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 instructions/azure/gpt-rag-ingestion/pythongit clone --depth 1 https://github.com/Azure/gpt-rag-ingestionWhat 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 | $0.00203 | $0.00203 |
| Opus 5 | $0.00102 | $0.00102 |
| Sonnet 5 | $0.00041 | $0.00041 |
| Haiku 4.5 | $0.00020 | $0.00020 |
Grade A, and why
gpt-rag-ingestion python.instructions.md 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 2d 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
Python 3.12
- Keep modules focused and follow local naming and import conventions.
- Use explicit type hints and typed boundary records where practical.
- Preserve async behavior through API, job, and Azure SDK paths. Do not call blocking network or file I/O on the event loop.
- Bound concurrency, retries, batches, memory, and external-call duration.
- Catch expected exceptions where context and recovery are known; do not add broad catches or success-shaped fallbacks.
- Use configured logging and telemetry, never
print. - Keep pure transformations testable without Azure credentials or network access.
- Run focused
pytesttests, then the broader suite according to risk. - Load
engineering-principlesfor meaningful design, security, data, integration, or operational changes.
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.
- 2d ago First seen · 20 lines · 203 tokens per session scan A 898ce6a0d7af
gpt-rag-ingestion python.instructions.md is an instructions file published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed 21d ago), licensed MIT. It adds 203 tokens to every session, about $0.0010 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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