Best use case
Report Generator Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Purpose
Teams using Report Generator Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/report-generator-adarsh/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Report Generator Skill Compares
| Feature / Agent | Report Generator Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Purpose
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Report Generator Skill
## Purpose
Single GPT-4.1-mini call that transforms aggregated marketing data into a structured, professional audit report. This is the ONLY AI call in the entire audit pipeline.
## Input Schema
```typescript
interface AuditData {
input: AuditInput;
instagram: InstagramData;
metaAds: MetaAdsData;
keywords: KeywordData;
competitors: CompetitorData;
websiteAudit: WebsiteAuditData;
collectedAt: string;
}
```
## Output Schema
```typescript
interface MarketingReport {
brand: string;
generatedAt: string;
sections: ReportSections;
rawData: AuditData;
reportMarkdown: string;
}
```
## API Dependencies
- **API:** OpenAI
- **Model:** `gpt-4.1-mini`
- **Auth:** `OPENAI_API_KEY`
- **Cost:** ~$0.001-0.002 per call
## Implementation Pattern
- System prompt defines the analyst persona and exact 6-section format
- User message is the full AuditData JSON
- Single API call with `max_tokens: 1500`, `temperature: 0.4`
- Response markdown is parsed into individual sections via `## ` header splitting
- Token usage is logged for cost tracking
- Fallback report is generated if OpenAI call fails
## Token Budget
- Input: ~1,500-2,000 tokens (JSON data)
- Output: ~800-1,200 tokens (report)
## Error Handling
- Missing API key: returns fallback report with error message
- API failure: returns fallback report with raw data preserved
- All errors logged with context
## Example Usage
```typescript
const report = await generateMarketingReport(auditData);
console.log(report.reportMarkdown);
```
## Notes
- This is the ONLY file that should make OpenAI calls (except competitor collector fallback)
- Never add additional GPT calls to other modulesRelated Skills
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