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/nutdnuy/sec-opendata-th/gemini-mdgit clone --depth 1 https://github.com/nutdnuy/sec-opendata-thWrote 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/instructions/nutdnuy/sec-opendata-th/gemini-md)<a href="https://agentmods.dev/instructions/nutdnuy/sec-opendata-th/gemini-md"><img src="https://agentmods.dev/badge/instructions/nutdnuy/sec-opendata-th/gemini-md.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 5.1 | $0.00317 | $0.00317 |
| Opus 5 | $0.00159 | $0.00159 |
| Sonnet 5 | $0.00063 | $0.00063 |
| Haiku 4.5 | $0.00032 | $0.00032 |
Grade A, and why
sec-opendata-th GEMINI.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 5d 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
Gemini / Antigravity Instructions
Follow AGENTS.md. Treat this repository as the source of truth for SEC Open
Data Thailand access across all AI tools.
Use the Python CLI for data pulls:
.venv/bin/python -m secopendata categories
.venv/bin/python -m secopendata amc-search "กสิกร"
.venv/bin/python -m secopendata fund-search "K-CHANGE" --status Registered
.venv/bin/python -m secopendata request --method GET --path /v1/<category>/<path>
.venv/bin/python -m secopendata request --method GET --path /v2/fund/factsheet/performance --cursor-paginate
.venv/bin/python -m secopendata request --method POST --path /v1/<category>/<path> --json '{"Name":""}'
Use skills/sec-open-api/SKILL.md as the reusable operating guide. Preserve
generic /v1 endpoint coverage for digital assets, funds, Licence Check, One
Report, provident funds, debt, equity, ESG, operators/professionals, investors,
and future SEC portal categories. Use --cursor-paginate for v2 endpoints that
return next_cursor and items.
Resolve fund and AMC names from the bundled offline catalog before asking for a subscription key. Use the live API when the user needs fresher data or details that are not present in the compact catalog.
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.
- 5d ago First seen · 26 lines · 317 tokens per session scan A dfe001a2be64
sec-opendata-th GEMINI.md is an instructions file published in the GitHub repository nutdnuy/sec-opendata-th (8 stars, last pushed 1mo ago), licensed MIT. It adds 317 tokens to every session, about $0.0016 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 2026-08-31.
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