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 j4flmao/agent-skills --skill data-observabilitygit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote 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/j4flmao/agent-skills/data-observability)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-observability"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-observability.svg" alt="Measured on agentmods" 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 | $0.00105 | $0.04605 |
| Opus 5 | $0.00053 | $0.02302 |
| Sonnet 5 | $0.00021 | $0.00921 |
| Haiku 4.5 | $0.00011 | $0.00460 |
Grade A, and why
data-data-observability 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 3d 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 — 502 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Observability
Purpose
Design comprehensive data observability across pipelines: freshness, volume, schema, quality, lineage, and incident management.
Agent Protocol
Trigger
Exact user phrases: "data observability", "data quality monitoring", "data profiling", "data health", "freshness check", "row count anomaly", "schema drift", "data incident", "data lineage", "data monitoring", "observability platform".
Input Context
- Data stack (warehouse, lake, pipelines, BI tools)
- Number of tables/datasets to monitor
- Existing quality checks and monitoring
- Team size and on-call rotation
- SLAs for data freshness and quality
- Incident management workflow
- Monitoring budget and tooling preferences
Output Artifact
Data observability architecture with monitoring checks, alerting rules, and incident response playbook.
Response Format
# Observability checks per dataset
# Freshness, volume, schema, quality
# Alert thresholds
# Incident response workflow
Completion Criteria
- Freshness checks configured for all critical datasets
- Volume monitoring with anomaly detection
- Schema drift detection on source and staging tables
- Quality checks on key columns (nulls, uniqueness, referential integrity)
- Lineage tracking from source to dashboard
- Alerting configured with severity levels
- Incident response runbook written
Workflow
Step 1: Dataset Inventory
Catalog all datasets by criticality and ownership. Tier 1: executive dashboards, financial reports, customer-facing data, regulatory data. Tier 2: operational reports, team-level analytics, internal tools. Tier 3: experimental, exploratory, ad-hoc queries.
Inventory Schema
For each dataset: name, owner, tier, source system, freshness SLA, location (table/view/API), upstream dependencies, downstream consumers, expected row count range, quality rules.
Step 2: Freshness Monitoring
Freshness Checks
Check data arrival within expected SLA window. Monitor: last_updated timestamp vs expected schedule. For batch: compare DAG completion time to SLA time. For streaming: compare latest event timestamp to current time.
What ships with it
8 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.
- references/anomaly-detection-observability.md 3.5 KB
- references/monitor-config-examples.md 5.5 KB
- references/observability-cost-monitoring.md 4.3 KB
- references/observability-dashboards.md 258 B
- references/observability-dimensions.md 4.2 KB
- references/observability-incident-response.md 4.0 KB
- references/observability-setup.md 3.9 KB
- references/slo-framework.md 4.7 KB
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.
- 3d ago First seen · 502 lines · 105 tokens per session scan A 16ee2469cd4d
data-data-observability is a skill published in the GitHub repository j4flmao/agent-skills (21 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 4,605 once invoked, about $0.0005 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.
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