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Get Started Free →Deep competitive analysis and market monitoring capabilities for product strategy
.claude/skills/a5c-ai-competitive-intelligence/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 30% | 0% |
Specialized skill for deep competitive analysis and market monitoring capabilities. Enables product teams to maintain awareness of competitor activities, identify market opportunities, and inform strategic decisions.
This skill integrates with the following processes:
competitive-analysis.js - Primary integration for full competitive analysis workflowsproduct-vision-strategy.js - Strategic positioning and differentiationproduct-launch-gtm.js - Competitive positioning for launchesquarterly-roadmap.js - Market opportunity identificationjson{ "type": "object", "properties": { "competitors": { "type": "array", "items": { "type": "string" }, "description": "List of competitor names or domains to analyze" }, "analysisScope": { "type": "string", "enum": ["pricing", "features", "positioning", "full"], "description": "Scope of competitive analysis" }, "industryContext": { "type": "string", "description": "Industry or market context for analysis" }, "focusAreas": { "type": "array", "items": { "type": "string" }, "description": "Specific areas to focus analysis on" } }, "required": ["competitors", "analysisScope"] }
json{ "type": "object", "properties": { "competitorProfiles": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "positioning": { "type": "string" }, "strengths": { "type": "array", "items": { "type": "string" } }, "weaknesses": { "type": "array", "items": { "type": "string" } }, "pricing": { "type": "object" }, "features": { "type": "array", "items": { "type": "string" } } } } }, "featureMatrix": { "type": "object", "description": "Feature comparison matrix across competitors" }, "marketGaps": { "type": "array", "items": { "type": "string" }, "description": "Identified market opportunities and white spaces" }, "recommendations": { "type": "array", "items": { "type": "string" }, "description": "Strategic recommendations based on analysis" } } }
javascriptconst competitiveAnalysis = await executeSkill('competitive-intel', { competitors: ['CompetitorA', 'CompetitorB', 'CompetitorC'], analysisScope: 'full', industryContext: 'B2B SaaS project management', focusAreas: ['pricing', 'collaboration features', 'integrations'] });
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 54,688 | 40,556 | -26% | 1 | 1 | 0% | 8,270 | 6,620 | -20% | 0 | 0 | — |
case-02 | fail→pass | 45,699 | 24,491 | -46% | 1 | 1 | 0% | 6,906 | 4,290 | -38% | 0 | 0 | — |
case-03 | fail→pass | 32,171 | 20,515 | -36% | 1 | 1 | 0% | 5,347 | 4,804 | -10% | 0 | 0 | — |
case-04 | fail→pass | 45,828 | 24,718 | -46% | 1 | 1 | 0% | 6,992 | 4,481 | -36% | 0 | 0 | — |
case-05 | fail→pass | 25,714 | 21,251 | -17% | 1 | 1 | 0% | 3,624 | 4,768 | +32% | 0 | 0 | — |
case-06 | fail→pass | 29,599 | 31,531 | +7% | 1 | 1 | 0% | 4,176 | 5,424 | +30% | 0 | 0 | — |
case-07 | fail→pass | 38,657 | 35,673 | -8% | 1 | 1 | 0% | 4,991 | 6,278 | +26% | 0 | 0 | — |
case-08 | fail→pass | 27,639 | 28,519 | +3% | 1 | 1 | 0% | 4,794 | 5,172 | +8% | 0 | 0 | — |
case-09 | fail→pass | 50,240 | 28,329 | -44% | 1 | 1 | 0% | 8,216 | 4,890 | -40% | 0 | 0 | — |
case-10 | fail→pass | 43,490 | 27,431 | -37% | 1 | 1 | 0% | 7,509 | 4,979 | -34% | 0 | 0 | — |
case-11 | fail→pass | 37,796 | 28,569 | -24% | 1 | 1 | 0% | 5,870 | 4,998 | -15% | 0 | 0 | — |
case-12 | fail→pass | 23,643 | 28,731 | +22% | 1 | 1 | 0% | 4,339 | 4,211 | -3% | 0 | 0 | — |
case-13 | fail→pass | 28,426 | 21,715 | -24% | 1 | 1 | 0% | 4,063 | 4,621 | +14% | 0 | 0 | — |
case-14 | fail→pass | 33,378 | 25,410 | -24% | 1 | 1 | 0% | 5,617 | 4,519 | -20% | 0 | 0 | — |
case-15 | fail→pass | 38,653 | 22,088 | -43% | 1 | 1 | 0% | 5,923 | 4,658 | -21% | 0 | 0 | — |
case-16 | fail→pass | 41,068 | 19,274 | -53% | 1 | 1 | 0% | 6,519 | 4,406 | -32% | 0 | 0 | — |
case-17 | fail→pass | 33,660 | 28,585 | -15% | 1 | 1 | 0% | 5,526 | 4,807 | -13% | 0 | 0 | — |
case-18 | fail→pass | 47,211 | 18,993 | -60% | 1 | 1 | 0% | 7,468 | 4,161 | -44% | 0 | 0 | — |
case-19 | pass→fail | 36,525 | 27,822 | -24% | 1 | 1 | 0% | 5,659 | 4,859 | -14% | 0 | 0 | — |
case-20 | pass→pass | 18,890 | 20,733 | +10% | 1 | 1 | 0% | 2,395 | 3,832 | +60% | 0 | 0 | — |
case-21 | pass→pass | 10,474 | 4,176 | -60% | 1 | 1 | 0% | 1,020 | 1,651 | +62% | 0 | 0 | — |
case-22 | pass→pass | 25,620 | 32,032 | +25% | 1 | 1 | 0% | 4,137 | 5,119 | +24% | 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. 22 cases were attempted. The headline lift of +73 percentage points is the difference between those two pass rates over the 22 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.