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 abrahamADSK/fpt-mcp --skill fpt-querygit clone --depth 1 https://github.com/abrahamADSK/fpt-mcpWrote 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/abrahamadsk/fpt-mcp/fpt-query)<a href="https://agentmods.dev/skills/abrahamadsk/fpt-mcp/fpt-query"><img src="https://agentmods.dev/badge/skills/abrahamadsk/fpt-mcp/fpt-query/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/abrahamadsk/fpt-mcp/fpt-query"><img src="https://agentmods.dev/badge/skills/abrahamadsk/fpt-mcp/fpt-query.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 | $0.00108 | $0.00979 |
| Opus 5.5 | $0.00043 | $0.00392 |
| Sonnet 5.5 | $0.00022 | $0.00196 |
| Haiku 4.5 | $0.00011 | $0.00098 |
Grade A, and why
fpt-query 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 17d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Querying and reporting on ShotGrid
Applies to any FPT site and entity type, not one project's pipeline. This is the read side: it cannot damage the database, so the failure modes are wrong answers and exhausted context, not lost work.
For filter operators, method signatures and pagination syntax use
search_sg_docs — that is the reference layer and it covers all three APIs.
This skill is which question deserves which tool, and what a plausible answer
hides.
The failure that looks like an answer
A single-entity check is not a state check
Asking "is ShotGrid clean?" and querying only Version returned clean in-vivo
while PublishedFile rows from the same operation were still there. The answer
was confidently wrong.
Enumerate every entity type the operation writes before declaring state. A
publish touches PublishedFile, usually Version, sometimes Attachment and
the Task status. A cleanup check that covers one of them is not a check.
Same rule for "did it work?" — verify the entity you created, not a neighbour that happens to exist.
Aggregate on the server
fpt_reporting(action="summarize") does count / sum / avg / min / max with
grouping, server-side. Reach for it whenever the question is a number or a
breakdown:
- "how many shots per status" →
summarize+grouping, one small payload. sg_findthen counting rows in your head → pulls every row into context to throw them away. For a few hundred shots this is the difference between a 200-token answer and a 40,000-token one.
Use sg_find when the user needs the rows themselves, not a statistic.
Field discipline
Every field you name is paid once per row. Name only the fields you will
actually read or show; never pass a field list harvested from sg_schema
wholesale. Set a real limit — an exploratory query is 5–20 rows, not the
default.
If you do not know the result size, summarize the count first, then decide
whether to fetch rows at all.
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.
- 17d ago Changed d7ce57df5f23
- 20d ago First seen · 90 lines · 108 tokens per session scan A a761be78053a
fpt-query is a skill published in the GitHub repository abrahamADSK/fpt-mcp (2 stars, last pushed 17d ago), licensed MIT. It adds 108 tokens to every session and 979 once invoked, about $0.0004 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 2026-09-29.
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