Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add DDS-Solutions/AI-TadPole-OS --skill smb-data-ingestiongit clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote 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/skills/dds-solutions/ai-tadpole-os/smb-data-ingestion)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/smb-data-ingestion"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/smb-data-ingestion/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/skills/dds-solutions/ai-tadpole-os/smb-data-ingestion"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/smb-data-ingestion.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 | $0.00039 | $0.01217 |
| Opus 5.5 | $0.00016 | $0.00487 |
| Sonnet 5.5 | $0.00008 | $0.00243 |
| Haiku 4.5 | $0.00004 | $0.00122 |
Grade A, and why
smb-data-ingestion 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / smb-data-ingestion
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Silent data corruption during type coercion, date parsing failures, or unredacted PII storage.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
SMB Data Ingestion & ETL Protocol
Purpose: Convert un-normalized, messy SMB business data exports (CSV, Excel, accounting dumps) into clean, validated JSON schemas and SQLite state with zero loss of precision.
Workflow Bindings: Integrated into/createand/migrate.
🎯 Progressive Disclosure & L3 Reference Index
Read the core Sovereign ETL rules below; consult deep L3 Reference Guides on demand:
| L3 Reference Guide | Purpose | Trigger / When to Load |
|---|---|---|
references/smb_data_mapping_and_normalization.md |
Date/currency coercion rules, fuzzy header mapping dictionaries, and transaction batching | Mapping raw CSV columns & normalizing types |
scripts/smb_ingest_validator.py |
Automated currency string cleaner, ISO-8601 date normalizer, and record validator | Scrubbing and parsing raw legacy files |
🔄 1. The 4-Phase Ingestion Pipeline
[1. DETECT] ───> [2. MAP HEADERS] ───> [3. SANITIZE & PII] ───> [4. LOAD]
Identify format Fuzzy match to Normalize dates/cur, Parameterized
& file encoding domain glossary scrub PII from text SQLite batch
- Format Identification: Detect file format (CSV, XLSX, JSON, legacy SQL dump) and verify encoding (UTF-8 with fallback to Windows-1252 / latin-1).
- Schema Ingestion & Mapping: Map source column headers to
CONTEXT.md/docs/GLOSSARY.mddomain entities using fuzzy matching. - Data Sanitization:
- Strip leading/trailing whitespace.
- Convert currency strings (
"$1,429.50") to numeric decimals viasmb_ingest_validator.py. - Normalize dates (
"10/24/2026") to ISO 8601 ("2026-10-24T00:00:00Z"). - Run PII scrubbing on all free-text notes fields prior to storage.
- Target Loading: Load records into SQLite or LanceDB using parameterized batch queries inside a single explicit transaction.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago Changed · +56 lines 2ee200e3a284
- 1mo ago First seen · 31 lines · 39 tokens per session scan A 397c3d42590d
smb-data-ingestion is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed 6d ago), licensed MIT. It adds 39 tokens to every session and 1,217 once invoked, about $0.0002 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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