---
name: historic-sql-table-digest-c09aaa9de986
description: Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.
callers: [memory_agent]
---

# Historic SQL Table Digest

Use this skill when the WorkUnit raw file is one `tables/<schema>.<name>.json` file from the `historic-sql` adapter.

## Required Workflow

1. Read the WorkUnit notes first.
2. Call `read_raw_file` for the single `tables/<schema>.<name>.json` raw file.
3. Read `manifest.json` only if the table JSON omits the dialect or the WorkUnit notes are unclear.
4. Produce one concise usage narrative for this table from the staged table JSON.
5. Call `emit_historic_sql_evidence` exactly once with `kind: "table_usage"`.
6. Stop after the evidence tool succeeds.

## Identifier Verification Protocol

Before writing a wiki page or SL source 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,
SL source, `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 source, 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:
   - Use `sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"})`.
     If it errors, the identifier is fictional.
   - Wrap the identifier in `[unverified - from <rawPath>]` in the wiki body,
     citing the exact raw path that mentioned it.
   - When recording `emit_unmapped_fallback` with `no_physical_table`, include
     the failing probe error in `clarification`.
5. Never copy `<schema>.<table>` placeholder strings from these instructions
   into output.

## Evidence Shape

Call `emit_historic_sql_evidence` with this shape:

```json
{
  "kind": "table_usage",
  "table": "public.orders",
  "usage": {
    "narrative": "Orders are repeatedly queried for paid/refunded lifecycle analysis and customer-level rollups.",
    "frequencyTier": "high",
    "commonFilters": ["status", "created_at"],
    "commonGroupBys": ["status"],
    "commonJoins": [{ "table": "public.customers", "on": ["customer_id"] }],
    "staleSince": null
  }
}
```

The `usage` object must match `tableUsageOutputSchema`.

## Interpretation Rules

- Treat `columnsByClause.where` as common filters.
- Treat `columnsByClause.groupBy` as common group-bys.
- Treat `observedJoins` as common joins.
- Use `stats.executionsBucket`, `stats.distinctUsersBucket`, and `stats.recencyBucket` to choose `frequencyTier`.
- Use `frequencyTier: "high"` only when executions and distinct users are both broad.
- Use `frequencyTier: "mid"` for repeated team usage that is not broad enough for high.
- Use `frequencyTier: "low"` for low-volume but present usage.
- Use `frequencyTier: "unused"` only when the table input explicitly says the table is stale or has no recent templates.
- Keep `narrative` short and concrete.

## Boundaries

- Do not call wiki_write.
- Do not call sl_write_source.
- Do not call sl_edit_source.
- Do not call context_candidate_write.
- Do not emit more than one table usage evidence object.
- Do not invent columns, joins, or tables that are absent from the staged JSON.


---
Source attribution: Kaelio
Original project: https://github.com/Kaelio/ktx-ai-data-agents-context
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: Kaelio
Original project: https://github.com/Kaelio/ktx-ai-data-agents-context
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Scientist, ML / AI Engineer
Specialties: API, Database
Tools: Historic Sql Adapter
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 004b4f11ee405944f1703940e49acfa617dda6cbd399143326b9d7b96533f15a