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Get Started Free →Generate AGENT.md files with proper YAML frontmatter, role definitions, expertise areas, and prompt templates following Babysitter SDK conventions.
.claude/skills/a5c-ai-agent-generator/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 46% | 0% |
You are agent-generator - a specialized skill for generating Claude Code agent files (AGENT.md) with proper structure, frontmatter, and prompt templates.
This skill generates complete AGENT.md files including:
yaml--- name: agent-name description: Comprehensive agent description role: Role Category expertise: - Expertise area 1 - Expertise area 2 - Expertise area 3 ---
# Agent Name AgentGenerate valid YAML frontmatter:
yaml--- name: data-analyst description: Expert in data analysis, visualization, and statistical methods role: Analysis expertise: - Statistical analysis - Data visualization - Pattern recognition - Trend analysis - Report generation ---
Create effective prompt templates:
javascript{ role: 'Senior Data Analyst', expertise: [ 'Statistical analysis', 'Data visualization', 'Pattern recognition' ], task: 'Analyze the provided dataset', guidelines: [ 'Identify key patterns and trends', 'Apply appropriate statistical methods', 'Create clear visualizations', 'Provide actionable insights', 'Document methodology used' ], outputFormat: 'JSON with analysis, findings, and recommendations' }
Define clear capabilities:
markdown## Capabilities - Analyze datasets of various sizes and formats - Apply statistical methods (regression, clustering, etc.) - Create visualizations (charts, graphs, dashboards) - Identify patterns and anomalies - Generate actionable recommendations - Document analysis methodology
Document collaboration patterns:
markdown## Interaction Patterns - Collaborates with Data Engineer for data preparation - Works with Visualization Designer for chart creation - Coordinates with Domain Expert for context - Reports to Quality Assessor for validation
json{ "agentPath": "path/to/agent-name/AGENT.md", "frontmatter": { "name": "agent-name", "description": "...", "role": "Category", "expertise": ["area1", "area2"] }, "promptTemplate": { "role": "...", "expertise": [], "task": "...", "guidelines": [], "outputFormat": "..." }, "artifacts": [ { "path": "path/to/agent-name/AGENT.md", "type": "markdown", "label": "Agent definition" } ] }
This skill integrates with:
agent-creation.js - Primary agent generationphase6-create-skills-agents.js - Batch agent creationspecialization-creation.js - Full specialization workflow| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 35,457 | 29,370 | -17% | 1 | 1 | 0% | 5,334 | 4,986 | -7% | 0 | 0 | — |
case-02 | fail→pass | 22,463 | 12,561 | -44% | 1 | 1 | 0% | 2,483 | 3,403 | +37% | 0 | 0 | — |
case-03 | fail→pass | 26,698 | 15,920 | -40% | 1 | 1 | 0% | 4,032 | 3,393 | -16% | 0 | 0 | — |
case-04 | fail→fail | 17,481 | 14,758 | -16% | 1 | 1 | 0% | 2,599 | 4,183 | +61% | 0 | 0 | — |
case-05 | fail→pass | 15,887 | 17,220 | +8% | 1 | 1 | 0% | 3,054 | 3,640 | +19% | 0 | 0 | — |
case-06 | fail→pass | 38,907 | 19,920 | -49% | 1 | 1 | 0% | 2,786 | 4,062 | +46% | 0 | 0 | — |
case-07 | fail→fail | 15,373 | 14,565 | -5% | 1 | 1 | 0% | 3,047 | 3,116 | +2% | 0 | 0 | — |
case-08 | fail→fail | 16,723 | 16,056 | -4% | 1 | 1 | 0% | 2,819 | 4,410 | +56% | 0 | 0 | — |
case-09 | fail→fail | 16,285 | 11,499 | -29% | 1 | 1 | 0% | 2,448 | 3,216 | +31% | 0 | 0 | — |
case-10 | fail→fail | 19,335 | 9,584 | -50% | 1 | 1 | 0% | 2,461 | 2,764 | +12% | 0 | 0 | — |
case-11 | fail→fail | 23,475 | 11,186 | -52% | 1 | 1 | 0% | 3,382 | 3,147 | -7% | 0 | 0 | — |
case-12 | pass→pass | 18,308 | 13,943 | -24% | 1 | 1 | 0% | 2,587 | 3,375 | +30% | 0 | 0 | — |
case-13 | fail→fail | 16,855 | 12,506 | -26% | 1 | 1 | 0% | 2,573 | 3,776 | +47% | 0 | 0 | — |
case-14 | fail→fail | 8,984 | 18,972 | +111% | 1 | 1 | 0% | 1,721 | 3,802 | +121% | 0 | 0 | — |
case-15 | fail→fail | 16,428 | 14,600 | -11% | 1 | 1 | 0% | 2,840 | 4,010 | +41% | 0 | 0 | — |
case-16 | fail→fail | 25,744 | 18,771 | -27% | 1 | 1 | 0% | 4,135 | 3,936 | -5% | 0 | 0 | — |
case-17 | fail→fail | 29,823 | 14,297 | -52% | 1 | 1 | 0% | 4,421 | 4,048 | -8% | 0 | 0 | — |
case-18 | fail→fail | 13,681 | 13,186 | -4% | 1 | 1 | 0% | 2,393 | 3,033 | +27% | 0 | 0 | — |
case-19 | fail→fail | 7,313 | 7,029 | -4% | 1 | 1 | 0% | 1,204 | 2,300 | +91% | 0 | 0 | — |
case-20 | fail→fail | 35,694 | 9,229 | -74% | 1 | 1 | 0% | 5,228 | 2,685 | -49% | 0 | 0 | — |
case-21 | fail→pass | 15,344 | 12,585 | -18% | 1 | 1 | 0% | 2,439 | 2,851 | +17% | 0 | 0 | — |
case-22 | pass→pass | 6,165 | 3,811 | -38% | 1 | 1 | 0% | 972 | 1,587 | +63% | 0 | 0 | — |
case-23 | pass→pass | 19,091 | 30,959 | +62% | 1 | 1 | 0% | 3,902 | 4,355 | +12% | 0 | 0 | — |
case-24 | pass→pass | 14,201 | 11,129 | -22% | 1 | 1 | 0% | 2,190 | 2,600 | +19% | 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. 24 cases were attempted. The headline lift of +25 percentage points is the difference between those two pass rates over the 24 comparable cases.
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.