Agent skill

Snowflake Data Quality Sentinel

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows.

MITAuto-check passedDatabases

Install Snowflake Data Quality Sentinel

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill snowflake-data-quality-sentinel -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace snowflake-data-quality-sentinel --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/snowflake-data-quality-sentinel .claude/skills/snowflake-data-quality-sentinel && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
snowflake-data-quality-sentinel
GitHub stars
2.8k
Token cost
~2k tokens
SKILL.md length
629 words
Files
11 (incl. scripts, references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows.

  • Works in 3 steps: Collect evidence → Establish trust and analyze → Interpret the result
  • Monitoring coverage may be incomplete
  • SKILL.md covers Purpose, Prerequisites, Workflow and Decision boundaries, plus 5 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Snowflake Data Quality Sentinel is an agent skill from jeremylongshore/tons-of-skills-marketplace. Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows. Use when evaluations, definitions, schedules, notifications, anomaly state, or monitoring coverage may be incomplete or unhealthy. Trigger with "Snowflake data quality", "DMF expectation", "definition drift", "missing evaluation", or "notification gap".

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts and reference files (for example `eval-spec.yaml`, `references/input-contract.md` and `references/source-notes.md`). Compatibility notes: Model-neutral; requires Python 3.10+. Optional collection requires Snowflake CLI with an existing read-only profile.

It sits in Databases, covering Data warehousing and Data cleaning. It works with Snowflake. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Monitoring coverage may be incomplete
  • With Snowflake data quality
  • DMF expectation
  • Definition drift

Example prompts

  • “Snowflake data quality”
  • “DMF expectation”
  • “definition drift”
  • “/snowflake-data-quality-sentinel”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Model-neutral; requires Python 3.10+. Optional collection requires Snowflake CLI with an existing read-only profile.
  • Pre-approved tools (allowed-tools): Read, Bash(python3:*)

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Collect evidence
  2. Establish trust and analyze
  3. Interpret the result

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash(python3:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 6 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.snowflake.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Model-neutral; requires Python 3.10+. Optional collection requires Snowflake CLI with an existing read-only profile.

    From compatibility in the SKILL.md frontmatter.

Context cost

Snowflake Data Quality Sentinel loads about 2k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 629 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 629 words, ~1,990 tokens.

Download SKILL.mdSave it as .claude/skills/snowflake-data-quality-sentinel/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
snowflake-data-quality-sentinel
description
Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows. Use when evaluations, definitions, schedules, notifications, anomaly state, or monitoring coverage may be incomplete or unhealthy. Trigger with "Snowflake data quality", "DMF expectation", "definition drift", "missing evaluation", or "notification gap".
allowed-tools
Read, Bash(python3:*)
compatibility
Model-neutral; requires Python 3.10+. Optional collection requires Snowflake CLI with an existing read-only profile.
argument-hint
[schema-2-evidence.json]
model
inherit
effort
high
version
3.16.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
saas, snowflake, data-quality, governance, observability, incident-response

Snowflake Data Quality Sentinel

Purpose

Produce separate configuration, history-observation, and history-completeness verdicts from an owner-approved requirement denominator and trusted schema-2 collector receipts. Missing or untrusted evidence never becomes a pass, and this history surface never supports an unqualified present-tense quality pass.

Read the input contract before assembly and consult the source notes when interpreting provider semantics.

Prerequisites

Use Python 3.10+, an owner-approved policy, and Snowflake CLI with an existing least-privilege profile. Never accept credentials or request customer rows, failed-row payloads, metric values, or SQL text.

Workflow

  1. Collect the exact per-object receipt set.
  2. Record independently trusted evidence and owner-policy digests.
  3. Analyze at the policy-bound time and preserve every non-claim.
Step 1: Collect evidence

