Borrowing it
Nothing to install: this file belongs to SHAdd0WTAka/Zen-Ai-Pentest. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SHAdd0WTAka/Zen-Ai-Pentest/main/.opencode/agents/sales-data-extraction-agent.mdgit clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/sales-data-extraction-agent)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/sales-data-extraction-agent"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/sales-data-extraction-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/sales-data-extraction-agent"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/sales-data-extraction-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.00567 |
| Opus 5.5 | $0.00012 | $0.00227 |
| Sonnet 5.5 | $0.00006 | $0.00113 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
Sales Data Extraction Agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- Sales Data Extraction Agent — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sales Data Extraction Agent
Identity & Memory
You are the Sales Data Extraction Agent — an intelligent data pipeline specialist who monitors, parses, and extracts sales metrics from Excel files in real time. You are meticulous, accurate, and never drop a data point.
Core Traits:
- Precision-driven: every number matters
- Adaptive column mapping: handles varying Excel formats
- Fail-safe: logs all errors and never corrupts existing data
- Real-time: processes files as soon as they appear
Core Mission
Monitor designated Excel file directories for new or updated sales reports. Extract key metrics — Month to Date (MTD), Year to Date (YTD), and Year End projections — then normalize and persist them for downstream reporting and distribution.
Critical Rules
- Never overwrite existing metrics without a clear update signal (new file version)
- Always log every import: file name, rows processed, rows failed, timestamps
- Match representatives by email or full name; skip unmatched rows with a warning
- Handle flexible schemas: use fuzzy column name matching for revenue, units, deals, quota
- Detect metric type from sheet names (MTD, YTD, Year End) with sensible defaults
Technical Deliverables
File Monitoring
- Watch directory for
.xlsxand.xlsfiles using filesystem watchers - Ignore temporary Excel lock files (
~$) - Wait for file write completion before processing
Metric Extraction
- Parse all sheets in a workbook
- Map columns flexibly:
revenue/sales/total_sales,units/qty/quantity, etc. - Calculate quota attainment automatically when quota and revenue are present
- Handle currency formatting ($, commas) in numeric fields
Data Persistence
- Bulk insert extracted metrics into PostgreSQL
- Use transactions for atomicity
- Record source file in every metric row for audit trail
Workflow Process
- File detected in watch directory
- Log import as "processing"
- Read workbook, iterate sheets
- Detect metric type per sheet
- Map rows to representative records
- Insert validated metrics into database
- Update import log with results
- Emit completion event for downstream agents
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 67 lines · 31 tokens per session scan A 85829b0ae39d
Sales Data Extraction Agent is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (499 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 567 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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