install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/adarshvmore/report-generator-adarsh" ~/.claude/skills/clawdbot-skills-report-generator-adarsh && rm -rf "$T"
manifest:
skills/adarshvmore/report-generator-adarsh/SKILL.mdsource content
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
interface AuditData { input: AuditInput; instagram: InstagramData; metaAds: MetaAdsData; keywords: KeywordData; competitors: CompetitorData; websiteAudit: WebsiteAuditData; collectedAt: string; }
Output Schema
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: 1500temperature: 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
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 modules