Collect one bounded history receipt. For every distinct governed object, collect one selector-bound live association receipt and one selector-bound live expectation receipt. Also collect one notification receipt for every distinct governed object whose notification_required is true:

bash
# Example fixed UTC window; replace with the audited interval.
python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality --connection readonly-observer \
  --window-start 2026-09-01T00:00:00Z --window-end 2026-09-02T00:00:00Z \
  --output ./dq-history.json

python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality-associations-current --connection readonly-observer \
  --data-quality-object GOVERNED_DB.GOVERNED_SCHEMA.GOVERNED_TABLE \
  --data-quality-domain TABLE \
  --output ./dq-associations.json

python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality-expectations-current --connection readonly-observer \
  --data-quality-object GOVERNED_DB.GOVERNED_SCHEMA.GOVERNED_TABLE \
  --data-quality-domain TABLE \
  --output ./dq-expectations.json

python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality-notification-current --connection readonly-observer \
  --data-quality-object GOVERNED_DB.GOVERNED_SCHEMA.GOVERNED_TABLE \
  --data-quality-domain TABLE --output ./dq-notification.json

The object selector is used only in reviewed local SQL. The receipt retains its scoped object hash and domain, never the raw selector. Do not escalate roles when a source is permission-blocked.

Step 2: Establish trust and analyze

Maintain the owner-approved policy as a separate policy.json. Assemble exactly schema_version, the byte-equivalent parsed policy, and collector_receipts in the evidence wrapper. Record the evidence and policy digests independently when each crosses its trusted local boundary. The embedded receipt checksums are not trust anchors.

bash
# Record these at their independent trusted local boundaries.
python3 "${CLAUDE_SKILL_DIR}/scripts/analyze_data_quality.py" evidence.json \
  --print-input-sha256
python3 "${CLAUDE_SKILL_DIR}/scripts/analyze_data_quality.py" \
  --policy-file policy.json --print-policy-sha256

python3 "${CLAUDE_SKILL_DIR}/scripts/analyze_data_quality.py" evidence.json \
  --policy-file policy.json \
  --evaluated-at 2026-09-03T12:00:00Z \
  --trusted-input-sha256 sha256:RECORD_FROM_EVIDENCE_BOUNDARY \
  --trusted-policy-sha256 sha256:RECORD_FROM_POLICY_BOUNDARY --pretty

Supplying digests recomputed from an already suspect file does not establish trust. The policy's analysis_as_of_utc must equal --evaluated-at, preventing a trusted old policy from being replayed against a shifted clock.

Step 3: Interpret the result

Preserve configuration_status, history_observation_status, history_completeness_status, every finding code and hashed scope, evidence integrity and coverage, the evaluated denominator, receipt hashes, and fixed non-claims. Do not convert an inconclusive result or satisfied observation into operational approval. If remediation would mutate Snowflake, produce a dry-run proposal and obtain separate authorization.

Show full SKILL.md (315 more words)Show less

Decision boundaries

  • EXPECTATION_VIOLATED=false is SATISFIED_OBSERVATION, never quality PASS. Snowflake publishes no finality SLA for the history surface, so history_completeness_status remains UNPROVEN_NO_PROVIDER_SLA, pass_supported remains false, and settled_through_utc remains null.
  • No matching result is DQ_NO_EVALUATION, not a pass. true is a violation; null is an evaluation failure.
  • Definition, schedule, role, filter, object-domain, grouping, or group-limit drift blocks a healthy verdict.
  • Trigger-on-change freshness and grouped-result completeness remain inconclusive until separately reviewed evidence proves them.
  • Anomaly objectives remain inconclusive until a separate trusted anomaly surface exists. Do not infer anomaly health from association configuration.
  • Notification ENABLED proves configuration only. Delivery always remains NOT_OBSERVED; missing visibility differs from disabled configuration.
  • One association-surface receipt and one expectation-surface receipt must cover every distinct governed object exactly. Multiple expectations may share an association, but the (association_key_sha256, expectation_key_sha256) policy key is unique.
  • Missing surfaces, stale or mixed contexts, caps, truncation, invalid schemas, duplicates, offline receipts, or either trust mismatch suppress classification.

Output

The report includes the three status axes, pass_supported, settled_through_utc, integrity and coverage, denominator counts, deterministic findings, safe provenance, fixed non-claims, and report_sha256. Findings contain fixed text and validated requirement hashes only.

Error Handling

  • Exit 2 is a fixed generic malformed-input error and never reflects rejected input.
  • evidence_integrity_status=INVALID means a trust or receipt check failed; recollect.
  • evidence_complete remains false because history completeness is not provider-proven.
  • Permission-filtered notification evidence is a visibility gap, not proof of disabled configuration.
  • Edition or privilege failures do not authorize automatic role escalation.

