Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add BanibrataChatterjee/AwesomeSalesforceSkills --skill analytics-dataflow-developmentgit clone --depth 1 https://github.com/BanibrataChatterjee/AwesomeSalesforceSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/banibratachatterjee/awesomesalesforceskills/analytics-dataflow-development)<a href="https://agentmods.dev/skills/banibratachatterjee/awesomesalesforceskills/analytics-dataflow-development"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/analytics-dataflow-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/banibratachatterjee/awesomesalesforceskills/analytics-dataflow-development"><img src="https://agentmods.dev/badge/skills/banibratachatterjee/awesomesalesforceskills/analytics-dataflow-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.03358 |
| Opus 5 | $0.00043 | $0.01679 |
| Sonnet 5 | $0.00017 | $0.00672 |
| Haiku 4.5 | $0.00009 | $0.00336 |
Grade A, and why
analytics-dataflow-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Dataflow Development
This skill activates when a practitioner needs to author, debug, or optimize a CRM Analytics dataflow — a JSON-defined ETL pipeline that extracts Salesforce data, applies transformations, and registers the result as a CRM Analytics dataset. It covers node composition, run-limit management, failure recovery, and performance tuning.
Before Starting
Gather this context before working on anything in this domain:
- Confirm whether the org already has dataflows consuming the same source objects; duplicate sfdcDigest nodes across multiple dataflows multiply row-count and run-time cost.
- Identify the target dataset row volume. Datasets are capped at 250 million rows; exceeding this causes the sfdcRegister node to fail.
- Count currently scheduled dataflow and recipe runs. The org is limited to 60 combined dataflow and recipe runs per rolling 24-hour window — not a calendar day reset. New dataflows added to a busy schedule may push the org over the limit.
- Determine whether this is a net-new build or a maintenance task. New development should generally use Recipes (the newer UI-driven ETL tool); dataflows remain the right choice for existing pipelines, complex SAQL expressions not supported in Recipes, or when explicit JSON control is required.
Core Concepts
Node Types and Pipeline Topology
CRM Analytics dataflows are JSON objects where each key is a node name and the value defines that node's action and parameters. Nodes fall into four functional groups:
Ingestion nodes — pull source data into the pipeline:
sfdcDigest— extracts records from a Salesforce object using the Analytics connector. Requires specifying theobjectand afieldslist. Supports afilterparameter to push predicate filtering to the source query.Digest— reads from a CSV or external data source already uploaded to the org.Edgemart— references an existing registered CRM Analytics dataset as input. Used for chaining dataflows.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 246 lines · 85 tokens per session scan A 9fc2f26042a3
analytics-dataflow-development is a skill published in the GitHub repository BanibrataChatterjee/AwesomeSalesforceSkills (3 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 85 tokens to every session and 3,358 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
analytics-dataflow-development
Use this skill when building, debugging, or optimizing CRM Analytics dataflows — defining node types (sfdcDigest, Append, Augment, computeExpression, computeRelative, Flatten, dim2mea, sfdcRegister), scheduling runs, handling run failures, and tuning performance. NOT for doing the same transformation in a Data Prep…
analytics-recipe-design
Use this skill when designing or building CRM Analytics Data Prep recipes — including node selection, join patterns, bucket field configuration, formula expressions, and scheduling. Triggers: 'build a recipe', 'join datasets in analytics', 'bucket a measure field', 'schedule a recipe', 'data prep transformation'. NOT…
ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
cocoindex
This skill should be used when building data processing pipelines with CocoIndex, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change…
docetl
Build and run LLM-powered data processing pipelines with DocETL. Use when users say "docetl", want to analyze unstructured data, process documents, extract information, or run ETL tasks on text. Helps with data collection, pipeline creation, execution, and optimization.
cml-admin
CML administration — user/group management, system info, licensing, resource monitoring. Use when creating CML users, checking license status, monitoring CML server resources, or auditing lab usage across teams.