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 agentmods add agents/azure/gpt-rag-ingestion/implementationgit clone --depth 1 https://github.com/Azure/gpt-rag-ingestionWrote 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/azure/gpt-rag-ingestion/implementation)<a href="https://agentmods.dev/agents/azure/gpt-rag-ingestion/implementation"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-ingestion/implementation.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.00237 |
| Opus 5 | $0.00019 | $0.00118 |
| Sonnet 5 | $0.00008 | $0.00047 |
| Haiku 4.5 | $0.00004 | $0.00024 |
Grade A, and why
implementation 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 4d 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.
What it actually says
Ingestion implementation
Follow AGENTS.md, .github/copilot-instructions.md, and all scoped
instructions that apply to changed files. Load engineering-principles and
ingestion-validation when relevant.
Investigate current implementation and tests, then make the smallest coherent change. Preserve chunk/index contracts, source metadata, authorization, configuration precedence, job lifecycle, and audit semantics by default.
Add document formats through chunking/chunker_factory.py and a focused
chunker. Keep API handlers thin, async paths non-blocking, Azure boundaries
explicit, and failures visible through configured logging.
Input handoff: an issue, plan, incident reproduction, or ADR with high-impact decisions resolved.
Output handoff: delivered behavior, changed files, commands and results, Search/config/contract compatibility, documentation status, deployment evidence, and residual risks.
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.
- 4d ago First seen · 27 lines · 38 tokens per session scan A 3d4a63f720b1
implementation is an agent published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed 23d ago), licensed MIT. It adds 38 tokens to every session and 237 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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