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 alivirgo/Major-AI-Skills --skill csv-over-json-tablesgit clone --depth 1 https://github.com/alivirgo/Major-AI-SkillsWrote 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/alivirgo/major-ai-skills/csv-over-json-tables)<a href="https://agentmods.dev/skills/alivirgo/major-ai-skills/csv-over-json-tables"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/csv-over-json-tables/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/alivirgo/major-ai-skills/csv-over-json-tables"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/csv-over-json-tables.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 | $0.00023 | $0.01119 |
| Opus 5.5 | $0.00009 | $0.00448 |
| Sonnet 5.5 | $0.00005 | $0.00224 |
| Haiku 4.5 | $0.00002 | $0.00112 |
Grade A, and why
csv-over-json-tables 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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tabular Serialization Protocol (CSV / TSV over JSON Arrays)
Overview
When an LLM extracts or analyzes tabular datasets (e.g., customer transaction lists, database query results, or server metric logs), defaulting to an array of JSON objects forces the model to repeat every column key on every single row ("first_name": "...", "transaction_amount": "...", "status": "...").
In a 100-row dataset, repeating JSON keys burns thousands of redundant tokens purely on structural quotation marks, colons, and braces.
The Tabular Serialization Protocol replaces JSON object arrays with Comma-Separated Values (CSV) or Tab-Separated Values (TSV) - defining the header keys once at row 1 and streaming raw comma-delimited data rows, cutting token usage by 65% to 75%.
JSON Object Array vs. CSV Data Stream
┌─────────────────────────────────────────────────────────────┐
│ Tabular Payload Comparison │
│ │
│ JSON Object Array (145 Tokens for 2 Rows): │
│ [ │
│ {"user_id": 101, "email": "[email protected]", "tier": "pro"}│
│ {"user_id": 102, "email": "[email protected]", "tier": "free"} │
│ ] │
│ ↳ Keys `user_id`, `email`, `tier` repeated on every row! │
│ │
│ CSV Data Stream (34 Tokens - 76.5% Reduction!): │
│ user_id,email,tier │
│ 101,[email protected],pro │
│ 102,[email protected],free │
│ ↳ Keys defined ONCE. Zero quotation/brace syntax waste. │
└─────────────────────────────────────────────────────────────┘
The Master CSV Extraction Prompt Template
When querying an LLM to extract or output structured rows:
Extract the customer records from the text below:
<source_data>
[PASTE UNSTRUCTURED TEXT]
</source_data>
Output Constraints:
- Format as raw **CSV (Comma-Separated Values)**.
- Line 1 MUST be the exact header row: `user_id,name,email,plan_tier,monthly_spend`
- Do NOT output JSON.
- Output ONLY the CSV block; zero introductory or concluding commentary.
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 Changed · -13 tokens per session 82af25c0085f
- 1mo ago First seen · 115 lines · 36 tokens per session scan A 37a54e80b121
csv-over-json-tables is a skill published in the GitHub repository alivirgo/Major-AI-Skills (2 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 1,119 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 2026-08-31.
Other skills, from other repositories
accounting-audit-system-builder
Routes an accounting or audit request to the right module skill, from software selection through monthly closing, asking only what is missing. Use for books of accounts or audit files.
snip
You are an expert at writing declarative YAML filters for snip, a CLI proxy that reduces LLM token consumption by filtering shell output.
azure-pipelines
Expert knowledge for Azure Pipelines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring YAML pipelines, agents, service connections, Key Vault secrets, or…
azure-kubernetes-service
Expert knowledge for Azure Kubernetes Service (AKS) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing AKS with Entra/RBAC, configuring Fleet, GPUs or KEDA…
azure-virtual-machines
Expert knowledge for Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing VM/disk/GPU SKUs, configuring NVMe/Ultra disks, using Trusted…
azure-arc
Expert knowledge for Azure Arc development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Arc-enabled Kubernetes, Arc SQL MI, resource bridge, Arc Agentic Retrieval, or…