PackagePublished package

@pasar6987/pipeliner

Name: Semantic model package @pasar6987/pipeliner
Creator: pasar6987

pipeliner API schema: 24 streams, 737 fields.

OSISchema 0.1.1Domain crm

Published package · Latest published 1.0.0 Mar 4, 2026 · 24 datasets / 94 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.0Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

24 datasets94 measures · 643 dimensions

Semantic Graph

Relationship counts appear after the graph loads.

Loading graph

Package graph is loading

Package relationships will appear when the summary is ready.

Reference context

Secondary package facts stay compact

Updated: Mar 11, 2026
Visibility: Public hub listing
License: MIT
Created: Mar 4, 2026

Reference facts

Secondary package facts after the usage path is clear

Licensing, categorization, ownership, and linked metadata stay below the runnable path so the page reads in the right order.

Format

OSI

Upstream

Not linked

Repository

Not linked

Domain

crm

Source

Not set

Owner

@pasar6987

Organization

Independent

License

MIT

Visibility

Public hub listing

Publisher

@pasar6987Published Mar 4, 2026

Latest published version

1.0.0Published Mar 4, 2026

Use this package in code and AI

After structure review, move straight into typed reads with load() or compact LLM context with to_prompt().

Python SDK

Python example for @pasar6987/pipeliner

This example uses the current package ref and, when preview data is available, fills in real dataset names from the published summary.

import rawctx

model = rawctx.load("@pasar6987/pipeliner")
prompt = rawctx.to_prompt(
    "@pasar6987/pipeliner",
    datasets=["accounts", "activities"],
    max_tokens=2000,
)

print(model.datasets)        # ["accounts", "activities", "clients"]
print(model.measures)        # [Measure(name="accounts.account_class", ...), Measure(name="accounts.health.score", ...), Measure(name="accounts.health_status", ...)]
print(model.dimensions)      # [Dimension(name="account_type", ...), Dimension(name="account_type_id", ...), Dimension(name="address", ...)]
print(model.relationships)   # [Relationship(name='...', ...)]
print(prompt)

See Python docs

README

Package narrative and examples

Use documentation after the package clears provenance, ownership, and runnable-path checks.

@pasar6987/pipeliner

pipeliner API schema — 24 streams, 737 fields.

Overview

	Count
Streams (Datasets)	24
Dimensions	643
Measures	94
Relationships	7

Streams

accounts — accounts data from pipeliner
activities — activities data from pipeliner
clients — clients data from pipeliner
contacts — contacts data from pipeliner
entities — entities data from pipeliner
data — data data from pipeliner
cloud_objects — cloud_objects data from pipeliner
fields — fields data from pipeliner
forecasts — forecasts data from pipeliner
form_views — form_views data from pipeliner
entity_scorings — entity_scorings data from pipeliner
leads — leads data from pipeliner
lead_oppties — lead_oppties data from pipeliner
memos — memos data from pipeliner
notes — notes data from pipeliner
entity_fitnesses — entity_fitnesses data from pipeliner
pipelines — pipelines data from pipeliner
products — products data from pipeliner
oppty_product_relations — oppty_product_relations data from pipeliner
profiles — profiles data from pipeliner
quotes — quotes data from pipeliner
reports — reports data from pipeliner
steps — steps data from pipeliner
tags — tags data from pipeliner

Install

rawctx snapshot-download @pasar6987/pipeliner

Structure review

Inspect package structure after the usage path is clear

Use the structural review when you need the package footprint, field counts, and model paths before a deeper explorer pass.

Models1

datasets24

measures94

dimensions643

relationships7

AI context1

models/pipeliner.osi.yamlAI context included

24 datasets94 measures643 dimensions7 relationships