Example

False produces SATISFIED_OBSERVATION, while a missing row produces DQ_NO_EVALUATION; neither produces quality PASS. A true row produces VIOLATION_OBSERVED and quality FAIL.

Safety

The analyzer and collector are read-only. A remediation request ends at a dry-run change proposal unless the caller separately authorizes mutation. Findings use validated requirement hashes as scopes and fixed text, so rejected receipt fields or values are never reflected into output.

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references) in skills/.curated/snowflake-data-quality-sentinel of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • eval-spec.yaml
  • references/input-contract.md
  • references/source-notes.md
  • scripts/analyze_data_quality.py
  • scripts/collect_snowflake_evidence.py
  • scripts/sql/data-quality-associations-current.sql
  • scripts/sql/data-quality-expectations-current.sql
  • scripts/sql/data-quality-notification-current.sql
  • scripts/sql/data-quality.sql
  • tests/test_analyze_data_quality.py

Open the folder on GitHubat commit cfae287

Compare with similar skills

Snowflake Data Quality Sentinel next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Snowflake Data Quality Sentinel compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Expensive Snowflake Query FinderAltimateAI/data-engineering-skills128—~662Automated safety check: PassMIT
dbt Snowflake to BigQuery Translatorgoogle/skills21k—~2.7kAutomated safety check: PassApache-2.0
Snowflake Developmentsickn33/agentic-awesome-skills47k2 repos~2.1kAutomated safety check: PassMIT
Snowflake Developmentalirezarezvani/claude-skills28k—~3.2kAutomated safety check: PassMIT

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Works with

Questions about Snowflake Data Quality Sentinel

What does Snowflake Data Quality Sentinel do?

Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows. Snowflake Data Quality Sentinel is an agent skill from jeremylongshore/tons-of-skills-marketplace. Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows.

When should I use Snowflake Data Quality Sentinel?

Snowflake Data Quality Sentinel fits situations like: monitoring coverage may be incomplete; with Snowflake data quality; DMF expectation; definition drift.

How do I install Snowflake Data Quality Sentinel in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill snowflake-data-quality-sentinel -a claude-code`. Or copy the skill folder (skills/.curated/snowflake-data-quality-sentinel in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/snowflake-data-quality-sentinel in your project. Claude Code loads it when a task matches its description.

How do I install Snowflake Data Quality Sentinel in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill snowflake-data-quality-sentinel -a codex`. Or copy the skill folder (skills/.curated/snowflake-data-quality-sentinel in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/snowflake-data-quality-sentinel in your project. Codex loads it when a task matches its description.

Can I use Snowflake Data Quality Sentinel in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill snowflake-data-quality-sentinel -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/snowflake-data-quality-sentinel, .gemini/skills/snowflake-data-quality-sentinel, .github/skills/snowflake-data-quality-sentinel and .opencode/skills/snowflake-data-quality-sentinel in your project.

What does Snowflake Data Quality Sentinel need to run?

Going by SKILL.md and its folder, Snowflake Data Quality Sentinel needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Bash(python3:*). Compatibility (from SKILL.md): Model-neutral; requires Python 3.10+. Optional collection requires Snowflake CLI with an existing read-only profile..

Does Snowflake Data Quality Sentinel access the network?

SKILL.md names 1 domain. As links in the text: docs.snowflake.com. This is read from the text; nothing was executed.

Is Snowflake Data Quality Sentinel safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Snowflake Data Quality Sentinel use?

Snowflake Data Quality Sentinel is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Snowflake Data Quality Sentinel use?

About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.

What are the alternatives to Snowflake Data Quality Sentinel?

Skills that share tags, products or a category with Snowflake Data Quality Sentinel: Neo4j Aura Graph Analytics Skill (neo4j-contrib/neo4j-skills, 114 stars), Expensive Snowflake Query Finder (AltimateAI/data-engineering-skills, 128 stars), dbt Snowflake to BigQuery Translator (google/skills, 21k stars) and Snowflake Development (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Snowflake Data Quality Sentinel?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.