Atlas / Skills / kaelio / Metricflow Ingest

Metricflow IngestSAFE

skills/kaelio/metricflow-ingest

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

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
Apache-2.0
Stars
1,616
01

Overview

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

Read from source at commit 6283cdd9b55cOBSERVED · 2026-10-12
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: metricflow_ingest
description: Map a MetricFlow semantic_model or metric into ktx semantic layer sources. Covers the MetricFlow to ktx primitive table, `extends:` inheritance flattening, metric-type handling (simple / derived / ratio / cumulative / conversion), `model: ref('x')` resolution, and four worked examples. Load when the turn contains `.yml`/`.yaml` files with top-level `semantic_models:` or `metrics:`.
callers: [memory_agent]
---

# MetricFlow to ktx Semantic Layer

A MetricFlow `semantic_model` maps to an SL source; MetricFlow `measures` map to ktx measures; MetricFlow `entities` map to ktx `joins`; MetricFlow `metrics` (top-level) map to ktx measures OR to cross-model derived measures. Files in one WorkUnit are ALWAYS part of the same logical entity (a connected component, possibly spanning `extends:` + cross-model metric refs). Flatten inheritance and cross-file references at write time.

## Mapping table

| MetricFlow | ktx form | Notes |
|---|---|---|
| `semantic_model: X { model: ref('t') }` with measures + dimensions | **Overlay** named `X` with `measures`, computed-only `columns`, `column_overrides`, `joins` | The `model:` ref resolves to a manifest table. |
| `semantic_model: X { model: source('s','t') }` | **Overlay** named `X` over table `t`. | Same shape; `source()` still resolves to a physical table. |
| `semantic_model: X { model: <literal> }` with no manifest entry | **Standalone** with explicit `sql:`, `grain:`, `columns:` | Happens when the dbt manifest isn't available. |
| `semantic_model: Y { extends: X }` | **Merge** Y's measures/dimensions/entities into X's overlay, or write a single overlay named for the most-derived child (Y) containing both X's and Y's primitives | Do not emit a second overlay for X - flatten. |
| `measures: [{ name, agg, expr }]` | `measures: [{ name, expr: "<agg>(<expr>)" }]` | Aggregation inlined. `agg: count_distinct` → `count(distinct ...)`. |
| `entities: [{ name, type: primary }]` | `grain: [<entity_name-or-expr>]` on the overlay/standalone | Primary/unique entities drive grain. |
| `entities: [{ name, type: foreign }]` | `joins:` entry joining to the primary-entity's semantic_model | Only when a matching primary is discoverable. |
| `metrics: [{ type: simple, type_params: { measure: X } }]` | If the base measure is labeled/described by the metric: in-place edit to the existing measure. Otherwise leave as-is. | Same-name metrics can absorb metadata. |
| `metrics: [{ type: simple, filter: <jinja> }]` | **New measure** on the same source, with the filter translated to SQL and attached via `filter:` | Translate Jinja `{{ Dimension('x__y') }}` to the column name `y`. |
| `metrics: [{ type: derived, type_params: { expr, metrics } }]` | **Derived measure** on whichever source owns the referenced measures, with `expr:` referencing measure names | If the metric spans models, still write it once on the source owning the "primary" measure (the one the agent judges most central). Mention the cross-model chain in the description. |
| `metrics: [{ type: ratio, type_params: { numerator, denominator } }]` | Same as derived; `expr: "numerator / NULLIF(denominator, 0)"` if no explicit expr | Safe-division by default. |
| `metrics: [{ type: cumulative, type_params: { window, grain_to_date } }]` | **Standalone** source with a window-function SQL; reference the resulting column as a normal measure | ktx SL has no first-class cumulative primitive (spec Non-goals). |
| `metrics: [{ type: conversion }]` | **Flag for human** - do NOT write. Emit a wiki note describing the intended semantics. | No ktx equivalent in v1. |
| Metric not mappable | Wiki page `<metric_name>-definition.md` with the full YAML body quoted | Capture the intent even if we can't emit SL. |

Type map: MetricFlow `time` to ktx `time`; `categorical` to `string`; `number` to `number`; `boolean` to `boolean`. Follow `expr` over `name` when both differ - `expr` is the physical column.

Verify each MetricFlow model source table with entity_details before producing
the corresponding sl_write_source.

## 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.

## Flattening `extends:`

Within one WorkUnit, multiple semantic_models linked by `extends:` are guaranteed to be present (the chunker groups them). Resolve inheritance **before** writing:

1. Start with the most-derived child (the one that no other semantic_model extends).
2. Walk the `extends:` chain upward, accumulating measures, dimensions, entities.
3. Write ONE overlay/standalone, named for the most-derived chil
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (3)

LOWInventory / provenance · inv.symlink · CWE-1104
CLAUDE.md
CLAUDE.md
Why it matters. link not followed
LOWInventory / provenance · inv.symlink · CWE-1104
GEMINI.md
GEMINI.md
Why it matters. link not followed
LOWInventory / provenance · inv.symlink · CWE-1104
python/ktx-sl/CLAUDE.md
python/ktx-sl/CLAUDE.md
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-10-12 · audit v0.4.1 · source sha 6283cdd9b55cfull audit observations/trust-audit/skill/kaelio__metricflow_ingest.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-126283cdd9b55cSAFEB89first audit
05

Questions

What does the Metricflow Ingest skill do?

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

Is Metricflow Ingest safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Metricflow Ingest access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (6283cdd9b55c), read on 2026-10-12. The repository is watched, and a new audit runs when it changes — this is the first audit.

Advertisement