Kaelio/ktx is a context layer that helps AI agents query analytical databases using company knowledge, approved metrics, table metadata, and relationships between columns. Data teams use it to make warehouse queries more accurate and consistent with their organization's definitions. Its catalogue add-ons teach agents how to use ktx and its data-querying interfaces.
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 Kaelio/ktx --skill gdrive_synthesizegit clone --depth 1 https://github.com/Kaelio/ktxWrote 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/kaelio/ktx/gdrive_synthesize)<a href="https://agentmods.dev/skills/kaelio/ktx/gdrive_synthesize"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/gdrive_synthesize/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/kaelio/ktx/gdrive_synthesize"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/gdrive_synthesize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.01360 |
| Opus 5.5 | $0.00014 | $0.00544 |
| Sonnet 5.5 | $0.00007 | $0.00272 |
| Haiku 4.5 | $0.00003 | $0.00136 |
Grade A, and why
gdrive_synthesize 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Drive Doc Synthesis
Use this skill when a WorkUnit contains staged Google Drive content from docs/**.
Role
Each WorkUnit is one Google Doc plus its metadata. Read the assigned raw files, then write a small set of durable wiki entries that capture reusable organizational knowledge. Write final memory directly; do not write candidates.
Required Workflow
- Read the WorkUnit notes and
rawFileslist. Document content lives inpage.md;metadata.jsonholds title, path, url, modified time, and Drive folder context. - For each assigned doc, call
read_raw_file, orread_raw_spanfor oversized docs when the notes specify a span. - Search
wiki_searchfor existing pages that overlap the WorkUnit topics. Prefer updating an existing page over creating a duplicate. - Use
context_evidence_search,context_evidence_read, andcontext_evidence_neighborswhen indexed document chunks would help reconcile related facts. PasschunkIdanddocumentIdvalues verbatim as returned by the evidence tools. - Write durable business knowledge with
wiki_write. Aim for a small number of high-quality pages per doc. IncluderawPathswith the exact Google Drive raw files that support each page. - If a doc references warehouse, dbt, Looker, Metabase, or MetricFlow objects, you may verify them with
discover_data,entity_details,sql_execution,sl_discover, orsl_read_source, but Google Drive docs are knowledge-only in v1. Do not create semantic-layer sources under thegdriveconnection. - For every deleted raw path in the Eviction Set, call
eviction_list, decide retention, thenemit_eviction_decision. Do this even when no wiki write is needed.
What To Capture
Capture durable, reusable company knowledge:
- policies, workflows, process rules, ownership conventions, and operating procedures
- product definitions, business terminology, and organizational guidance
- source-of-truth statements, caveats, conflict notes, and supersession guidance
- cross-system aliases that connect doc terminology to warehouse, dbt, Looker, Metabase, or MetricFlow names
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 First seen · 98 lines · 34 tokens per session scan A 31dedc5eda0e
gdrive_synthesize is a skill published in the GitHub repository Kaelio/ktx (1,623 stars, last pushed 5d ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,360 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 2026-08-30.
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