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 ibm-self-serve-assets/building-blocks --skill data-quality-rulesgit clone --depth 1 https://github.com/ibm-self-serve-assets/building-blocksWrote 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/ibm-self-serve-assets/building-blocks/data-quality-rules)<a href="https://agentmods.dev/skills/ibm-self-serve-assets/building-blocks/data-quality-rules"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/data-quality-rules/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/ibm-self-serve-assets/building-blocks/data-quality-rules"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/data-quality-rules.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.01304 |
| Opus 5 | $0.00042 | $0.00652 |
| Sonnet 5 | $0.00017 | $0.00261 |
| Haiku 4.5 | $0.00008 | $0.00130 |
Grade A, and why
data-quality-rules scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post(self._IAM_URL, How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IBM watsonx.data Intelligence Data Quality Builder
Purpose
This skill defines the complete workflow for implementing IBM watsonx.data Intelligence data quality capabilities and generating production-ready code that includes:
- IBM IAM API key → Bearer token authentication with auto-refresh
- watsonx.data Intelligence (DAI) REST API integration for DQ rule CRUD
- Asynchronous rule execution and result polling
- Column-level data profiling job submission and status polling
- Quality score aggregation across project rules
- IBM COS report archiving using
ibm-cos-sdk - FastAPI service patterns matching IBM building-blocks conventions
IBM Cloud Product Coverage
| IBM Cloud Product | DAI Feature Used |
|---|---|
| watsonx.data Intelligence | REST API: /data_quality/rules, /data_quality/results, /data_quality/profile_jobs |
| IBM Cloud IAM | POST /identity/token (apikey grant) |
| IBM Cloud Object Storage | ibm-cos-sdk put_object for report archiving |
Objective
Transform natural language data quality requirements into deployable services that:
- Authenticate to IBM Cloud via standard IAM API key pattern
- Create, execute, and monitor DQ rules against watsonx.data Intelligence assets
- Surface quality scores and profiling statistics via REST API
- Follow Python 3.12 best practices with Pydantic v2 models
Rules
- Always use
IAMTokenManagerwith 5-minute expiry buffer - Wrap all DAI API calls with
@retry(stop=stop_after_attempt(3), ...)fromtenacity - Use
WXDI_PROJECT_IDfrom environment — never hardcode project IDs - DAI base URL:
https://api.{WXDI_REGION}.dai.cloud.ibm.com— currently us-south is the supported region for DQ rules - All request/response bodies modelled with Pydantic v2
BaseModel - Use
python-dotenv— provide.env.examplewith all IBM Cloud variable names
Scope
- watsonx.data Intelligence data quality rule authoring and execution
- Completeness, uniqueness, validity, consistency, accuracy rule types
- Column-level profiling: null rate, distinct count, min, max, distribution histograms
- Quality score computation and trend analysis
- IBM COS archiving of quality reports
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.
- 10d ago First seen · 134 lines · 84 tokens per session scan A dc88ab20d0ba
data-quality-rules is a skill published in the GitHub repository ibm-self-serve-assets/building-blocks (24 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 1,304 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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