48% | 1 | 1 | 0% | 3,939 | 5,846 | +48% | 0 | 0 | — |
case-22 | pass→pass | 34,292 | 26,295 | -23% | 1 | 1 | 0% | 6,240 | 5,846 | -6% | 0 | 0 | — |
case-23 | pass→pass | 17,185 | 19,297 | +12% | 1 | 1 | 0% | 2,643 | 4,982 | +88% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +43 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.