
This handles the write path for ktx's institutional memory: metric definitions, filter conventions, aliases, and cross-references to semantic layer sources. It's loaded exclusively by the memory agent after each turn, while the research agent reads wiki pages but never writes them. The scope routing is automatic (personal vs. global), but the real judgment call is what to capture at all. Capture durable rules like "revenue means booked revenue after refunds" or "exclude test accounts from ARR." Skip one-off requests, formatting preferences, and especially query results or time-bounded snapshots, which go stale the moment new data lands. The identifier verification protocol is thorough, requiring entity_details checks before writing any schema.table references to avoid fictional identifiers making it into the knowledge base.
$ npx -y skills add kaelio/ktx --skill wiki_capture --agent claude-codeInstalls into .claude/skills of the current project.
The knowledge base stores durable, reusable business knowledge for an analytics assistant. Each page is a self-contained rule, definition, or convention that answers "how should this concept be handled in this organization?" - written once and reused across chats.
Scope selection is handled by the runtime:
sourceType: 'external_ingest'), writes go to the global scope.The wiki_write tool picks the right scope based on the session. Capture logic does not need to choose - focus on whether the content is worth capturing at all.
Capture when the user or the ingested document expresses:
Do NOT capture:
sl_refs) and let future query tools pull live data - the wiki captures the rule (definition, exclusion, segmentation), the SL source captures the measure, and query execution captures the current values.If nothing is worth capturing, respond without calling any tool.
discover_data first when a page relates to data or SL concepts - find
existing wiki pages, SL sources, and raw warehouse schema together.wiki_search with the topic - find related wiki pages to populate refs.sl_discover with the concept - if the page defines a metric (revenue, churn, retention, LTV, ARR, MRR, CAC, attribution, etc.), find matching SL sources or measures to populate sl_refs. If no matches, pass sl_refs: [] so future readers know you checked.wiki_read it first. Use the returned structured.content or markdown body as the exact stored text for targeted replacements; current tags, refs, and sl_refs are returned in structured metadata.wiki_write to create or update. Prefer merging into an existing page over creating a new one.wiki_remove only when a page is truly obsolete - not to replace stale content (update it instead).For bundle/external ingest, include rawPaths on every wiki_write/wiki_remove call with only the raw files that directly support that wiki action. This keeps ingest provenance tied to the actual source file, not every file in the WorkUnit.
Before writing a wiki page or SL source on any topic:
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,
SL source, tables: frontmatter, sl_refs, or emit_unmapped_fallback:
entity_details({connectionId, targets: [{display: "<identifier>"}]}) -
confirm the identifier resolves; inspect native types, FK/PK, and
sampleValues.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"}).sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"}).
If it errors, the identifier is fictional.[unverified - from <rawPath>] in the wiki body,
citing the exact raw path that mentioned it.emit_unmapped_fallback with no_physical_table, include
the failing probe error in clarification.<schema>.<table> placeholder strings from these instructions
into output.leads-source-filter, revenue-definition, churn-calculation. No namespacing, no prefixes.## [Topic Title]
- Rule or preference statement
- Another rule if applicable
Prefer fewer, richer pages over many thin ones. Each page covers one coherent topic thoroughly. If the new information relates to an existing page, update that page instead of fragmenting the knowledge.
The wiki_write tool accepts three array fields that go into the page frontmatter:
tags: 1–3 short lowercase topic tags (["finance"], ["data-quality"]). Call wiki_list_tags first to reuse existing tags for consistency.refs: keys of related wiki pages. Add when the new page materially depends on concepts from another (e.g., a churn definition that uses the paid-orders filter from a revenue definition). Don't add refs just because pages share a topic area.sl_refs: names of SL sources or measures the page relates to. Format: "source_name" or "source_name.measure_name". Discover via sl_discover → inspect with sl_read_source → include the confirmed matches.Wiki page keys must be flat slugs. Use large-contract-requesters, not
historic-sql/large-contract-requesters. Use tags, source, and content
headings for grouping.
All three fields use REPLACE semantics on update:
[] → field is cleared.[values] → replaces existing with exactly those values (no merging).A project may have several databases whose schemas reuse the same concept names
(two warehouses each with orders, customers, …). The connections
frontmatter field keeps database-specific pages from polluting searches about
other databases.
wiki_write tool accepts a connections field (list of connection ids,
same REPLACE semantics as tags). Absent or empty ⇒ the page is unscoped
and applies to every connection.connectionId: warehouse in the SL Sources header or the
<context> block), set connections: [<that id>] on pages whose content is
specific to that database ("in this warehouse user_id is the device id,
not the account id"). Pair this with a connection-distinctive key so two
databases' same-concept pages can coexist: orders_sales_db, not orders.connections empty for clearly org-wide knowledge ("fiscal year
starts in February") so it stays visible everywhere. Do not scope a page to a
connection just because the turn happened to be connection-scoped.connections does not namespace
them. A connection-scoped write whose key already belongs to a page scoped to
a different connection is rejected to prevent silently overwriting it — pick
a connection-distinctive key instead.Two modes:
content to rewrite the whole page. Use when the page structure needs to change.replacements: [{ oldText, newText }] to apply exact-string replacements. Use for small updates; preserves the rest of the page.When editing, read the page first so the edit matches exact whitespace and indentation.
Organization (GLOBAL) pages are read-only from a user's personal-scope session. To override a global rule for a single user, write a personal page with the same key. At read time the USER page wins.
User says: "Going forward, the official refund rate is total refunded amount divided by total gross transaction amount."
wiki_list_tags()
→ existing tags include "finance"
wiki_search({ query: "refund revenue paid orders" })
→ returns `revenue-definition` (related - defines paid-orders filter)
sl_discover({ query: "refund rate" })
→ returns fct_orders (score 0.08), fct_gaap_revenue (0.06)
sl_read_source({ connectionId: "warehouse", sourceName: "fct_orders" })
→ confirms amount_refunded_dollars and transaction_amount_dollars exist
wiki_write({
key: "refund-rate-definition",
summary: "Refund rate = refunded amount / gross transaction amount",
content: "## Refund Rate\n- Definition: sum(amount_refunded_dollars) / sum(transaction_amount_dollars)\n- Source of truth: fct_orders\n- Related: see revenue-definition for paid-orders filter.",
tags: ["finance"],
refs: ["revenue-definition"],
sl_refs: ["fct_orders.refund_rate_pct", "fct_orders"]
})
Search-then-write order matters. Cross-references are part of the page's identity, not an afterthought.
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