analytics
Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of",…
Synthesize durable KTX wiki pages from staged Google Drive document pulls. Load when a WorkUnit contains Google Doc raw files from `docs/**`.
$ npx -y skills add Kaelio/ktx --skill gdrive_synthesize --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/gdrive_synthesizeContext preview
The summary Claude sees to decide when to auto-load this skill.
Synthesize durable KTX wiki pages from staged Google Drive document pulls. Load when a WorkUnit contains Google Doc raw files from `docs/**`.
name: gdrive_synthesize description: Synthesize durable KTX wiki pages from staged Google Drive document pulls. Load when a WorkUnit contains Google Doc raw files from `docs/**`. callers: [memory_agent]
Use this skill when a WorkUnit contains staged Google Drive content from `docs/**`.
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.
1. Read the WorkUnit notes and `rawFiles` list. Document content lives in `page.md`; `metadata.json` holds title, path, url, modified time, and Drive folder context. 2. For each assigned doc, call `read_raw_file`, or `read_raw_span` for oversized docs when the notes specify a span. 3. Search `wiki_search` for existing pages that overlap the WorkUnit topics. Prefer updating an existing page over creating a duplicate. 4. Use `context_evidence_search`, `context_evidence_read`, and `context_evidence_neighbors` when indexed document chunks would help reconcile related facts. Pass `chunkId` and `documentId` values verbatim as returned by the evidence tools. 5. Write durable business knowledge with `wiki_write`. Aim for a small number of high-quality pages per doc. Include `rawPaths` with the exact Google Drive raw files that support each page. 6. If a doc references warehouse, dbt, Looker, Metabase, or MetricFlow objects, you may verify them with `discover_data`, `entity_details`, `sql_execution`, `sl_discover`, or `sl_read_source`, but Google Drive docs are knowledge-only in v1. Do not create semantic-layer sources under the `gdrive` connection. 7. For every deleted raw path in the Eviction Set, call `eviction_list`, decide retention, then `emit_eviction_decision`. Do this even when no wiki write is needed.
Capture durable, reusable company knowledge:
Skip noisy or transient content:
Prefer fewer, stronger entries. Every wiki entry must cite at least one Google Doc using its title or path and last modified date when available. When evidence conflicts, write a conflict note inside the wiki page rather than choosing silently.
If one doc covers several related ideas, synthesize the shared durable rules instead of writing one thin page per paragraph. For oversized spans, read only the assigned span unless the WorkUnit explicitly asks for neighboring context.
Search existing wiki pages for the same `tables:` or `sl_refs:` frontmatter and for source-of-truth aliases before creating a new page. If an existing page already documents the same warehouse object or business concept, update it instead of creating a differently named duplicate.
## Agentic Harness - The harness provides the operational framework that turns an agent prototype into a production system. - Source: Google Doc - Herness, last modified 2026-05-24. - Conflict note: An older internal note uses a narrower definition focused only on tool wiring; treat the current Google Doc as the durable operating definition unless replaced explicitly.
Before writing a wiki page on any topic:
1. `discover_data({query: "<topic>"})` - see what wikis, SL sources, and raw tables already exist. Prefer updating existing pages over creating new ones.
Before emitting any `schema.table` or `schema.table.column` into a wiki body, `tables:` frontmatter, `sl_refs`, or `emit_unmapped_fallback`:
2. `entity_details({connectionId, targets: [{display: "<identifier>"}]})` - confirm the identifier resolves; inspect native types, FK/PK, and sampleValues. 3. For literal values from the doc, such as status codes or plan tiers, check whether they appear in `entity_details` sampleValues for the relevant column. If sampleValues is short or the sample may have missed real values, run a `sql_execution` probe with the same warehouse connection id: `sql_execution({connectionId, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"})`. 4. If the candidate identifier still does not resolve, do one of:
If it errors, the identifier is fictional.
citing the exact raw path that mentioned it.
the failing probe error in `clarification`. 5. Never copy `<schema>.<table>` placeholder strings from these instructions into output.
Allowed: `read_raw_file`, `read_raw_span`, `wiki_search`, `wiki_read`, `wiki_write`, `discover_data`, `entity_details`, `sql_execution`, `sl_discover`, `sl_read_source`, `context_evidence_search`, `context_evidence_read
ktx is an executable context layer for data and analytics agents 🐙 Allow Claude Code, Codex, or other AI agents to query analytical databases accurately and with full context of your company
Repo: Kaelio/ktx
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